diff --git a/.DS_Store b/.DS_Store
index 632e169..4c1ecd0 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.ipynb_checkpoints/Dataset1-checkpoint.ipynb b/.ipynb_checkpoints/Dataset1-checkpoint.ipynb
index 5484c98..5832b8b 100644
--- a/.ipynb_checkpoints/Dataset1-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Dataset1-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,33 +362,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE Training for fold 0 = 0.10400571565013786\n",
-      "RMSE Testing for fold 0 = 0.0997875718081956\n",
-      "RMSE Training for fold 1 = 0.10370147977329444\n",
-      "RMSE Testing for fold 1 = 0.10258680387540348\n",
-      "RMSE Training for fold 2 = 0.10403667023213119\n",
-      "RMSE Testing for fold 2 = 0.09948249179500962\n",
-      "RMSE Training for fold 3 = 0.10399692337273836\n",
-      "RMSE Testing for fold 3 = 0.09987071247691796\n",
-      "RMSE Training for fold 4 = 0.1023387014504197\n",
-      "RMSE Testing for fold 4 = 0.11425633876928286\n",
-      "RMSE Training for fold 5 = 0.10346837217623998\n",
-      "RMSE Testing for fold 5 = 0.10468309066373536\n",
-      "RMSE Training for fold 6 = 0.1041261226688795\n",
-      "RMSE Testing for fold 6 = 0.09864477362514056\n",
-      "RMSE Training for fold 7 = 0.10379373695227591\n",
-      "RMSE Testing for fold 7 = 0.10174577472738733\n",
-      "RMSE Training for fold 8 = 0.10307111007107508\n",
-      "RMSE Testing for fold 8 = 0.10815642854126484\n",
-      "RMSE Training for fold 9 = 0.10333463066100897\n",
-      "RMSE Testing for fold 9 = 0.10587579634157593\n"
+      "RMSE Training for fold 0 = 0.10303539344689733\n",
+      "RMSE Testing for fold 0 = 0.10845567097417186\n",
+      "RMSE Training for fold 1 = 0.10419089612177095\n",
+      "RMSE Testing for fold 1 = 0.09802045254832206\n",
+      "RMSE Training for fold 2 = 0.10349185745093904\n",
+      "RMSE Testing for fold 2 = 0.10447346688262305\n",
+      "RMSE Training for fold 3 = 0.10344469887189557\n",
+      "RMSE Testing for fold 3 = 0.10490538873653102\n",
+      "RMSE Training for fold 4 = 0.1025899769379141\n",
+      "RMSE Testing for fold 4 = 0.11222617684153978\n",
+      "RMSE Training for fold 5 = 0.1030302651199911\n",
+      "RMSE Testing for fold 5 = 0.10850493306300102\n",
+      "RMSE Training for fold 6 = 0.10450952572006245\n",
+      "RMSE Testing for fold 6 = 0.09492755358007658\n",
+      "RMSE Training for fold 7 = 0.10398159298299949\n",
+      "RMSE Testing for fold 7 = 0.10000759755069878\n",
+      "RMSE Training for fold 8 = 0.1036367084864887\n",
+      "RMSE Testing for fold 8 = 0.10317625379266929\n",
+      "RMSE Training for fold 9 = 0.10396055800882181\n",
+      "RMSE Testing for fold 9 = 0.10019529711302326\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/base.py:485: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
+      "  linalg.lstsq(X, y)\n"
      ]
     }
    ],
@@ -411,15 +419,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average RMSE Training = 0.10358734630082009\n",
-      "Average RMSE Testing = 0.10350897826239136\n"
+      "Average RMSE Training = 0.10358714731477806\n",
+      "Average RMSE Testing = 0.10348927910826566\n"
      ]
     }
    ],
@@ -437,22 +445,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x11b442c88>"
+       "<matplotlib.legend.Legend at 0x11430d3c8>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -464,7 +472,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -514,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -536,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -574,14 +582,14 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 0 = 0.06126071297842859\n",
-      "RMSE Testing for fold 0 = 0.05798243518318052\n",
-      "Out of Bag error for fold 0 = 0.34422750167498295\n",
+      "RMSE Training for fold 0 = 0.060342718323088906\n",
+      "RMSE Testing for fold 0 = 0.06600103122494523\n",
+      "Out of Bag error for fold 0 = 0.3422191688756486\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 1 = 0.06038490279774731\n",
-      "RMSE Testing for fold 1 = 0.06486151881898947\n",
-      "Out of Bag error for fold 1 = 0.3378527237237616\n",
+      "RMSE Training for fold 1 = 0.05999354404077284\n",
+      "RMSE Testing for fold 1 = 0.05318740413550893\n",
+      "Out of Bag error for fold 1 = 0.334175430446894\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -600,14 +608,14 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 2 = 0.06112971065222718\n",
-      "RMSE Testing for fold 2 = 0.05714984869304187\n",
-      "Out of Bag error for fold 2 = 0.34150480422816987\n",
+      "RMSE Training for fold 2 = 0.06070297993348546\n",
+      "RMSE Testing for fold 2 = 0.06200857336669701\n",
+      "Out of Bag error for fold 2 = 0.3418665442449461\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 3 = 0.06039092317950195\n",
-      "RMSE Testing for fold 3 = 0.05797000039547089\n",
-      "Out of Bag error for fold 3 = 0.336287156937473\n",
+      "RMSE Training for fold 3 = 0.060227733092934436\n",
+      "RMSE Testing for fold 3 = 0.06623514605665273\n",
+      "Out of Bag error for fold 3 = 0.3369150802713663\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -626,14 +634,14 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 4 = 0.06022803943783143\n",
-      "RMSE Testing for fold 4 = 0.06581867857608557\n",
-      "Out of Bag error for fold 4 = 0.34362030845707925\n",
+      "RMSE Training for fold 4 = 0.05982159473119974\n",
+      "RMSE Testing for fold 4 = 0.0622506437717224\n",
+      "Out of Bag error for fold 4 = 0.3403855824932449\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 5 = 0.06017914080444452\n",
-      "RMSE Testing for fold 5 = 0.06096465116727237\n",
-      "Out of Bag error for fold 5 = 0.3364687194970032\n",
+      "RMSE Training for fold 5 = 0.06030482955967637\n",
+      "RMSE Testing for fold 5 = 0.06633429241801071\n",
+      "Out of Bag error for fold 5 = 0.34033955939962013\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -652,24 +660,24 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 6 = 0.060143998555742206\n",
-      "RMSE Testing for fold 6 = 0.052439791284759806\n",
-      "Out of Bag error for fold 6 = 0.33367885942183073\n",
+      "RMSE Training for fold 6 = 0.06066202054205524\n",
+      "RMSE Testing for fold 6 = 0.053184772935539554\n",
+      "Out of Bag error for fold 6 = 0.33606049192430976\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 7 = 0.060881586887986665\n",
-      "RMSE Testing for fold 7 = 0.0599276734215616\n",
-      "Out of Bag error for fold 7 = 0.34238807095937507\n",
+      "RMSE Training for fold 7 = 0.060589565741104454\n",
+      "RMSE Testing for fold 7 = 0.05662312472349231\n",
+      "Out of Bag error for fold 7 = 0.3390706702553714\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 8 = 0.06041761265041053\n",
-      "RMSE Testing for fold 8 = 0.06449080352793264\n",
-      "Out of Bag error for fold 8 = 0.34241013974135526\n",
+      "RMSE Training for fold 8 = 0.06084161060273395\n",
+      "RMSE Testing for fold 8 = 0.06178033569929699\n",
+      "Out of Bag error for fold 8 = 0.3417962938503758\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 9 = 0.06007263987303771\n",
-      "RMSE Testing for fold 9 = 0.06408537393773711\n",
-      "Out of Bag error for fold 9 = 0.33692796236060274\n",
+      "RMSE Training for fold 9 = 0.06141660589675676\n",
+      "RMSE Testing for fold 9 = 0.05618073772151881\n",
+      "Out of Bag error for fold 9 = 0.3461750723884218\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -677,8 +685,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
@@ -708,16 +714,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average RMSE Training = 0.060508926781735806\n",
-      "Average RMSE Testing = 0.06056907750060318\n",
-      "Average Out of Bag Error = 0.33953662470016344\n"
+      "Average RMSE Training = 0.06049032024638081\n",
+      "Average RMSE Testing = 0.06037860620533847\n",
+      "Average Out of Bag Error = 0.3399003894150199\n"
      ]
     }
    ],
@@ -736,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -802,8 +808,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -814,23 +818,23 @@
       "-----------------------------------------------------\n",
       "Number of trees = 1\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.08600269522770379\n",
-      "RMSE Testing = 0.08595696859673009\n",
-      "Out of Bag error = 1.1050466340290819\n",
+      "RMSE Training = 0.0823494804595731\n",
+      "RMSE Testing = 0.08283825378858026\n",
+      "Out of Bag error = 1.0798942445035107\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 2\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.08131789964855246\n",
-      "RMSE Testing = 0.081823250044802\n",
-      "Out of Bag error = 0.9309084353663628\n",
+      "RMSE Training = 0.08246242451041672\n",
+      "RMSE Testing = 0.08236891188157434\n",
+      "Out of Bag error = 0.9424330057816268\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 3\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07829344871000513\n",
-      "RMSE Testing = 0.0786995157685069\n",
-      "Out of Bag error = 0.81172920593767\n",
+      "RMSE Training = 0.07700968909257182\n",
+      "RMSE Testing = 0.07729087270993668\n",
+      "Out of Bag error = 0.8086883660957028\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -877,16 +881,16 @@
       "-----------------------------------------------------\n",
       "Number of trees = 4\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07582317293627978\n",
-      "RMSE Testing = 0.07573334498888282\n",
-      "Out of Bag error = 0.7328192851555144\n",
+      "RMSE Training = 0.07721935004206128\n",
+      "RMSE Testing = 0.07707896370361683\n",
+      "Out of Bag error = 0.7332061780309468\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 5\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07824559250021182\n",
-      "RMSE Testing = 0.07836107763487572\n",
-      "Out of Bag error = 0.7009958596982658\n",
+      "RMSE Training = 0.07863996660341205\n",
+      "RMSE Testing = 0.07854574300100362\n",
+      "Out of Bag error = 0.7091519144356438\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -935,8 +939,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -947,9 +949,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 6\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07640061404938889\n",
-      "RMSE Testing = 0.07632529246491367\n",
-      "Out of Bag error = 0.6462296226395466\n",
+      "RMSE Training = 0.0769363463805625\n",
+      "RMSE Testing = 0.07721853154745752\n",
+      "Out of Bag error = 0.652577364919106\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -984,9 +986,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 7\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07460925253711437\n",
-      "RMSE Testing = 0.07448477393934767\n",
-      "Out of Bag error = 0.6026901888399395\n",
+      "RMSE Training = 0.07817899517714692\n",
+      "RMSE Testing = 0.07830310363208996\n",
+      "Out of Bag error = 0.6411896230990621\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -994,6 +996,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1019,9 +1035,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 8\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07944487097181759\n",
-      "RMSE Testing = 0.07942807162849061\n",
-      "Out of Bag error = 0.6490891495948425\n",
+      "RMSE Training = 0.07638903068808353\n",
+      "RMSE Testing = 0.07636637230657327\n",
+      "Out of Bag error = 0.6078084706214314\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1029,20 +1045,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1068,9 +1070,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 9\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07597551510562257\n",
-      "RMSE Testing = 0.07652799581282663\n",
-      "Out of Bag error = 0.5885329341718089\n",
+      "RMSE Training = 0.0780297522723633\n",
+      "RMSE Testing = 0.07796337708701274\n",
+      "Out of Bag error = 0.6109831026909391\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1089,6 +1091,8 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1099,9 +1103,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 10\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07546305242371641\n",
-      "RMSE Testing = 0.07569678993183042\n",
-      "Out of Bag error = 0.5680485618581963\n",
+      "RMSE Training = 0.07801569170501642\n",
+      "RMSE Testing = 0.07816308286579876\n",
+      "Out of Bag error = 0.6053342525686438\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1142,9 +1146,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 11\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07566112894341903\n",
-      "RMSE Testing = 0.07583294135415959\n",
-      "Out of Bag error = 0.566820740869799\n",
+      "RMSE Training = 0.07488855239588874\n",
+      "RMSE Testing = 0.0750597476921647\n",
+      "Out of Bag error = 0.5603137511586556\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1173,6 +1177,8 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1183,9 +1189,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 12\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07715429465337018\n",
-      "RMSE Testing = 0.07743244318892788\n",
-      "Out of Bag error = 0.5785813405087349\n",
+      "RMSE Training = 0.07624367889486336\n",
+      "RMSE Testing = 0.07613668278127306\n",
+      "Out of Bag error = 0.5680860332335186\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1204,14 +1210,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1222,9 +1220,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 13\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07720024830430336\n",
-      "RMSE Testing = 0.07733590416674095\n",
-      "Out of Bag error = 0.5839544705074777\n",
+      "RMSE Training = 0.0750797633541285\n",
+      "RMSE Testing = 0.07519365927505199\n",
+      "Out of Bag error = 0.551779443641419\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1251,6 +1249,10 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1261,9 +1263,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 14\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07619605240453789\n",
-      "RMSE Testing = 0.0764184178141841\n",
-      "Out of Bag error = 0.564030868798622\n",
+      "RMSE Training = 0.07536183743217255\n",
+      "RMSE Testing = 0.07535394691109602\n",
+      "Out of Bag error = 0.5549605720167998\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1300,9 +1302,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 15\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07670317013050573\n",
-      "RMSE Testing = 0.07677319463516982\n",
-      "Out of Bag error = 0.5679379989471646\n",
+      "RMSE Training = 0.07743813429749472\n",
+      "RMSE Testing = 0.07722647518373568\n",
+      "Out of Bag error = 0.580024430246438\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1325,6 +1327,8 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1335,9 +1339,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 16\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0752049922106906\n",
-      "RMSE Testing = 0.07512678835322723\n",
-      "Out of Bag error = 0.5441951680201874\n",
+      "RMSE Training = 0.07618056512651251\n",
+      "RMSE Testing = 0.07630961188411545\n",
+      "Out of Bag error = 0.5583260317490131\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1360,6 +1364,14 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1370,9 +1382,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 17\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07674241632782196\n",
-      "RMSE Testing = 0.07693811162963506\n",
-      "Out of Bag error = 0.5646337147274499\n",
+      "RMSE Training = 0.07750537942593308\n",
+      "RMSE Testing = 0.07758514483885923\n",
+      "Out of Bag error = 0.5766389838922714\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1395,14 +1407,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1413,9 +1417,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 18\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07763879289249812\n",
-      "RMSE Testing = 0.07764892157557338\n",
-      "Out of Bag error = 0.5764361439446976\n",
+      "RMSE Training = 0.078374541301954\n",
+      "RMSE Testing = 0.07843413042284367\n",
+      "Out of Bag error = 0.5881840900894801\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1438,6 +1442,10 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1448,9 +1456,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 19\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.076519542272754\n",
-      "RMSE Testing = 0.07645544522728247\n",
-      "Out of Bag error = 0.559888257167874\n",
+      "RMSE Training = 0.07612144226345642\n",
+      "RMSE Testing = 0.0760127377219748\n",
+      "Out of Bag error = 0.5558869008133287\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1458,12 +1466,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1481,9 +1483,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 20\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0771206577092081\n",
-      "RMSE Testing = 0.07711920066633249\n",
-      "Out of Bag error = 0.5684832564441655\n",
+      "RMSE Training = 0.07562922962849987\n",
+      "RMSE Testing = 0.07548020647252797\n",
+      "Out of Bag error = 0.5462728530029385\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1502,10 +1504,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1516,9 +1514,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 21\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07603220563891631\n",
-      "RMSE Testing = 0.07607224764076086\n",
-      "Out of Bag error = 0.551797214060229\n",
+      "RMSE Training = 0.07631871945238278\n",
+      "RMSE Testing = 0.07633118384670001\n",
+      "Out of Bag error = 0.5527747369459928\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1526,6 +1524,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
@@ -1537,9 +1543,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 22\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07502509151557815\n",
-      "RMSE Testing = 0.07498503632360716\n",
-      "Out of Bag error = 0.5363651863435039\n",
+      "RMSE Training = 0.07748464330222933\n",
+      "RMSE Testing = 0.07758514654811037\n",
+      "Out of Bag error = 0.5696295020902284\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1547,6 +1553,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1560,9 +1568,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 23\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07562849460346883\n",
-      "RMSE Testing = 0.07576027330252429\n",
-      "Out of Bag error = 0.5454331102816521\n",
+      "RMSE Training = 0.07658261461225616\n",
+      "RMSE Testing = 0.07660933485830759\n",
+      "Out of Bag error = 0.5557432169830344\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1570,6 +1578,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1583,9 +1593,16 @@
       "-----------------------------------------------------\n",
       "Number of trees = 24\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07672960063549569\n",
-      "RMSE Testing = 0.07669246373780436\n",
-      "Out of Bag error = 0.558586290981216\n",
+      "RMSE Training = 0.07650060621082821\n",
+      "RMSE Testing = 0.07666614202120393\n",
+      "Out of Bag error = 0.5542164496720027\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 1\n",
+      "RMSE Training = 0.07548103514942568\n",
+      "RMSE Testing = 0.07560117094875803\n",
+      "Out of Bag error = 0.5415250778391896\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1593,6 +1610,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
@@ -1601,19 +1624,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-----------------------------------------------------\n",
-      "Number of trees = 25\n",
-      "Number of features = 1\n",
-      "RMSE Training = 0.0754951248960899\n",
-      "RMSE Testing = 0.07559725450022196\n",
-      "Out of Bag error = 0.543916016883893\n",
-      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 26\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07666511517549227\n",
-      "RMSE Testing = 0.07673950967716087\n",
-      "Out of Bag error = 0.5576725589079038\n",
+      "RMSE Training = 0.07632516787758371\n",
+      "RMSE Testing = 0.0763062921015964\n",
+      "Out of Bag error = 0.5513046957639924\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1632,9 +1648,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 27\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07584134859550855\n",
-      "RMSE Testing = 0.07565105617082121\n",
-      "Out of Bag error = 0.5459957269304607\n",
+      "RMSE Training = 0.07544981335578578\n",
+      "RMSE Testing = 0.07554968401705917\n",
+      "Out of Bag error = 0.5368631671022817\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1653,1263 +1669,9965 @@
       "-----------------------------------------------------\n",
       "Number of trees = 28\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577555460251728\n",
-      "RMSE Testing = 0.07585899333572792\n",
-      "Out of Bag error = 0.5417646544943107\n",
+      "RMSE Training = 0.07525868403669272\n",
+      "RMSE Testing = 0.07524707605464637\n",
+      "Out of Bag error = 0.5358115785056431\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 29\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07581628675894876\n",
-      "RMSE Testing = 0.07588488729464553\n",
-      "Out of Bag error = 0.5442131311587797\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07665226834823699\n",
+      "RMSE Testing = 0.07675569718446554\n",
+      "Out of Bag error = 0.5546467958625863\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 30\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585628500256583\n",
-      "RMSE Testing = 0.07580125192402978\n",
-      "Out of Bag error = 0.5433968900015672\n",
+      "RMSE Training = 0.0761936567956908\n",
+      "RMSE Testing = 0.07611522902481274\n",
+      "Out of Bag error = 0.5495200058810872\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 31\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07505828310895313\n",
-      "RMSE Testing = 0.07515802950745977\n",
-      "Out of Bag error = 0.533308215969996\n",
+      "RMSE Training = 0.07651646260339953\n",
+      "RMSE Testing = 0.0766205511856137\n",
+      "Out of Bag error = 0.5524115904208368\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 32\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07499179151234613\n",
-      "RMSE Testing = 0.07509033705125467\n",
-      "Out of Bag error = 0.5323148868935752\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07584099063661298\n",
+      "RMSE Testing = 0.07580396577837398\n",
+      "Out of Bag error = 0.5414660381996333\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 33\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545091611345603\n",
-      "RMSE Testing = 0.07554299120785445\n",
-      "Out of Bag error = 0.537071634240084\n",
+      "RMSE Training = 0.07540698077485537\n",
+      "RMSE Testing = 0.07530173457795059\n",
+      "Out of Bag error = 0.5360907623890065\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 34\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07605370712659223\n",
-      "RMSE Testing = 0.07608082747656465\n",
-      "Out of Bag error = 0.5455795620097355\n",
+      "RMSE Training = 0.07581859200854627\n",
+      "RMSE Testing = 0.07591589243408299\n",
+      "Out of Bag error = 0.5407673686894209\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 35\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760790655576517\n",
-      "RMSE Testing = 0.0759896396798175\n",
-      "Out of Bag error = 0.5450167951004771\n",
+      "RMSE Training = 0.07529211184476055\n",
+      "RMSE Testing = 0.07526453446374134\n",
+      "Out of Bag error = 0.5328883064921333\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 36\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0763832149087087\n",
-      "RMSE Testing = 0.07657098783118405\n",
-      "Out of Bag error = 0.5470974498917556\n",
+      "RMSE Training = 0.07565883099995116\n",
+      "RMSE Testing = 0.07575255029759534\n",
+      "Out of Bag error = 0.5403240107848742\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 37\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585103393024265\n",
-      "RMSE Testing = 0.07597207242482736\n",
-      "Out of Bag error = 0.5408806502816212\n",
+      "RMSE Training = 0.07527823779501895\n",
+      "RMSE Testing = 0.0753704080364046\n",
+      "Out of Bag error = 0.5338463601038417\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 38\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07704133322450611\n",
-      "RMSE Testing = 0.07702469758149547\n",
-      "Out of Bag error = 0.558480493776463\n",
+      "RMSE Training = 0.07660899173333309\n",
+      "RMSE Testing = 0.0766329463278887\n",
+      "Out of Bag error = 0.5506570066586556\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 39\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07510800172778873\n",
-      "RMSE Testing = 0.07502000037436724\n",
-      "Out of Bag error = 0.5318724771491956\n",
+      "RMSE Training = 0.07502320533018259\n",
+      "RMSE Testing = 0.07499159734364955\n",
+      "Out of Bag error = 0.5286396564287894\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 40\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07482024911042545\n",
-      "RMSE Testing = 0.07505716705411222\n",
-      "Out of Bag error = 0.5263220655379305\n",
+      "RMSE Training = 0.07612961030986078\n",
+      "RMSE Testing = 0.07618291184907856\n",
+      "Out of Bag error = 0.5456000166522562\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 41\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07565229248383143\n",
-      "RMSE Testing = 0.07573382840549017\n",
-      "Out of Bag error = 0.5378017295522068\n",
+      "RMSE Training = 0.0765427627011733\n",
+      "RMSE Testing = 0.07659227616556834\n",
+      "Out of Bag error = 0.5512650999510613\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 42\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0756441779277265\n",
-      "RMSE Testing = 0.07564938816812526\n",
-      "Out of Bag error = 0.5387521446721655\n",
+      "RMSE Training = 0.07686109693591195\n",
+      "RMSE Testing = 0.07686636426964712\n",
+      "Out of Bag error = 0.5527309216599725\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 43\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07620754965113714\n",
-      "RMSE Testing = 0.07626757070947734\n",
-      "Out of Bag error = 0.5438703016358162\n",
+      "RMSE Training = 0.07652871189684493\n",
+      "RMSE Testing = 0.07649088488461826\n",
+      "Out of Bag error = 0.5493112087166441\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 44\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07621060932235915\n",
-      "RMSE Testing = 0.07631684343117422\n",
-      "Out of Bag error = 0.5434817455649771\n",
+      "RMSE Training = 0.07522467025546517\n",
+      "RMSE Testing = 0.07531022855947056\n",
+      "Out of Bag error = 0.52964624512371\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 45\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07520859511276996\n",
-      "RMSE Testing = 0.07530957205660843\n",
-      "Out of Bag error = 0.530727107287938\n",
+      "RMSE Training = 0.07612949007922372\n",
+      "RMSE Testing = 0.0759937047279512\n",
+      "Out of Bag error = 0.5444717752346881\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 46\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07591157075683377\n",
-      "RMSE Testing = 0.0759848743212733\n",
-      "Out of Bag error = 0.5402067865896735\n",
+      "RMSE Training = 0.07541565981639817\n",
+      "RMSE Testing = 0.07544438582003574\n",
+      "Out of Bag error = 0.5344008492972344\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 47\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07593536643772794\n",
-      "RMSE Testing = 0.0760731318453113\n",
-      "Out of Bag error = 0.5412497128561352\n",
+      "RMSE Training = 0.07619494517118065\n",
+      "RMSE Testing = 0.07630436807038969\n",
+      "Out of Bag error = 0.5443996309755301\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 48\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07445242759490078\n",
-      "RMSE Testing = 0.07437136246367622\n",
-      "Out of Bag error = 0.5204037218252701\n",
+      "RMSE Training = 0.07598770148342765\n",
+      "RMSE Testing = 0.07599742736215458\n",
+      "Out of Bag error = 0.5397128598198202\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 49\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07483012274719172\n",
-      "RMSE Testing = 0.07491465049757567\n",
-      "Out of Bag error = 0.5263393665006246\n",
+      "RMSE Training = 0.07551043966089613\n",
+      "RMSE Testing = 0.07564476353069619\n",
+      "Out of Bag error = 0.534088721401002\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 50\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07559214390492866\n",
-      "RMSE Testing = 0.07579410332482357\n",
-      "Out of Bag error = 0.5339587059739423\n",
+      "RMSE Training = 0.07559806415817905\n",
+      "RMSE Testing = 0.07556214130066383\n",
+      "Out of Bag error = 0.5366649623076131\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 51\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07582173920541133\n",
-      "RMSE Testing = 0.0758020848564908\n",
-      "Out of Bag error = 0.5376980190046557\n",
+      "RMSE Training = 0.07602184170838766\n",
+      "RMSE Testing = 0.07610694112909996\n",
+      "Out of Bag error = 0.5423616955376533\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 52\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07582585144192569\n",
-      "RMSE Testing = 0.07592949197508415\n",
-      "Out of Bag error = 0.5379845197585155\n",
+      "RMSE Training = 0.07568523136832765\n",
+      "RMSE Testing = 0.07569733219655689\n",
+      "Out of Bag error = 0.5376761856536418\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 53\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07579287176516017\n",
-      "RMSE Testing = 0.0758469986232887\n",
-      "Out of Bag error = 0.5387744730070622\n",
+      "RMSE Training = 0.07507825151669564\n",
+      "RMSE Testing = 0.0750519193872969\n",
+      "Out of Bag error = 0.5282498456722899\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 54\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577454216132688\n",
-      "RMSE Testing = 0.0758274548064323\n",
-      "Out of Bag error = 0.5369651141761546\n",
+      "RMSE Training = 0.07591437564008781\n",
+      "RMSE Testing = 0.07598371044142609\n",
+      "Out of Bag error = 0.5386091949291956\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 55\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07550097442010185\n",
-      "RMSE Testing = 0.07556900091663282\n",
-      "Out of Bag error = 0.5338330774200802\n",
+      "RMSE Training = 0.07613186576115889\n",
+      "RMSE Testing = 0.07612920180967472\n",
+      "Out of Bag error = 0.5432903153178866\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 56\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07638641210772812\n",
-      "RMSE Testing = 0.07646687465795936\n",
-      "Out of Bag error = 0.5461636438519452\n",
+      "RMSE Training = 0.07495153932224219\n",
+      "RMSE Testing = 0.07493114861778331\n",
+      "Out of Bag error = 0.5266503085857464\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 57\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07548364240275404\n",
-      "RMSE Testing = 0.07564412108878019\n",
-      "Out of Bag error = 0.534924705506598\n",
+      "RMSE Training = 0.07571481064790261\n",
+      "RMSE Testing = 0.07574777515631527\n",
+      "Out of Bag error = 0.5358272984801802\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 58\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754021688996431\n",
-      "RMSE Testing = 0.07546614426795636\n",
-      "Out of Bag error = 0.5321386378452462\n",
+      "RMSE Training = 0.07586757005448583\n",
+      "RMSE Testing = 0.07573512162316556\n",
+      "Out of Bag error = 0.5378214473792834\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 59\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07493517606964108\n",
-      "RMSE Testing = 0.07508249991053349\n",
-      "Out of Bag error = 0.5255926102510913\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07509053347002595\n",
+      "RMSE Testing = 0.07511705945122882\n",
+      "Out of Bag error = 0.5274328506173831\n",
+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 60\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585180175424878\n",
-      "RMSE Testing = 0.07586193526946577\n",
-      "Out of Bag error = 0.5380584797822021\n",
+      "RMSE Training = 0.07626584353720604\n",
+      "RMSE Testing = 0.07618656118212139\n",
+      "Out of Bag error = 0.5444086612856719\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 61\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0761469963529337\n",
-      "RMSE Testing = 0.07624659446725604\n",
-      "Out of Bag error = 0.5428789674695258\n",
+      "RMSE Training = 0.07476174069482866\n",
+      "RMSE Testing = 0.07475043297143982\n",
+      "Out of Bag error = 0.5237620544789783\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 62\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07596075152851363\n",
-      "RMSE Testing = 0.07605029886666986\n",
-      "Out of Bag error = 0.5396722245928266\n",
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+      "RMSE Testing = 0.07537214558299951\n",
+      "Out of Bag error = 0.5297687064746592\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 63\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07550718442783613\n",
-      "RMSE Testing = 0.07569368198586024\n",
-      "Out of Bag error = 0.5333404010023426\n",
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+      "RMSE Testing = 0.07556394874645363\n",
+      "Out of Bag error = 0.5330323429512324\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 64\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07558012580943793\n",
-      "RMSE Testing = 0.07558895944279784\n",
-      "Out of Bag error = 0.5337559307676754\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07594561665567989\n",
+      "RMSE Testing = 0.07610458108584198\n",
+      "Out of Bag error = 0.5398380725753009\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 65\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07513975503793238\n",
-      "RMSE Testing = 0.07515768203362276\n",
-      "Out of Bag error = 0.527923325941377\n",
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+      "RMSE Testing = 0.07561554860514196\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 66\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07548465874491544\n",
-      "RMSE Testing = 0.07553902108194152\n",
-      "Out of Bag error = 0.5318290099242506\n",
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+      "RMSE Testing = 0.07663150052446871\n",
+      "Out of Bag error = 0.5453992162027341\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 67\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07660241017332556\n",
-      "RMSE Testing = 0.07681983340554882\n",
-      "Out of Bag error = 0.5489015354390332\n",
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+      "RMSE Testing = 0.07603155367928206\n",
+      "Out of Bag error = 0.5386378825154263\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 68\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07619329397476624\n",
-      "RMSE Testing = 0.07633242948035629\n",
-      "Out of Bag error = 0.5428021190337917\n",
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+      "RMSE Testing = 0.0750154782682817\n",
+      "Out of Bag error = 0.5251934611185058\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 69\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07495083729726022\n",
-      "RMSE Testing = 0.075039700295273\n",
-      "Out of Bag error = 0.5256102845049964\n",
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+      "RMSE Testing = 0.07582509958583543\n",
+      "Out of Bag error = 0.5356233358507221\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 70\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545344805916489\n",
-      "RMSE Testing = 0.07538871723800124\n",
-      "Out of Bag error = 0.5319014932900549\n",
+      "RMSE Training = 0.0760922161246296\n",
+      "RMSE Testing = 0.07601276264360977\n",
+      "Out of Bag error = 0.541635280963265\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 71\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545028971186409\n",
-      "RMSE Testing = 0.0754579402042265\n",
-      "Out of Bag error = 0.5310897123411785\n",
+      "RMSE Training = 0.07517674616201744\n",
+      "RMSE Testing = 0.07524506845879798\n",
+      "Out of Bag error = 0.5272803345252208\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 72\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07448277032768796\n",
-      "RMSE Testing = 0.0745470566904322\n",
-      "Out of Bag error = 0.5197015194053779\n",
+      "RMSE Training = 0.07592215167868985\n",
+      "RMSE Testing = 0.07587861287875756\n",
+      "Out of Bag error = 0.5389963460126845\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 73\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07593203408003817\n",
-      "RMSE Testing = 0.07588500314149613\n",
-      "Out of Bag error = 0.5382300216062184\n",
+      "RMSE Training = 0.07597011142076651\n",
+      "RMSE Testing = 0.07594384750535285\n",
+      "Out of Bag error = 0.5384606660221232\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 74\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07578528770090459\n",
-      "RMSE Testing = 0.0758249179247527\n",
-      "Out of Bag error = 0.5363556410316042\n",
+      "RMSE Training = 0.07594940728321684\n",
+      "RMSE Testing = 0.07598255774959825\n",
+      "Out of Bag error = 0.5388670833079569\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 75\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07592941909059345\n",
-      "RMSE Testing = 0.07596584308494124\n",
-      "Out of Bag error = 0.5384862868978324\n",
+      "RMSE Training = 0.07541528922865218\n",
+      "RMSE Testing = 0.07540898008214722\n",
+      "Out of Bag error = 0.5316135644425752\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 76\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07526213540930335\n",
-      "RMSE Testing = 0.07523206053062247\n",
-      "Out of Bag error = 0.5290322183952324\n",
+      "RMSE Training = 0.07612664700804994\n",
+      "RMSE Testing = 0.07617729730126803\n",
+      "Out of Bag error = 0.5414460540061334\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 77\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07581902287505174\n",
-      "RMSE Testing = 0.07604925850444547\n",
-      "Out of Bag error = 0.5360983647116006\n",
+      "RMSE Training = 0.075100925098853\n",
+      "RMSE Testing = 0.07512724675031691\n",
+      "Out of Bag error = 0.5267425765905533\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 78\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07611262771855602\n",
-      "RMSE Testing = 0.07613596105448575\n",
-      "Out of Bag error = 0.5396876069448893\n",
+      "RMSE Training = 0.07553612695069506\n",
+      "RMSE Testing = 0.07557936836836371\n",
+      "Out of Bag error = 0.5325599588846209\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 79\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07530153417800604\n",
-      "RMSE Testing = 0.07545695904518662\n",
-      "Out of Bag error = 0.5311659372096275\n",
+      "RMSE Training = 0.07588207950525587\n",
+      "RMSE Testing = 0.0758868502349137\n",
+      "Out of Bag error = 0.5364755824404401\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 80\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760776163909794\n",
-      "RMSE Testing = 0.07593511740631925\n",
-      "Out of Bag error = 0.5404675137156849\n",
+      "RMSE Training = 0.07512372169600745\n",
+      "RMSE Testing = 0.07510751252222134\n",
+      "Out of Bag error = 0.5279026252040822\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 81\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075944695053495\n",
-      "RMSE Testing = 0.07618793716438117\n",
-      "Out of Bag error = 0.5394629415430467\n",
+      "RMSE Training = 0.07542207764271931\n",
+      "RMSE Testing = 0.07545375435421088\n",
+      "Out of Bag error = 0.53104337230695\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 82\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07546024443622673\n",
-      "RMSE Testing = 0.07550165088632846\n",
-      "Out of Bag error = 0.5309283741350678\n",
+      "RMSE Training = 0.07561189147374342\n",
+      "RMSE Testing = 0.07567440589969913\n",
+      "Out of Bag error = 0.5330229181493398\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 83\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07543336177360657\n",
-      "RMSE Testing = 0.07542615061990927\n",
-      "Out of Bag error = 0.5311846308298648\n",
+      "RMSE Training = 0.07598024790862987\n",
+      "RMSE Testing = 0.07602296151643718\n",
+      "Out of Bag error = 0.5385178952727574\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 84\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755677784392363\n",
-      "RMSE Testing = 0.07563728011260054\n",
-      "Out of Bag error = 0.5332201345469912\n",
+      "RMSE Training = 0.07525072695195001\n",
+      "RMSE Testing = 0.07524072121583276\n",
+      "Out of Bag error = 0.5290353013651472\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 85\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07526698325253305\n",
-      "RMSE Testing = 0.07537818519431129\n",
-      "Out of Bag error = 0.5287574244172433\n",
+      "RMSE Training = 0.07541048725338477\n",
+      "RMSE Testing = 0.07548883982515656\n",
+      "Out of Bag error = 0.5308605375592987\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 86\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07580928645392869\n",
-      "RMSE Testing = 0.07574575180992978\n",
-      "Out of Bag error = 0.5371757263765414\n",
+      "RMSE Training = 0.07561928119651404\n",
+      "RMSE Testing = 0.07563764864979002\n",
+      "Out of Bag error = 0.5328454671329259\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 87\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07514416069889376\n",
-      "RMSE Testing = 0.07518074805225035\n",
-      "Out of Bag error = 0.528045724680254\n",
+      "RMSE Training = 0.07488555989814716\n",
+      "RMSE Testing = 0.07491655223438118\n",
+      "Out of Bag error = 0.5241015227400045\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 88\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07515964460414426\n",
-      "RMSE Testing = 0.07510756254202518\n",
-      "Out of Bag error = 0.5272088989052488\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 89\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755033352778864\n",
-      "RMSE Testing = 0.07562223494186625\n",
-      "Out of Bag error = 0.5319465027282689\n",
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+      "RMSE Testing = 0.07624158498854411\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 90\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07607414322322624\n",
-      "RMSE Testing = 0.07622684493558891\n",
-      "Out of Bag error = 0.5398935150478943\n",
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+      "RMSE Testing = 0.0760381570747993\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 91\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075317083106847\n",
-      "RMSE Testing = 0.07552111717321952\n",
-      "Out of Bag error = 0.5288037381290077\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.0752930698293447\n",
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+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 92\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07620477267392281\n",
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-      "Out of Bag error = 0.5413247434960854\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 93\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07555096964349867\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 94\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07558232208408201\n",
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-      "Out of Bag error = 0.5321055859700753\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 95\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07559004488926935\n",
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-      "Out of Bag error = 0.5324249654259592\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 96\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07499471736854571\n",
-      "RMSE Testing = 0.07502742071490842\n",
-      "Out of Bag error = 0.5253866894493597\n",
-      "-----------------------------------------------------\n",
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+      "Out of Bag error = 0.5274652187961797\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 97\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07580647513012354\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 98\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 99\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 100\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07575919778747828\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 101\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07511759775444084\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 102\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07597171580695902\n",
-      "RMSE Testing = 0.07610354270394652\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 103\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07503511717273881\n",
-      "RMSE Testing = 0.07511560461693227\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 104\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07533730362556357\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 105\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07546039480263508\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 106\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760323930507647\n",
-      "RMSE Testing = 0.07601918285100154\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 107\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07528736216818574\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 108\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07562512449068448\n",
-      "RMSE Testing = 0.07571253087236926\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 109\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07568626072596554\n",
-      "RMSE Testing = 0.07573771839811165\n",
-      "Out of Bag error = 0.5331005256398983\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 110\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07564697191389236\n",
-      "RMSE Testing = 0.0757890927217293\n",
-      "Out of Bag error = 0.5326093959066616\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 111\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07570334223381936\n",
-      "RMSE Testing = 0.07588562091896942\n",
-      "Out of Bag error = 0.5340170463797541\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 112\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07535408815018987\n",
-      "RMSE Testing = 0.07550693048130673\n",
-      "Out of Bag error = 0.5304777075495626\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 113\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07522794975114078\n",
-      "RMSE Testing = 0.0752841401036194\n",
-      "Out of Bag error = 0.5263681497928017\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 114\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754482445066051\n",
-      "RMSE Testing = 0.07551892942489594\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 115\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07554038784803362\n",
-      "RMSE Testing = 0.0755593654294237\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 116\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07593183651893476\n",
-      "RMSE Testing = 0.07609500713506441\n",
-      "Out of Bag error = 0.5377758484907859\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 117\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07537914817146418\n",
-      "RMSE Testing = 0.07549641744127417\n",
-      "Out of Bag error = 0.5292622852957327\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 118\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07600657931592124\n",
-      "RMSE Testing = 0.07611905812620903\n",
-      "Out of Bag error = 0.5373814343488689\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 119\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07551092152880665\n",
-      "RMSE Testing = 0.07555840711358683\n",
-      "Out of Bag error = 0.5322863435650217\n",
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+      "RMSE Testing = 0.0758927594018813\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 120\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07516020719500298\n",
-      "RMSE Testing = 0.07524724905792793\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 121\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07579696497183885\n",
-      "RMSE Testing = 0.07588178792321262\n",
-      "Out of Bag error = 0.53552093295137\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 122\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0756635519030493\n",
-      "RMSE Testing = 0.0756766016763937\n",
-      "Out of Bag error = 0.533061642299923\n",
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+      "RMSE Testing = 0.07510217507803273\n",
+      "Out of Bag error = 0.5237888453882997\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 123\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07576103223788962\n",
-      "RMSE Testing = 0.07577012747352196\n",
-      "Out of Bag error = 0.5343184769789698\n",
-      "-----------------------------------------------------\n"
-     ]
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07489449028242154\n",
+      "RMSE Testing = 0.07487665522847722\n",
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+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 124\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07551382602247195\n",
-      "RMSE Testing = 0.07550459488608056\n",
-      "Out of Bag error = 0.5303192784883604\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 125\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754458075466175\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 126\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07609423196773961\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 127\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07557269734826384\n",
-      "RMSE Testing = 0.0757184736665953\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 128\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07580390703882564\n",
-      "RMSE Testing = 0.07575120819457035\n",
-      "Out of Bag error = 0.5347470374265648\n",
-      "-----------------------------------------------------\n",
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+      "RMSE Testing = 0.07551923096139883\n",
+      "Out of Bag error = 0.5304435570217383\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 129\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 130\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 131\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 132\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 133\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07576654392600504\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 134\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07524632151986212\n",
-      "RMSE Testing = 0.07525243104706666\n",
-      "Out of Bag error = 0.5277877072000249\n",
+      "RMSE Training = 0.07559779884616169\n",
+      "RMSE Testing = 0.07560160816073516\n",
+      "Out of Bag error = 0.5316323503541571\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 135\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07509161727050231\n",
-      "RMSE Testing = 0.07513963400303762\n",
-      "Out of Bag error = 0.5253190489062248\n",
+      "RMSE Training = 0.07548493157234351\n",
+      "RMSE Testing = 0.07550529550182622\n",
+      "Out of Bag error = 0.530530780743536\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 136\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0758646717742697\n",
-      "RMSE Testing = 0.07598125914760724\n",
-      "Out of Bag error = 0.535353738046289\n",
+      "RMSE Training = 0.07559438594411785\n",
+      "RMSE Testing = 0.07558139085307813\n",
+      "Out of Bag error = 0.5319963526177556\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 137\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07469566627113436\n",
-      "RMSE Testing = 0.07470599671915548\n",
-      "Out of Bag error = 0.5191694669292335\n",
+      "RMSE Training = 0.07606719014891675\n",
+      "RMSE Testing = 0.07614977783627305\n",
+      "Out of Bag error = 0.538516903798707\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 138\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07549939817673074\n",
-      "RMSE Testing = 0.07550539161270639\n",
-      "Out of Bag error = 0.5306874027859225\n",
+      "RMSE Training = 0.07527633850845934\n",
+      "RMSE Testing = 0.07538600153441269\n",
+      "Out of Bag error = 0.5271633389723712\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 139\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07551857972192913\n",
-      "RMSE Testing = 0.07566801332268781\n",
-      "Out of Bag error = 0.5296049516802613\n",
+      "RMSE Training = 0.07508079882959375\n",
+      "RMSE Testing = 0.0751306768314934\n",
+      "Out of Bag error = 0.5251190439243001\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 140\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577547779604596\n",
-      "RMSE Testing = 0.0758243993451162\n",
-      "Out of Bag error = 0.534082059835969\n",
+      "RMSE Training = 0.07542300433417934\n",
+      "RMSE Testing = 0.07546419614000158\n",
+      "Out of Bag error = 0.5289013014862635\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 141\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585299965186065\n",
-      "RMSE Testing = 0.07590018953192054\n",
-      "Out of Bag error = 0.5364312835156234\n",
+      "RMSE Training = 0.07496146313924734\n",
+      "RMSE Testing = 0.07501728904006791\n",
+      "Out of Bag error = 0.5228636526488961\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 142\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0752891530341262\n",
-      "RMSE Testing = 0.07531443855144906\n",
-      "Out of Bag error = 0.5270632600110174\n",
+      "RMSE Training = 0.07582489051612444\n",
+      "RMSE Testing = 0.07587593625796409\n",
+      "Out of Bag error = 0.5348630001531505\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 143\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07598059140573235\n",
-      "RMSE Testing = 0.07602898385823538\n",
-      "Out of Bag error = 0.5370152049887668\n",
+      "RMSE Training = 0.07582964507586387\n",
+      "RMSE Testing = 0.07584898816518688\n",
+      "Out of Bag error = 0.5350638384572969\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 144\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760004348537188\n",
-      "RMSE Testing = 0.0760251055574471\n",
-      "Out of Bag error = 0.5370221993017881\n",
+      "RMSE Training = 0.07590722861885048\n",
+      "RMSE Testing = 0.07598903157237517\n",
+      "Out of Bag error = 0.5358977471658709\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 145\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07544556580476432\n",
-      "RMSE Testing = 0.075424112501326\n",
-      "Out of Bag error = 0.5294837070196066\n",
+      "RMSE Training = 0.07579313298178339\n",
+      "RMSE Testing = 0.0757610311132874\n",
+      "Out of Bag error = 0.534624516361842\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 146\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07553417183878355\n",
-      "RMSE Testing = 0.07552005883303728\n",
-      "Out of Bag error = 0.5315984491599296\n",
+      "RMSE Training = 0.07588930954424\n",
+      "RMSE Testing = 0.07593060451900172\n",
+      "Out of Bag error = 0.5362338069025142\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 147\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07519726183967043\n",
-      "RMSE Testing = 0.07527536806221209\n",
-      "Out of Bag error = 0.5244983503412483\n",
+      "RMSE Training = 0.07528990322756395\n",
+      "RMSE Testing = 0.07532430510630686\n",
+      "Out of Bag error = 0.5279142519574018\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 148\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07544945199263443\n",
-      "RMSE Testing = 0.0754918176011963\n",
-      "Out of Bag error = 0.5300994777086873\n",
+      "RMSE Training = 0.07528478324060857\n",
+      "RMSE Testing = 0.07523829848653447\n",
+      "Out of Bag error = 0.5271783010901802\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 149\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07508585385783412\n",
-      "RMSE Testing = 0.07523582814683968\n",
-      "Out of Bag error = 0.5240265428260947\n",
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+      "RMSE Testing = 0.07622900470040014\n",
+      "Out of Bag error = 0.5409951974556859\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 150\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07521615137357378\n",
-      "RMSE Testing = 0.07521525407032933\n",
-      "Out of Bag error = 0.526951859379193\n",
+      "RMSE Training = 0.07511941262921648\n",
+      "RMSE Testing = 0.07507060969089943\n",
+      "Out of Bag error = 0.5254623100098572\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 151\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577782653552743\n",
-      "RMSE Testing = 0.07583342405047608\n",
-      "Out of Bag error = 0.5338842722823289\n",
+      "RMSE Training = 0.07569658460546116\n",
+      "RMSE Testing = 0.07569565822298001\n",
+      "Out of Bag error = 0.5329566382232824\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 152\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07542070987775251\n",
-      "RMSE Testing = 0.07545548905967299\n",
-      "Out of Bag error = 0.5289807143088211\n",
+      "RMSE Training = 0.07567326221973661\n",
+      "RMSE Testing = 0.07563943989042393\n",
+      "Out of Bag error = 0.5322388275286031\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 153\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07560066466630185\n",
-      "RMSE Testing = 0.07564597126841621\n",
-      "Out of Bag error = 0.5319648325949389\n",
+      "RMSE Training = 0.07569855952899127\n",
+      "RMSE Testing = 0.07579975522817625\n",
+      "Out of Bag error = 0.5327171169642767\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 154\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755750822692732\n",
-      "RMSE Testing = 0.07567669867277127\n",
-      "Out of Bag error = 0.5317477145185909\n",
+      "RMSE Training = 0.07572449128564218\n",
+      "RMSE Testing = 0.07578560164057171\n",
+      "Out of Bag error = 0.5341572307733209\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 155\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754501202190336\n",
-      "RMSE Testing = 0.0755117430392189\n",
-      "Out of Bag error = 0.52965065456122\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07521447659450417\n",
+      "RMSE Testing = 0.07525608874852636\n",
+      "Out of Bag error = 0.5270434844113645\n",
+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 156\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07536182430556722\n",
-      "RMSE Testing = 0.07536827712675692\n",
-      "Out of Bag error = 0.5287533671987179\n",
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+      "RMSE Testing = 0.0752291077844619\n",
+      "Out of Bag error = 0.525280084793879\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 157\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075751942221789\n",
-      "RMSE Testing = 0.07583432980155282\n",
-      "Out of Bag error = 0.5342336003814958\n",
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+      "RMSE Testing = 0.07542656285005786\n",
+      "Out of Bag error = 0.5285491168243385\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 158\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07518274840914574\n",
-      "RMSE Testing = 0.07517968710359282\n",
-      "Out of Bag error = 0.5250831459876747\n",
+      "RMSE Training = 0.07587849864445574\n",
+      "RMSE Testing = 0.07596933170925699\n",
+      "Out of Bag error = 0.536551127424307\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 159\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07542627032156865\n",
-      "RMSE Testing = 0.0754429814888806\n",
-      "Out of Bag error = 0.5287591468780638\n",
+      "RMSE Training = 0.07490980458357696\n",
+      "RMSE Testing = 0.07495831580481196\n",
+      "Out of Bag error = 0.5219740932053988\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 160\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07519152093241258\n",
-      "RMSE Testing = 0.07522233570176137\n",
-      "Out of Bag error = 0.5262431091182425\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07518923077766806\n",
+      "RMSE Testing = 0.07521600869869442\n",
+      "Out of Bag error = 0.526540284163709\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 161\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07575832497991737\n",
-      "RMSE Testing = 0.07582699444626298\n",
-      "Out of Bag error = 0.5332110273709982\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 162\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07596837747034454\n",
-      "RMSE Testing = 0.07601815405789811\n",
-      "Out of Bag error = 0.5359393470891554\n",
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+      "Out of Bag error = 0.5318741887995464\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 163\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07564265403822314\n",
-      "RMSE Testing = 0.07563356984917972\n",
-      "Out of Bag error = 0.5331690703630836\n",
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+      "Out of Bag error = 0.5373396808352661\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 164\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07581940934346577\n",
-      "RMSE Testing = 0.0757669218381685\n",
-      "Out of Bag error = 0.5350139467189327\n",
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+      "RMSE Testing = 0.07507408400277832\n",
+      "Out of Bag error = 0.5235400877572312\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 165\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07561513691707351\n",
-      "RMSE Testing = 0.07571986160137059\n",
-      "Out of Bag error = 0.5328844473371355\n",
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+      "RMSE Testing = 0.075846739319953\n",
+      "Out of Bag error = 0.5357796986573564\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 166\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07555105954326045\n",
-      "RMSE Testing = 0.07567488872918465\n",
-      "Out of Bag error = 0.5300897564602116\n",
+      "RMSE Training = 0.07561116367808225\n",
+      "RMSE Testing = 0.07563572326930719\n",
+      "Out of Bag error = 0.5314604242478678\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 167\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07526493587225877\n",
-      "RMSE Testing = 0.07533641214001072\n",
-      "Out of Bag error = 0.5273907466353802\n",
+      "RMSE Training = 0.07526587965188429\n",
+      "RMSE Testing = 0.07529159847897292\n",
+      "Out of Bag error = 0.5277704431817356\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 168\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07548684344459546\n",
-      "RMSE Testing = 0.07554187381780311\n",
-      "Out of Bag error = 0.5309134178996355\n",
+      "RMSE Training = 0.07554905601683537\n",
+      "RMSE Testing = 0.07558880953250043\n",
+      "Out of Bag error = 0.5300329358682286\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 169\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755383282027333\n",
-      "RMSE Testing = 0.07565175173156433\n",
-      "Out of Bag error = 0.5307471695122156\n",
+      "RMSE Training = 0.07566820205932379\n",
+      "RMSE Testing = 0.07573499144100648\n",
+      "Out of Bag error = 0.5329091602602599\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 170\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0751491267525114\n",
-      "RMSE Testing = 0.07513617744587316\n",
-      "Out of Bag error = 0.5249455844361309\n",
+      "RMSE Training = 0.07526938640734773\n",
+      "RMSE Testing = 0.07525800036355565\n",
+      "Out of Bag error = 0.5278125477646618\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 171\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07464751572980277\n",
-      "RMSE Testing = 0.07465969887931148\n",
-      "Out of Bag error = 0.5186518381099707\n",
+      "RMSE Training = 0.07547934049658159\n",
+      "RMSE Testing = 0.07552550230911237\n",
+      "Out of Bag error = 0.5300264821753753\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 172\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075449061049901\n",
-      "RMSE Testing = 0.07549111234965059\n",
-      "Out of Bag error = 0.5296850723642444\n",
+      "RMSE Training = 0.07576127636529946\n",
+      "RMSE Testing = 0.07571707019293546\n",
+      "Out of Bag error = 0.5342938429337921\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 173\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07503035248298005\n",
-      "RMSE Testing = 0.07513943844042792\n",
-      "Out of Bag error = 0.524090550348806\n",
+      "RMSE Training = 0.07563813500743709\n",
+      "RMSE Testing = 0.07570872435827306\n",
+      "Out of Bag error = 0.5322713500300403\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 174\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07524873107425531\n",
-      "RMSE Testing = 0.07521340865988332\n",
-      "Out of Bag error = 0.5264008961543701\n",
+      "RMSE Training = 0.0751760397867024\n",
+      "RMSE Testing = 0.07521002733111645\n",
+      "Out of Bag error = 0.5264731769782939\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 175\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0753535350749465\n",
-      "RMSE Testing = 0.07537311974400965\n",
-      "Out of Bag error = 0.528004230137531\n",
+      "RMSE Training = 0.07545431139865184\n",
+      "RMSE Testing = 0.07555768434216027\n",
+      "Out of Bag error = 0.529464082219694\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 176\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07552194774404555\n",
-      "RMSE Testing = 0.07560137843242248\n",
-      "Out of Bag error = 0.5309514611470927\n",
+      "RMSE Training = 0.07570976076499292\n",
+      "RMSE Testing = 0.07574034870286997\n",
+      "Out of Bag error = 0.5328394841428661\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 177\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07547346619592746\n",
-      "RMSE Testing = 0.0755432625647683\n",
-      "Out of Bag error = 0.5299019849871802\n",
+      "RMSE Training = 0.07567034042015638\n",
+      "RMSE Testing = 0.07573031097225968\n",
+      "Out of Bag error = 0.5323604448845918\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 178\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07521709772454141\n",
-      "RMSE Testing = 0.07526800517072217\n",
-      "Out of Bag error = 0.5253047572096603\n",
+      "RMSE Training = 0.07526038557058522\n",
+      "RMSE Testing = 0.07525650716176677\n",
+      "Out of Bag error = 0.526089110752282\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 179\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07568185576429258\n",
-      "RMSE Testing = 0.07573073583613842\n",
-      "Out of Bag error = 0.5320329099040034\n",
+      "RMSE Training = 0.07541305044277732\n",
+      "RMSE Testing = 0.07543332164547781\n",
+      "Out of Bag error = 0.5287226897221448\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 180\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07528925333014029\n",
-      "RMSE Testing = 0.07532925871595453\n",
-      "Out of Bag error = 0.5274126555294465\n",
+      "RMSE Training = 0.07535735959500761\n",
+      "RMSE Testing = 0.07528217901599711\n",
+      "Out of Bag error = 0.5281843928545433\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 181\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0752665607748491\n",
-      "RMSE Testing = 0.07523008960681767\n",
-      "Out of Bag error = 0.5263849539668879\n",
+      "RMSE Training = 0.07583166915872945\n",
+      "RMSE Testing = 0.07578634921392291\n",
+      "Out of Bag error = 0.5343280606978761\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 182\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755097439837491\n",
-      "RMSE Testing = 0.07559701440754722\n",
-      "Out of Bag error = 0.5299322164182689\n",
+      "RMSE Training = 0.07600591391402833\n",
+      "RMSE Testing = 0.07606090810066596\n",
+      "Out of Bag error = 0.5366394855517144\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 183\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07571763359155233\n",
-      "RMSE Testing = 0.07582358743890905\n",
-      "Out of Bag error = 0.5335558241727628\n",
+      "RMSE Training = 0.07544775633330911\n",
+      "RMSE Testing = 0.07556391619806617\n",
+      "Out of Bag error = 0.5287867956132797\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 184\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07596139331313315\n",
-      "RMSE Testing = 0.07605458252988606\n",
-      "Out of Bag error = 0.5358848018193639\n",
+      "RMSE Training = 0.07548608528258915\n",
+      "RMSE Testing = 0.07553266702050462\n",
+      "Out of Bag error = 0.5300999201415089\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 185\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07568088627653713\n",
-      "RMSE Testing = 0.0756793860654808\n",
-      "Out of Bag error = 0.5322047536481868\n",
+      "RMSE Training = 0.07585327656401604\n",
+      "RMSE Testing = 0.07585056945901508\n",
+      "Out of Bag error = 0.5348833177792065\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 186\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07507514707213\n",
-      "RMSE Testing = 0.07513213752118848\n",
-      "Out of Bag error = 0.5240657408092674\n",
+      "RMSE Training = 0.07554914876761624\n",
+      "RMSE Testing = 0.07555626082162062\n",
+      "Out of Bag error = 0.5303077574084444\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 187\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07574663665272587\n",
-      "RMSE Testing = 0.07573871188987572\n",
-      "Out of Bag error = 0.5333884178348999\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07524189000258305\n",
+      "RMSE Testing = 0.07525693349135831\n",
+      "Out of Bag error = 0.5267442609800217\n",
+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 188\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07543836772090402\n",
-      "RMSE Testing = 0.07550089779341042\n",
-      "Out of Bag error = 0.5292383670784413\n",
+      "RMSE Training = 0.07530149647278658\n",
+      "RMSE Testing = 0.07533511890957867\n",
+      "Out of Bag error = 0.5272509807542989\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 189\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07542761003833338\n",
-      "RMSE Testing = 0.07547695903646885\n",
-      "Out of Bag error = 0.5292038764363014\n",
+      "RMSE Training = 0.07553079354365064\n",
+      "RMSE Testing = 0.07554667075646676\n",
+      "Out of Bag error = 0.5307557784708414\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 190\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07565283260323509\n",
-      "RMSE Testing = 0.07566057902458635\n",
-      "Out of Bag error = 0.5330652619357432\n",
+      "RMSE Training = 0.07525446135450481\n",
+      "RMSE Testing = 0.07521828171188447\n",
+      "Out of Bag error = 0.5266782390328086\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 191\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07541953382251464\n",
-      "RMSE Testing = 0.07539555471134284\n",
-      "Out of Bag error = 0.5293957845824322\n",
+      "RMSE Training = 0.07576889559332296\n",
+      "RMSE Testing = 0.07578884009444416\n",
+      "Out of Bag error = 0.5340800301289093\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 192\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07543344216446879\n",
-      "RMSE Testing = 0.075474890251476\n",
-      "Out of Bag error = 0.5292450426919462\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07554099617224819\n",
+      "RMSE Testing = 0.07554777691190619\n",
+      "Out of Bag error = 0.5296599095865839\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 193\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07532046531609998\n",
-      "RMSE Testing = 0.07540775544829445\n",
-      "Out of Bag error = 0.5274217007429486\n",
+      "RMSE Training = 0.07585600595961418\n",
+      "RMSE Testing = 0.07586840577822077\n",
+      "Out of Bag error = 0.535299987894567\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 194\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07570966340398066\n",
-      "RMSE Testing = 0.07579886599744201\n",
-      "Out of Bag error = 0.5325330766487422\n",
+      "RMSE Training = 0.07543411816904709\n",
+      "RMSE Testing = 0.0754509029706389\n",
+      "Out of Bag error = 0.5299034118787473\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 195\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07575716116038005\n",
-      "RMSE Testing = 0.07577302255840493\n",
-      "Out of Bag error = 0.5341217708224284\n",
+      "RMSE Training = 0.0756142594705356\n",
+      "RMSE Testing = 0.07565605619613076\n",
+      "Out of Bag error = 0.5323111909474576\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 196\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07601031654619551\n",
-      "RMSE Testing = 0.07604723714506438\n",
-      "Out of Bag error = 0.5364578015696316\n",
+      "RMSE Training = 0.07503585863167599\n",
+      "RMSE Testing = 0.0751058599319214\n",
+      "Out of Bag error = 0.5232014304948289\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 197\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545140434553027\n",
-      "RMSE Testing = 0.07551499640537154\n",
-      "Out of Bag error = 0.529292702199709\n",
+      "RMSE Training = 0.07533246819686014\n",
+      "RMSE Testing = 0.07534299616029376\n",
+      "Out of Bag error = 0.5277539388363498\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 198\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754492383733348\n",
-      "RMSE Testing = 0.07557081629340981\n",
-      "Out of Bag error = 0.5292337564559814\n",
+      "RMSE Training = 0.07522045040463712\n",
+      "RMSE Testing = 0.07523951896296646\n",
+      "Out of Bag error = 0.5260775320915072\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 199\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07560427538765978\n",
-      "RMSE Testing = 0.0756817560801624\n",
-      "Out of Bag error = 0.531347607452984\n",
+      "RMSE Training = 0.0759499813172658\n",
+      "RMSE Testing = 0.07600676361917018\n",
+      "Out of Bag error = 0.5352738806959693\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 200\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07556424778431499\n",
-      "RMSE Testing = 0.07562011411256366\n",
-      "Out of Bag error = 0.5303383076185137\n",
+      "RMSE Training = 0.07553043961035649\n",
+      "RMSE Testing = 0.0755970238144939\n",
+      "Out of Bag error = 0.5307622758634305\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.07178652050458452\n",
+      "RMSE Testing = 0.07178200004422779\n",
+      "Out of Bag error = 1.0356267901315057\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0726697834068104\n",
+      "RMSE Testing = 0.07308400704216603\n",
+      "Out of Bag error = 0.8592995069973972\n",
       "-----------------------------------------------------\n"
      ]
     },
     {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06487242306434574\n",
+      "RMSE Testing = 0.06451966982478567\n",
+      "Out of Bag error = 0.6787140736816983\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0644133809511976\n",
+      "RMSE Testing = 0.064661660735457\n",
+      "Out of Bag error = 0.5829130983855079\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0663598040395997\n",
+      "RMSE Testing = 0.06682981965883114\n",
+      "Out of Bag error = 0.5553033135410621\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06519051388152254\n",
+      "RMSE Testing = 0.06462270035134408\n",
+      "Out of Bag error = 0.5105767700601486\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0662196346450886\n",
+      "RMSE Testing = 0.06632569903368388\n",
+      "Out of Bag error = 0.4909468012970565\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06622372357429944\n",
+      "RMSE Testing = 0.06615809942121673\n",
+      "Out of Bag error = 0.4684481073230691\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06434115889543943\n",
+      "RMSE Testing = 0.06428319661123529\n",
+      "Out of Bag error = 0.4369225878897531\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06512096032761854\n",
+      "RMSE Testing = 0.06530886838787078\n",
+      "Out of Bag error = 0.4334347210704312\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06430791734155433\n",
+      "RMSE Testing = 0.06412010408765621\n",
+      "Out of Bag error = 0.4175385710403421\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06505826462743876\n",
+      "RMSE Testing = 0.06478348740750697\n",
+      "Out of Bag error = 0.41916837216229197\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06484481459821478\n",
+      "RMSE Testing = 0.06480105566516324\n",
+      "Out of Bag error = 0.41579271024683007\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0642547747464278\n",
+      "RMSE Testing = 0.06401117330414106\n",
+      "Out of Bag error = 0.40910130060997174\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06436675614441278\n",
+      "RMSE Testing = 0.06407040976893327\n",
+      "Out of Bag error = 0.4037411212200661\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06390500870817678\n",
+      "RMSE Testing = 0.06396442350234294\n",
+      "Out of Bag error = 0.3993135463826897\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06511954501375637\n",
+      "RMSE Testing = 0.0650432562587933\n",
+      "Out of Bag error = 0.41099033717619193\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06487464959261156\n",
+      "RMSE Testing = 0.0648198658129944\n",
+      "Out of Bag error = 0.40680368135490824\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06362128189895408\n",
+      "RMSE Testing = 0.06365153654423275\n",
+      "Out of Bag error = 0.3908470464974198\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06215976611859999\n",
+      "RMSE Testing = 0.061999044921853964\n",
+      "Out of Bag error = 0.3713279696234391\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06557541526636546\n",
+      "RMSE Testing = 0.06573811694814168\n",
+      "Out of Bag error = 0.4118586293363878\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06616864279425463\n",
+      "RMSE Testing = 0.06636917574584694\n",
+      "Out of Bag error = 0.4205290160009133\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06459936873664758\n",
+      "RMSE Testing = 0.0646836264797117\n",
+      "Out of Bag error = 0.4001268313177896\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06480807125517422\n",
+      "RMSE Testing = 0.06479376896372703\n",
+      "Out of Bag error = 0.400757158343614\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06364801512176574\n",
+      "RMSE Testing = 0.06373716818129668\n",
+      "Out of Bag error = 0.3865154653635585\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06486145155546025\n",
+      "RMSE Testing = 0.06492146568425641\n",
+      "Out of Bag error = 0.4009342596891525\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06430086590674514\n",
+      "RMSE Testing = 0.0642827612311269\n",
+      "Out of Bag error = 0.3933189498615797\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06511240375853918\n",
+      "RMSE Testing = 0.06491288401663023\n",
+      "Out of Bag error = 0.40361938738509606\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06352262784653906\n",
+      "RMSE Testing = 0.06347215596536432\n",
+      "Out of Bag error = 0.3830497223854249\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06450185011422013\n",
+      "RMSE Testing = 0.06454044515427419\n",
+      "Out of Bag error = 0.39433857076111345\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06416644479346552\n",
+      "RMSE Testing = 0.06420587550875544\n",
+      "Out of Bag error = 0.39156595781872333\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 32\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06446683994288313\n",
+      "RMSE Testing = 0.06434757485427642\n",
+      "Out of Bag error = 0.3926509477259156\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 33\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06441247030027095\n",
+      "RMSE Testing = 0.06453441642732527\n",
+      "Out of Bag error = 0.39221102094753507\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 34\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06458472136515386\n",
+      "RMSE Testing = 0.06461250260835895\n",
+      "Out of Bag error = 0.39248598880930136\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 35\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06492953101299971\n",
+      "RMSE Testing = 0.06508067291025563\n",
+      "Out of Bag error = 0.3997254194143144\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 36\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06331290056253389\n",
+      "RMSE Testing = 0.06332521394662652\n",
+      "Out of Bag error = 0.3796634690808921\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 37\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06438373221214208\n",
+      "RMSE Testing = 0.06433133835911278\n",
+      "Out of Bag error = 0.3914653814300143\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 38\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06512173419363478\n",
+      "RMSE Testing = 0.0650264324805011\n",
+      "Out of Bag error = 0.39925938078390716\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 39\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06517220159094442\n",
+      "RMSE Testing = 0.06516749239784043\n",
+      "Out of Bag error = 0.4019999979376598\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 40\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0648387145506503\n",
+      "RMSE Testing = 0.06493096694285816\n",
+      "Out of Bag error = 0.397363636364967\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 41\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06474820110088149\n",
+      "RMSE Testing = 0.06464317926388186\n",
+      "Out of Bag error = 0.39507951038316297\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 42\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06334185538614115\n",
+      "RMSE Testing = 0.06344131044133298\n",
+      "Out of Bag error = 0.37845666719867366\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 43\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06408228384896968\n",
+      "RMSE Testing = 0.06414785015732677\n",
+      "Out of Bag error = 0.38534614022487435\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 44\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0641358167604925\n",
+      "RMSE Testing = 0.06401958892346403\n",
+      "Out of Bag error = 0.3876016131454201\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 45\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06425533878393569\n",
+      "RMSE Testing = 0.06423080606161576\n",
+      "Out of Bag error = 0.3884665558413696\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 46\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06422880448156444\n",
+      "RMSE Testing = 0.06430218243323338\n",
+      "Out of Bag error = 0.3884496811311422\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 47\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0644130912147063\n",
+      "RMSE Testing = 0.06440838806845307\n",
+      "Out of Bag error = 0.39071957785973593\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 48\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06446185258265448\n",
+      "RMSE Testing = 0.06454226468213035\n",
+      "Out of Bag error = 0.3905410098668532\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 49\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06460046565121814\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 50\n",
+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "Out of Bag error = 0.37711608722313494\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 122\n",
+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
+      "RMSE Training = 0.06436073501109733\n",
+      "RMSE Testing = 0.06438803051921273\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 124\n",
+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "Number of trees = 125\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Out of Bag error = 0.38251274330219254\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "RMSE Testing = 0.06378819001862615\n",
+      "Out of Bag error = 0.3803633844231918\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 198\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06457782837636127\n",
+      "RMSE Testing = 0.06453505382232247\n",
+      "Out of Bag error = 0.38803048637685233\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 199\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06393745139275225\n",
+      "RMSE Testing = 0.06398972210665098\n",
+      "Out of Bag error = 0.3809930802135987\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 200\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06430447695533062\n",
+      "RMSE Testing = 0.06429056587197565\n",
+      "Out of Bag error = 0.38463565335483946\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06553401012972591\n",
+      "RMSE Testing = 0.06550804447191212\n",
+      "Out of Bag error = 0.9908918423885418\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06387133042837698\n",
+      "RMSE Testing = 0.06451192024000502\n",
+      "Out of Bag error = 0.784177451957365\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061202182678221674\n",
+      "RMSE Testing = 0.06121238092300961\n",
+      "Out of Bag error = 0.6240928441127434\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.059894405324473654\n",
+      "RMSE Testing = 0.059656244194449384\n",
+      "Out of Bag error = 0.5281402117824234\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06138008488929333\n",
+      "RMSE Testing = 0.061709006034034855\n",
+      "Out of Bag error = 0.4874484801391616\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061923913272474165\n",
+      "RMSE Testing = 0.06164440082534928\n",
+      "Out of Bag error = 0.44906090621213773\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06164181287418358\n",
+      "RMSE Testing = 0.06149302032079954\n",
+      "Out of Bag error = 0.4200279728394487\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061698016479165264\n",
+      "RMSE Testing = 0.06150613201623101\n",
+      "Out of Bag error = 0.39892114483185404\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060935816472520256\n",
+      "RMSE Testing = 0.0609529284673679\n",
+      "Out of Bag error = 0.37978340465360527\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06127626635171092\n",
+      "RMSE Testing = 0.06135688488768567\n",
+      "Out of Bag error = 0.3777089714168403\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060073485315508136\n",
+      "RMSE Testing = 0.06009569282024065\n",
+      "Out of Bag error = 0.35819510706166297\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06141701157037845\n",
+      "RMSE Testing = 0.061365552055427555\n",
+      "Out of Bag error = 0.3718622843964467\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06132937024780869\n",
+      "RMSE Testing = 0.06105013722351488\n",
+      "Out of Bag error = 0.36387576408508016\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.0617761665389836\n",
+      "RMSE Testing = 0.06200491458424704\n",
+      "Out of Bag error = 0.371484348422994\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.059910818075094775\n",
+      "RMSE Testing = 0.05970959090253096\n",
+      "Out of Bag error = 0.348452090012462\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061197908451467664\n",
+      "RMSE Testing = 0.06114343412960055\n",
+      "Out of Bag error = 0.3603435947838507\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060809465513254335\n",
+      "RMSE Testing = 0.06092085145838756\n",
+      "Out of Bag error = 0.3519935020158699\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06065813972233147\n",
+      "RMSE Testing = 0.06062475790828999\n",
+      "Out of Bag error = 0.35155417890115526\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06094977746338827\n",
+      "RMSE Testing = 0.06089806474481897\n",
+      "Out of Bag error = 0.3538944786111735\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06034146644632125\n",
+      "RMSE Testing = 0.06028089437117893\n",
+      "Out of Bag error = 0.34760298500235715\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06140212329287655\n",
+      "RMSE Testing = 0.06131345506190292\n",
+      "Out of Bag error = 0.3581428652125612\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06063846173170615\n",
+      "RMSE Testing = 0.060598510153520836\n",
+      "Out of Bag error = 0.35010134184588815\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06105689861615602\n",
+      "RMSE Testing = 0.0609852755258435\n",
+      "Out of Bag error = 0.3536024145647128\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06141989592914422\n",
+      "RMSE Testing = 0.06138488651079797\n",
+      "Out of Bag error = 0.35686888297736785\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06048517956058511\n",
+      "RMSE Testing = 0.060381439485088576\n",
+      "Out of Bag error = 0.3460494064825067\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06102609018043307\n",
+      "RMSE Testing = 0.06093999156621187\n",
+      "Out of Bag error = 0.35115264487337966\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06081501914167149\n",
+      "RMSE Testing = 0.06086725558003485\n",
+      "Out of Bag error = 0.34995281985083226\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060351737654026204\n",
+      "RMSE Testing = 0.06026179512820079\n",
+      "Out of Bag error = 0.34395089361486464\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06025591663894929\n",
+      "RMSE Testing = 0.06022577999308729\n",
+      "Out of Bag error = 0.341515111267\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060375685119655076\n",
+      "RMSE Testing = 0.06019375744622302\n",
+      "Out of Bag error = 0.3451857687826104\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
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+      "RMSE Testing = 0.060834482910836665\n",
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+      "-----------------------------------------------------\n",
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+      "Number of trees = 32\n",
+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "Number of trees = 37\n",
+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "Out of Bag error = 0.3421095200871126\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 105\n",
+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "RMSE Training = 0.0601161569441027\n",
+      "RMSE Testing = 0.060039651872003594\n",
+      "Out of Bag error = 0.3363063784816626\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 180\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06037055517154151\n",
+      "RMSE Testing = 0.060244232798847906\n",
+      "Out of Bag error = 0.3386949425761038\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 181\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060449172638294336\n",
+      "RMSE Testing = 0.06031052428571754\n",
+      "Out of Bag error = 0.34027600801633806\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 182\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06014420794953651\n",
+      "RMSE Testing = 0.06007856140797372\n",
+      "Out of Bag error = 0.33641739483327104\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 183\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06014758697180652\n",
+      "RMSE Testing = 0.06010694939765578\n",
+      "Out of Bag error = 0.33591597328879075\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 184\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06023139907559093\n",
+      "RMSE Testing = 0.060173406414336605\n",
+      "Out of Bag error = 0.3367623394201132\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 185\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060370214939271596\n",
+      "RMSE Testing = 0.06031444824592823\n",
+      "Out of Bag error = 0.339010881179527\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 186\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060192776143814096\n",
+      "RMSE Testing = 0.06013591104827516\n",
+      "Out of Bag error = 0.3367175578655172\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 187\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06019330609577145\n",
+      "RMSE Testing = 0.06017079911524701\n",
+      "Out of Bag error = 0.3369526219175011\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 188\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060163587702625164\n",
+      "RMSE Testing = 0.060163186708657335\n",
+      "Out of Bag error = 0.3369376702658231\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 189\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060227918753759144\n",
+      "RMSE Testing = 0.06011396628218755\n",
+      "Out of Bag error = 0.3375215483558187\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 190\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060336974134015856\n",
+      "RMSE Testing = 0.06029178455444939\n",
+      "Out of Bag error = 0.3385432431311683\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 191\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06022098522327355\n",
+      "RMSE Testing = 0.06008635289680366\n",
+      "Out of Bag error = 0.3366435120814636\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 192\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.059928935199137556\n",
+      "RMSE Testing = 0.05985764204616641\n",
+      "Out of Bag error = 0.33383012697262415\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 193\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060524834600750546\n",
+      "RMSE Testing = 0.06049595406071562\n",
+      "Out of Bag error = 0.34036345732852596\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 194\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06030523233714351\n",
+      "RMSE Testing = 0.06021839551200955\n",
+      "Out of Bag error = 0.33772313714402846\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 195\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060226891126613336\n",
+      "RMSE Testing = 0.0601804804108637\n",
+      "Out of Bag error = 0.33676349174013376\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 196\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06030489654929433\n",
+      "RMSE Testing = 0.06030600425288481\n",
+      "Out of Bag error = 0.3380837995603131\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 197\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06023822339776932\n",
+      "RMSE Testing = 0.06017626884793763\n",
+      "Out of Bag error = 0.33749637250859343\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 198\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06029938681903951\n",
+      "RMSE Testing = 0.060230350032389815\n",
+      "Out of Bag error = 0.3376266516997296\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 199\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060240773381599944\n",
+      "RMSE Testing = 0.06017051094806937\n",
+      "Out of Bag error = 0.337309379880469\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 200\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060629429089982256\n",
+      "RMSE Testing = 0.060618103840524476\n",
+      "Out of Bag error = 0.3414631202236917\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06622081557708329\n",
+      "RMSE Testing = 0.06613917761372051\n",
+      "Out of Bag error = 0.9989227052768015\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0624149760545714\n",
+      "RMSE Testing = 0.0625513990489035\n",
+      "Out of Bag error = 0.768686850974345\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06247988883026455\n",
+      "RMSE Testing = 0.06204564554594899\n",
+      "Out of Bag error = 0.61496979821353\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0615014623675554\n",
+      "RMSE Testing = 0.06159492015431214\n",
+      "Out of Bag error = 0.5278360340266047\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06160913156835167\n",
+      "RMSE Testing = 0.061383865268429504\n",
+      "Out of Bag error = 0.46251462122022124\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06132364737970385\n",
+      "RMSE Testing = 0.06130166113151372\n",
+      "Out of Bag error = 0.42674838784162505\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06123026111351374\n",
+      "RMSE Testing = 0.06110618248553681\n",
+      "Out of Bag error = 0.3961969015271327\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06155600535835478\n",
+      "RMSE Testing = 0.06126807421089649\n",
+      "Out of Bag error = 0.3878838937484169\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06167336302116192\n",
+      "RMSE Testing = 0.06158846594006345\n",
+      "Out of Bag error = 0.3726181914456862\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091190741063583\n",
+      "RMSE Testing = 0.06086388827637057\n",
+      "Out of Bag error = 0.3610159741723852\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06130223343992548\n",
+      "RMSE Testing = 0.06128178958743534\n",
+      "Out of Bag error = 0.35927355812537504\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06116055054491849\n",
+      "RMSE Testing = 0.06106334541168342\n",
+      "Out of Bag error = 0.3549954834185199\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060998430514659675\n",
+      "RMSE Testing = 0.06079251958832349\n",
+      "Out of Bag error = 0.35299582332932067\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060836362126178455\n",
+      "RMSE Testing = 0.06066256261776468\n",
+      "Out of Bag error = 0.3498292952300833\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060873163507187744\n",
+      "RMSE Testing = 0.060808667149011286\n",
+      "Out of Bag error = 0.3497604866762827\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06061544757216536\n",
+      "RMSE Testing = 0.06057025144962257\n",
+      "Out of Bag error = 0.3458474368297687\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084252727042494\n",
+      "RMSE Testing = 0.06080246092048817\n",
+      "Out of Bag error = 0.34660515660765095\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06079823687803676\n",
+      "RMSE Testing = 0.06070200330774241\n",
+      "Out of Bag error = 0.3453835586718533\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060671217058017236\n",
+      "RMSE Testing = 0.06052508423873683\n",
+      "Out of Bag error = 0.3451124217227414\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06133128374509785\n",
+      "RMSE Testing = 0.06126421367209876\n",
+      "Out of Bag error = 0.3506528790102406\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084132600511309\n",
+      "RMSE Testing = 0.06071607826143214\n",
+      "Out of Bag error = 0.34544187451641445\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.061091267021912955\n",
+      "RMSE Testing = 0.06103577145569168\n",
+      "Out of Bag error = 0.3486954683569904\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06121797121682471\n",
+      "RMSE Testing = 0.06108657953810035\n",
+      "Out of Bag error = 0.3490989334514982\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096410487616257\n",
+      "RMSE Testing = 0.060961726092832166\n",
+      "Out of Bag error = 0.3466600878678811\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094814300147363\n",
+      "RMSE Testing = 0.060898995141142845\n",
+      "Out of Bag error = 0.3468295416027555\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.061004208841215025\n",
+      "RMSE Testing = 0.060921125221089424\n",
+      "Out of Bag error = 0.34684226925772954\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06113235948900906\n",
+      "RMSE Testing = 0.06102763495972748\n",
+      "Out of Bag error = 0.34804506463535495\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06090459775712141\n",
+      "RMSE Testing = 0.06084043281210004\n",
+      "Out of Bag error = 0.34542219151867265\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060708310704548255\n",
+      "RMSE Testing = 0.060637151549991705\n",
+      "Out of Bag error = 0.34320478303819374\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060988187086323566\n",
+      "RMSE Testing = 0.06086628412812807\n",
+      "Out of Bag error = 0.3463913425874309\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06095907901613247\n",
+      "RMSE Testing = 0.06079365754064634\n",
+      "Out of Bag error = 0.34664907979702814\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 32\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096464696630373\n",
+      "RMSE Testing = 0.06095371703892871\n",
+      "Out of Bag error = 0.3464643014684583\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 33\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06086266180185784\n",
+      "RMSE Testing = 0.060764766858585485\n",
+      "Out of Bag error = 0.34486150213068767\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 34\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06095714464053971\n",
+      "RMSE Testing = 0.06082074291319921\n",
+      "Out of Bag error = 0.3449102624618317\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 35\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06074326171351361\n",
+      "RMSE Testing = 0.06066023581446567\n",
+      "Out of Bag error = 0.34314904339924696\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 36\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096622595004042\n",
+      "RMSE Testing = 0.06089038333182094\n",
+      "Out of Bag error = 0.34622912011633733\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 37\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060985691100965946\n",
+      "RMSE Testing = 0.06087853093517955\n",
+      "Out of Bag error = 0.3471187906460547\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 38\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085587302042863\n",
+      "RMSE Testing = 0.060703517520556735\n",
+      "Out of Bag error = 0.3452735742882294\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 39\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091170459712678\n",
+      "RMSE Testing = 0.060830817126428814\n",
+      "Out of Bag error = 0.34517408323035487\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 40\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096999038724219\n",
+      "RMSE Testing = 0.06086437952795707\n",
+      "Out of Bag error = 0.345131248829713\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 41\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081101520606898\n",
+      "RMSE Testing = 0.060810976927114055\n",
+      "Out of Bag error = 0.3439693362626654\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 42\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087901355872799\n",
+      "RMSE Testing = 0.06079092227620173\n",
+      "Out of Bag error = 0.3442874174280868\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 43\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094233446051818\n",
+      "RMSE Testing = 0.06088310131159382\n",
+      "Out of Bag error = 0.345226366638104\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 44\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06092070722437748\n",
+      "RMSE Testing = 0.06075954145837166\n",
+      "Out of Bag error = 0.34497468250435565\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 45\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06089276516672583\n",
+      "RMSE Testing = 0.06078989599552405\n",
+      "Out of Bag error = 0.34475924105183436\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 46\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094984799062038\n",
+      "RMSE Testing = 0.06089880084940855\n",
+      "Out of Bag error = 0.344680490462824\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 47\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094219814640235\n",
+      "RMSE Testing = 0.06083458156078762\n",
+      "Out of Bag error = 0.3449351950125156\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 48\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088401674427648\n",
+      "RMSE Testing = 0.060800812224900545\n",
+      "Out of Bag error = 0.34419154255250406\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 49\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0610020109190267\n",
+      "RMSE Testing = 0.06088586957703791\n",
+      "Out of Bag error = 0.3447302480650438\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 50\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060878064570425106\n",
+      "RMSE Testing = 0.060801196579623\n",
+      "Out of Bag error = 0.3444074427831239\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 51\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096278557838253\n",
+      "RMSE Testing = 0.060820274824663956\n",
+      "Out of Bag error = 0.3454697505132665\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 52\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096978162253482\n",
+      "RMSE Testing = 0.06093085511950971\n",
+      "Out of Bag error = 0.34556644600555925\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 53\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085870219213346\n",
+      "RMSE Testing = 0.060817915650445566\n",
+      "Out of Bag error = 0.3440971324520146\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 54\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060854764191413356\n",
+      "RMSE Testing = 0.06074312349605038\n",
+      "Out of Bag error = 0.3443843295474925\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 55\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060932077157694685\n",
+      "RMSE Testing = 0.06080227653158256\n",
+      "Out of Bag error = 0.3447215495845441\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 56\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091473886990663\n",
+      "RMSE Testing = 0.06087773746319691\n",
+      "Out of Bag error = 0.34430633281462997\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 57\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087993650755384\n",
+      "RMSE Testing = 0.060745173856172584\n",
+      "Out of Bag error = 0.3443967680478708\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 58\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060881082024453206\n",
+      "RMSE Testing = 0.0607774610198097\n",
+      "Out of Bag error = 0.34386948980296017\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 59\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0609442675579695\n",
+      "RMSE Testing = 0.06081544737631931\n",
+      "Out of Bag error = 0.3444909230392723\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 60\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091191272245984\n",
+      "RMSE Testing = 0.060757097023564556\n",
+      "Out of Bag error = 0.3448358413451369\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 61\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088583105337644\n",
+      "RMSE Testing = 0.06082686645694378\n",
+      "Out of Bag error = 0.3437289129625448\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 62\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060846337758554594\n",
+      "RMSE Testing = 0.06075384556867226\n",
+      "Out of Bag error = 0.34354834337194223\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 63\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06104941019136205\n",
+      "RMSE Testing = 0.060982411648140056\n",
+      "Out of Bag error = 0.3465583155648016\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 64\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060944312414737065\n",
+      "RMSE Testing = 0.06077570406568274\n",
+      "Out of Bag error = 0.34495995028114806\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 65\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06066086271218202\n",
+      "RMSE Testing = 0.060575294257608256\n",
+      "Out of Bag error = 0.3414713242143452\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 66\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060757381077378256\n",
+      "RMSE Testing = 0.06069288756181383\n",
+      "Out of Bag error = 0.3429616214390498\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 67\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060932352265966175\n",
+      "RMSE Testing = 0.06081438053984998\n",
+      "Out of Bag error = 0.34393790928729173\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 68\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060929834871926346\n",
+      "RMSE Testing = 0.06083920610277969\n",
+      "Out of Bag error = 0.3443783575166787\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 69\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06099840157811761\n",
+      "RMSE Testing = 0.0607838234519764\n",
+      "Out of Bag error = 0.3449747264743199\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 70\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060800272049978996\n",
+      "RMSE Testing = 0.06073180500792824\n",
+      "Out of Bag error = 0.3430160464489288\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 71\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06070983930408931\n",
+      "RMSE Testing = 0.06057529207086902\n",
+      "Out of Bag error = 0.342344260332444\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 72\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06092270673191047\n",
+      "RMSE Testing = 0.06081409054242182\n",
+      "Out of Bag error = 0.34442904521774836\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 73\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060995260399529495\n",
+      "RMSE Testing = 0.060932436663091075\n",
+      "Out of Bag error = 0.34479290854781536\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 74\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06071502880298243\n",
+      "RMSE Testing = 0.060586666688430116\n",
+      "Out of Bag error = 0.3421848466524061\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 75\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080599109303\n",
+      "RMSE Testing = 0.06076163128225607\n",
+      "Out of Bag error = 0.34318587629386427\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 76\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060890710954498495\n",
+      "RMSE Testing = 0.06081032897856836\n",
+      "Out of Bag error = 0.34381432650499827\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 77\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084619210670975\n",
+      "RMSE Testing = 0.06078689937883112\n",
+      "Out of Bag error = 0.34323646827541776\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 78\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0608453223373421\n",
+      "RMSE Testing = 0.06073382949842623\n",
+      "Out of Bag error = 0.3438515048255629\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 79\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06095845929514655\n",
+      "RMSE Testing = 0.06087158240714561\n",
+      "Out of Bag error = 0.3445310767556437\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 80\n",
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+      "Number of trees = 81\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 154\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060870415954317844\n",
+      "RMSE Testing = 0.060793815298246\n",
+      "Out of Bag error = 0.3432453779952319\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 155\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084843225359097\n",
+      "RMSE Testing = 0.060768465246795224\n",
+      "Out of Bag error = 0.3430080109235288\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 156\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081282021912525\n",
+      "RMSE Testing = 0.06073228280436834\n",
+      "Out of Bag error = 0.34269995209786897\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 157\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06078721248731548\n",
+      "RMSE Testing = 0.060707077963557586\n",
+      "Out of Bag error = 0.3424555024401913\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 158\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060825958241347844\n",
+      "RMSE Testing = 0.06077223262512689\n",
+      "Out of Bag error = 0.34297898457672293\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 159\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06071482830088064\n",
+      "RMSE Testing = 0.06062576597982824\n",
+      "Out of Bag error = 0.34174209241167414\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 160\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082084370344491\n",
+      "RMSE Testing = 0.06070802914868693\n",
+      "Out of Bag error = 0.34269315794986904\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 161\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082284235453397\n",
+      "RMSE Testing = 0.06076379620491856\n",
+      "Out of Bag error = 0.34287502526049796\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 162\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082149937610165\n",
+      "RMSE Testing = 0.06073583598231401\n",
+      "Out of Bag error = 0.34264088215380434\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 163\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060789935902683355\n",
+      "RMSE Testing = 0.060661590161132616\n",
+      "Out of Bag error = 0.34255908610010294\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 164\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06075076827794479\n",
+      "RMSE Testing = 0.06065692645489766\n",
+      "Out of Bag error = 0.3420224706126414\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 165\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0608285649152703\n",
+      "RMSE Testing = 0.060753799845022195\n",
+      "Out of Bag error = 0.3429329329132237\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 166\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084086980781807\n",
+      "RMSE Testing = 0.06077174183159937\n",
+      "Out of Bag error = 0.3430805960492944\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 167\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083254970673704\n",
+      "RMSE Testing = 0.06074492479008399\n",
+      "Out of Bag error = 0.3428896358906285\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 168\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06086529315132342\n",
+      "RMSE Testing = 0.06080795345906591\n",
+      "Out of Bag error = 0.3430581458659912\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 169\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088849596994317\n",
+      "RMSE Testing = 0.0607885579276104\n",
+      "Out of Bag error = 0.34344727228647215\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 170\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081266098944822\n",
+      "RMSE Testing = 0.060683374400124\n",
+      "Out of Bag error = 0.34278257828180364\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 171\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060881138636599365\n",
+      "RMSE Testing = 0.060783252916838504\n",
+      "Out of Bag error = 0.3431956000321273\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 172\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082839195621574\n",
+      "RMSE Testing = 0.06075421588621795\n",
+      "Out of Bag error = 0.3428692039927966\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 173\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081986285330182\n",
+      "RMSE Testing = 0.06068880409465124\n",
+      "Out of Bag error = 0.3428074513504944\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 174\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06098541568050677\n",
+      "RMSE Testing = 0.060947631293231885\n",
+      "Out of Bag error = 0.3443939239685229\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 175\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080340355580545\n",
+      "RMSE Testing = 0.06073413096490362\n",
+      "Out of Bag error = 0.34248260076862425\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 176\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060891593812645306\n",
+      "RMSE Testing = 0.060808588449915345\n",
+      "Out of Bag error = 0.34345928611778154\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 177\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081369119973475\n",
+      "RMSE Testing = 0.06073885085369231\n",
+      "Out of Bag error = 0.3430496429479596\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 178\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0608711962357756\n",
+      "RMSE Testing = 0.06075865698531539\n",
+      "Out of Bag error = 0.34319660515873085\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 179\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060829785141136905\n",
+      "RMSE Testing = 0.060747276866154123\n",
+      "Out of Bag error = 0.34254603879678686\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 180\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083990508880686\n",
+      "RMSE Testing = 0.060767605434844184\n",
+      "Out of Bag error = 0.34297648289289767\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 181\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088232776250392\n",
+      "RMSE Testing = 0.0608110083919992\n",
+      "Out of Bag error = 0.343490385964349\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 182\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060843006877505726\n",
+      "RMSE Testing = 0.06076021961854851\n",
+      "Out of Bag error = 0.34274114589109334\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 183\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060784286930503605\n",
+      "RMSE Testing = 0.06066257622321708\n",
+      "Out of Bag error = 0.34266701641964564\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 184\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087391706089289\n",
+      "RMSE Testing = 0.06077850474899417\n",
+      "Out of Bag error = 0.3432137821730148\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 185\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083663571398519\n",
+      "RMSE Testing = 0.06077503627179157\n",
+      "Out of Bag error = 0.34282348309580063\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 186\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081480231366606\n",
+      "RMSE Testing = 0.060690825478767293\n",
+      "Out of Bag error = 0.34274108998196906\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 187\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085438010823191\n",
+      "RMSE Testing = 0.06074995571867999\n",
+      "Out of Bag error = 0.3429869295015915\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 188\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085213590206372\n",
+      "RMSE Testing = 0.06075469391033729\n",
+      "Out of Bag error = 0.3432526164899947\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 189\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080761457377722\n",
+      "RMSE Testing = 0.06073977281040595\n",
+      "Out of Bag error = 0.3424491564209558\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 190\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084734610483842\n",
+      "RMSE Testing = 0.06077529381898443\n",
+      "Out of Bag error = 0.34323243900187766\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 191\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085405508502558\n",
+      "RMSE Testing = 0.060744580660350656\n",
+      "Out of Bag error = 0.3433797467117118\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 192\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084729961712916\n",
+      "RMSE Testing = 0.06081111943799011\n",
+      "Out of Bag error = 0.34325989244403776\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 193\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06073716811603248\n",
+      "RMSE Testing = 0.06064307051832411\n",
+      "Out of Bag error = 0.3417887226645167\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 194\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087244398860362\n",
+      "RMSE Testing = 0.060751995131652504\n",
+      "Out of Bag error = 0.3431860918055074\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 195\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083526029252395\n",
+      "RMSE Testing = 0.06073824437620058\n",
+      "Out of Bag error = 0.3431224768756135\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 196\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082897468868272\n",
+      "RMSE Testing = 0.06076472687598217\n",
+      "Out of Bag error = 0.3426771325451004\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 197\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085629286588542\n",
+      "RMSE Testing = 0.0607616161703697\n",
+      "Out of Bag error = 0.34314619175742916\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 198\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06090461198044198\n",
+      "RMSE Testing = 0.06082583043984453\n",
+      "Out of Bag error = 0.3434724491834149\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 199\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085235758969537\n",
+      "RMSE Testing = 0.06074395447276561\n",
+      "Out of Bag error = 0.3432606196402245\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 200\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080671948089893\n",
+      "RMSE Testing = 0.060708451895735524\n",
+      "Out of Bag error = 0.342527180164462\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06130257795764323\n",
+      "RMSE Testing = 0.0611533218631935\n",
+      "Out of Bag error = 0.9816331427859183\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06127097816599222\n",
+      "RMSE Testing = 0.06108258033302315\n",
+      "Out of Bag error = 0.7457310331684974\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060535711467073774\n",
+      "RMSE Testing = 0.06059226282998461\n",
+      "Out of Bag error = 0.6046426749439033\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06094126619709735\n",
+      "RMSE Testing = 0.060788925161511985\n",
+      "Out of Bag error = 0.4968494152250399\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060940962970267284\n",
+      "RMSE Testing = 0.06080343877395228\n",
+      "Out of Bag error = 0.4414771142184938\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0601712392672092\n",
+      "RMSE Testing = 0.06008858822741235\n",
+      "Out of Bag error = 0.40815849584155883\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060426465117423646\n",
+      "RMSE Testing = 0.060320380081397965\n",
+      "Out of Bag error = 0.3847940722199362\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06067381489332881\n",
+      "RMSE Testing = 0.06053130610240319\n",
+      "Out of Bag error = 0.3656173828018863\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06085933781978516\n",
+      "RMSE Testing = 0.06075839412560078\n",
+      "Out of Bag error = 0.3599579305630957\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06074987350266488\n",
+      "RMSE Testing = 0.06064520869907915\n",
+      "Out of Bag error = 0.35327625816796504\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06072748011200113\n",
+      "RMSE Testing = 0.06065774630589947\n",
+      "Out of Bag error = 0.3490578324516167\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0604371404628397\n",
+      "RMSE Testing = 0.06034942133242186\n",
+      "Out of Bag error = 0.34406431686372546\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06061122647582209\n",
+      "RMSE Testing = 0.06051787334397015\n",
+      "Out of Bag error = 0.3432348129409606\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060575192958832236\n",
+      "RMSE Testing = 0.06046266195682688\n",
+      "Out of Bag error = 0.3424739389866447\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06041313027298841\n",
+      "RMSE Testing = 0.060317794691478265\n",
+      "Out of Bag error = 0.34022744854601134\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06056545366855113\n",
+      "RMSE Testing = 0.060429807660394796\n",
+      "Out of Bag error = 0.3409988449257531\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06068735121927106\n",
+      "RMSE Testing = 0.0605711324007566\n",
+      "Out of Bag error = 0.3415145507922735\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06024688737300401\n",
+      "RMSE Testing = 0.06016560955667929\n",
+      "Out of Bag error = 0.33755380707374505\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060510356857562154\n",
+      "RMSE Testing = 0.060478638430547904\n",
+      "Out of Bag error = 0.3402508808964576\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060359102304168244\n",
+      "RMSE Testing = 0.060260581846648634\n",
+      "Out of Bag error = 0.33855963191988886\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06072398954345293\n",
+      "RMSE Testing = 0.06062499305811937\n",
+      "Out of Bag error = 0.34150461427058093\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06023892748963681\n",
+      "RMSE Testing = 0.06014636951763388\n",
+      "Out of Bag error = 0.33719419036890647\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06049680098602008\n",
+      "RMSE Testing = 0.06037689130400801\n",
+      "Out of Bag error = 0.3399067148516989\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06080526074608326\n",
+      "RMSE Testing = 0.06074795066583531\n",
+      "Out of Bag error = 0.3424588040245041\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060690133842309944\n",
+      "RMSE Testing = 0.060572647044541725\n",
+      "Out of Bag error = 0.3414950141553378\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060610737883301236\n",
+      "RMSE Testing = 0.06053243159304309\n",
+      "Out of Bag error = 0.34026317626356584\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06051503720007836\n",
+      "RMSE Testing = 0.06039516344219642\n",
+      "Out of Bag error = 0.3395300723746448\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06054966284189127\n",
+      "RMSE Testing = 0.060457920224559904\n",
+      "Out of Bag error = 0.3404886185154773\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06076038934426713\n",
+      "RMSE Testing = 0.06067784818631734\n",
+      "Out of Bag error = 0.3417390466746008\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06049379008887886\n",
+      "RMSE Testing = 0.06038507416549031\n",
+      "Out of Bag error = 0.33926768855315753\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060459232578001974\n",
+      "RMSE Testing = 0.06035721448693619\n",
+      "Out of Bag error = 0.3383707639832075\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 32\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06063005199605759\n",
+      "RMSE Testing = 0.060555147907721076\n",
+      "Out of Bag error = 0.3403914238330229\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 33\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060555768286412195\n",
+      "RMSE Testing = 0.060450489094746485\n",
+      "Out of Bag error = 0.33995809286091905\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 34\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06055503253765725\n",
+      "RMSE Testing = 0.06049581738454146\n",
+      "Out of Bag error = 0.33986268179169177\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 35\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06044816131608165\n",
+      "RMSE Testing = 0.06036873160122502\n",
+      "Out of Bag error = 0.33873361188652523\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 36\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060723759496034854\n",
+      "RMSE Testing = 0.06062345125046305\n",
+      "Out of Bag error = 0.34134184635704645\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 37\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06059503347082114\n",
+      "RMSE Testing = 0.060511513959132526\n",
+      "Out of Bag error = 0.34018314246135645\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 38\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0605516297917527\n",
+      "RMSE Testing = 0.060420893165421194\n",
+      "Out of Bag error = 0.3399379877720743\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 39\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0603295444220258\n",
+      "RMSE Testing = 0.06027417224988659\n",
+      "Out of Bag error = 0.33758444208330796\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 40\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06039145661824165\n",
+      "RMSE Testing = 0.06030887407814689\n",
+      "Out of Bag error = 0.33801194236369403\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 41\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06073214013651117\n",
+      "RMSE Testing = 0.06061099737320362\n",
+      "Out of Bag error = 0.341268852698809\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 42\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060428431177237875\n",
+      "RMSE Testing = 0.060346047933824656\n",
+      "Out of Bag error = 0.33813919766261397\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 43\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06057858598049861\n",
+      "RMSE Testing = 0.06047870784602997\n",
+      "Out of Bag error = 0.339815311453847\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 44\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0605189768313531\n",
+      "RMSE Testing = 0.060376577153751776\n",
+      "Out of Bag error = 0.33947036820372956\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 45\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06063391258566316\n",
+      "RMSE Testing = 0.06053614495425401\n",
+      "Out of Bag error = 0.34037771201938255\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 46\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06060801923294821\n",
+      "RMSE Testing = 0.06051020737324897\n",
+      "Out of Bag error = 0.34001838433690956\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 47\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060546854226931915\n",
+      "RMSE Testing = 0.06046569792440232\n",
+      "Out of Bag error = 0.3395360071596207\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 48\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060477422763570876\n",
+      "RMSE Testing = 0.0603601450128113\n",
+      "Out of Bag error = 0.3388421195510132\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 49\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06047082505421094\n",
+      "RMSE Testing = 0.06036841138483988\n",
+      "Out of Bag error = 0.33865739571052955\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 50\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06053067699893759\n",
+      "RMSE Testing = 0.060441975399971724\n",
+      "Out of Bag error = 0.3392701836060493\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 51\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060455961298320614\n",
+      "RMSE Testing = 0.06031735897143744\n",
+      "Out of Bag error = 0.3386378991949061\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 52\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060468921745249794\n",
+      "RMSE Testing = 0.060355525120081556\n",
+      "Out of Bag error = 0.3386073575036922\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 53\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06045247392808719\n",
+      "RMSE Testing = 0.06035175756960831\n",
+      "Out of Bag error = 0.33864291053355167\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 54\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06055179341471526\n",
+      "RMSE Testing = 0.060411713086356136\n",
+      "Out of Bag error = 0.3396126413182988\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 55\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06046340199885832\n",
+      "RMSE Testing = 0.06036526094172319\n",
+      "Out of Bag error = 0.3387249847001773\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 56\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06046611245591035\n",
+      "RMSE Testing = 0.060378683113944044\n",
+      "Out of Bag error = 0.3387983761411312\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 57\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06034031533295242\n",
+      "RMSE Testing = 0.060259715455692156\n",
+      "Out of Bag error = 0.3375305983363586\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 58\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06060618194930698\n",
+      "RMSE Testing = 0.06051260978083767\n",
+      "Out of Bag error = 0.34010628814728133\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 59\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06046600745556548\n",
+      "RMSE Testing = 0.06039513657239054\n",
+      "Out of Bag error = 0.33872651451725544\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 60\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060538336617214875\n",
+      "RMSE Testing = 0.06044030235339774\n",
+      "Out of Bag error = 0.3392735879852906\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 61\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06060200349036845\n",
+      "RMSE Testing = 0.06052377667364513\n",
+      "Out of Bag error = 0.3400636181435182\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -2919,7 +11637,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2931,7 +11649,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2943,7 +11661,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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bSm5O1Mrmb6jh96+vornNkoMxxsSSsCukReQJ3I2/i0WkEvhf3H1iUdU/4YZAPht345oG3J2/EibD5/Jgq5UcjDEmpoQlB+++uNFeV+C6RK0/VHtyaLGSgzHGxNQjGqT3h8x0LzlYycEYY2LqPcmho1rJ7l9hjDGx9JrkYNVKxhgTv16THNqrlaxB2hhjYus1ySHDJ4C1ORhjTDx6TXLItGolY4yJW+9JDlatZIwxces1ycEugjPGmPj1uuRg1UrGGBNbr0kOn14EZ9c5GGNMLL0nObRXK1nJwRhjYuo1ySEj3bqyGmNMvHpPcrAGaWOMiVuvSQ4dbQ5WrWSMMTH1nuTgs1FZjTEmXr0mOXRUK7VZbyVjjIml1yQHX5rgSxNrczDGmDj0muQAbvA9q1YyxpjYellySLMGaWOMiUOvSg5Z6WlWrWSMMXHoVcnBSg7GGBOfhCYHETlTRJaLyCoRuSnM6yNE5DURWSgib4rI0ETGk+GzkoMxxsQjYclBRHzA/cBZwCTgUhGZFDLb3cAjqnoYcBtwe6LiAXchXKsNvGeMMTElsuQwBVilqmtUtQV4EpgWMs8k4HXv8RthXt+vMnxpNFu1kjHGxJTI5DAEqAh6XulNC/YxcIH3+HygQET6JyqgTJ9d52CMMfFIdYP094ETRWQ+cCKwEfCHziQi14pIuYiUV1VV7fXKMq23kjHGxCWRyWEjMCzo+VBvWgdV3aSqF6jqkcCPvWk7QxekqjNUtUxVy0pKSvY6IOutZIwx8UlkcpgHjBORUSKSCVwCzAyeQUSKRaQ9hpuBBxMYj/VWMsaYOCUsOahqGzAdmA0sBZ5S1cUicpuITPVmOwlYLiIrgIHALxMVD7hqJbtNqDHGxJaeyIWr6ixgVsi0W4IePw08ncgYgmX60mhp69SkYYwxJkSqG6STKsMndp2DMcbEoVclB+utZIwx8elVycF6KxljTHx6X3KwkoMxxsTUq5KDDdltjDHx6VXJwaqVjDEmPr0uOQQU/AHrsWSMMdH0quSQme4+rlUtGWNMdL0qOWT4BMCG7TbGmBh6VXKwkoMxxsSndyUHnyUHY4yJR69KDhlecrAeS8YYE13vSg5WrWSMMXHpVckhs6PkYF1ZjTEmmt6VHNJdbyUbQsMYY6LrVckhwxqkjTEmLr0qOXT0VrIGaWOMiapXJYf2BmmrVjLGmOh6VXLItK6sxhgTl96VHDq6slpvJWOMiaZXJQdrkDbGmPgkNDmIyJkislxEVonITWFeHy4ib4jIfBFZKCJnJzKe9oH3rFrJGGOiS1hyEBEfcD9wFjAJuFREJoXM9hPgKVU9ErgE+EOi4oFPq5WsQdoYY6JLZMlhCrBKVdeoagvwJDAtZB4FCr3HRcCmBMZjA+8ZY0ycEpkchgAVQc8rvWnBbgW+LCKVwCzg+nALEpFrRaRcRMqrqqr2OiAbeM8YY+KT6gbpS4GHVHUocDbwqIh0iklVZ6hqmaqWlZSU7PXK7H4OxhgTn0Qmh43AsKDnQ71pwa4CngJQ1feAbKA4UQGlp7WPrWRdWY0xJpqoyUFEfCLyxl4uex4wTkRGiUgmrsF5Zsg8G4BTvXUdhEsOe19vFIOIkOlLs2olY4yJIWpyUFU/EBCRoq4uWFXbgOnAbGAprlfSYhG5TUSmerN9D7hGRD4GngC+oqoJPa3P8IlVKxljTAzpccxTBywSkVeA+vaJqvqtWG9U1Vm4hubgabcEPV4CfC7uaPeDzPQ0mtv8yVylMcb0OPEkh2e8vwNCVrrPqpWMMSaGmMlBVR/22gzGe5OWq2prYsNKnOyMNJotORhjTFQxk4OInAQ8DKwDBBgmIleq6pzEhpYYWek+mlstORhjTDTxVCvdA3xeVZcDiMh4XOPxUYkMLFGyMqzNwRhjYonnOoeM9sQAoKorgIzEhZRYWelpNFnJwRhjooqn5FAuIn8B/u49vwwoT1xIiZWV7qOhpS3VYRhjTLcWT3L4JnAd0N519W0SPHpqImWlp1HTYCUHY4yJJmpy8IbdflBVLwPuTU5IiZVlvZWMMSameK6QHuF1ZT0gZKf7rEHaGGNiiKdaaQ3wrojMZM8rpHtkSSIrI826shpjTAzxJIfV3l8aUJDYcBIvK91n1UrGGBNDPG0OBar6/STFk3CuK6tVKxljTDTxtDkkdWC8RMtKdw3SCR781RhjerR4qpUWeO0N/2TPNoceORhfVoYPgBZ/gKx0X4qjMcaY7ime5JAN7ABOCZqm9NCRWrO8W4U2t1lyMMaYSOIZlfWryQgkWdpLDs2tAZf2jDHGdBJzbCURGS8ir4nIJ97zw0TkJ4kPLTE+LTlYo7QxxkQSz8B7DwA3A60AqroQdz/oHim4WskYY0x48SSHXFX9IGRajx25rr2dwbqzGmNMZPEkh+0iMgbXCI2IfBHYnNCoEigrw0oOxhgTSzy9la4DZgATRWQjsBY3bHeP1FGtZENoGGNMRPH0VloDnCYieUCaqtbGu3ARORP4LeAD/qKqd4S8/hvgZO9pLjBAVfvEu/y90V6tZA3SxhgTWTwlBwBUtT72XJ/yht64HzgdqATmichMVV0StMzvBM1/PXBkV9axN7KtWskYY2KKp81hb00BVqnqGlVtAZ4EpkWZ/1LcvakT6tOSgyUHY4yJJJHJYQhQEfS80pvWiYiMAEYBr0d4/VoRKReR8qqqqn0K6tM2B6tWMsaYSGJWK4nIBWEm7wIWqeq2/RTHJcDT3kB/najqDFyjOGVlZfs0Yl57b6UmKzkYY0xE8bQ5XAV8BnjDe34S8CEwSkRuU9VHI7xvIzAs6PlQb1o4l+B6RSVcR7WSlRyMMSaieJJDOnCQqm4FEJGBwCPAMcAcIFJymAeME5FRuKRwCfCl0JlEZCLQF3ivy9HvBbtC2hhjYounzWFYe2LwbPOmVeMNqRGOqrYB04HZwFLgKVVdLCK3icjUoFkvAZ7UJN1gwZKDMcbEFk/J4U0ReR53PweAC71pecDOaG9U1VnArJBpt4Q8vzXuaPcDEfFu+GPVSsYYE0m8V0hfyKd3hHsE+Jd3pn9yxHd1Y1npaXaFtDHGRBHPFdIKPO39HRCyMnxWrWSMMVHEcz+HY0VknojUiUiLiPhFZHcygksUV3KwaiVjjIkkngbp/8NdvbwSyAGuxg2L0WO5NgcrORhjTCRxXSGtqqsAn6r6VfVvwJmJDSuxstJ91iBtjDFRxNMg3SAimcACEfk17l4OiRx2I+GyMqzkYIwx0cRzkL/cm286UI+76vnCRAaVaNnpPuutZIwxUcTTW2m997AJ+Fliw0mOrIw0aupbUh2GMcZ0WxFLDiIyTkQeEpF7RWSoiLzo9Vj6WESOTmaQ+5s1SBtjTHTRqpX+BvwX2AS8DzwIFAPfx/Vg6rGy0n00WVdWY4yJKFpyyFfVGap6N9Coqv9U1SZVfQXISlJ8CWElB2OMiS5acgg+eoZe9Najj6zWW8kYY6KL1iA9UUQWAgKM8R7jPR+d8MgSKCvdZ1dIG2NMFNGSw0FJiyLJsq3kYIwxUUVMDkFdWA84Wek+2gJKmz9Auq9HX89njDEJ0SuPjHbDH2OMic6SgzHGmE6iXQT3mvf/zuSFkxzZGT4AGq1R2hhjworWIF0qIp8FporIk7heSh1U9aOERpZA+dnuY9c3t6U4EmOM6Z6iJYdbgJ8CQ4F7Q15T4JREBZVoeVnuY9c2WXIwxphwovVWehp4WkR+qqo/T2JMCVeQZSUHY4yJJmaDtKr+XESmisjd3t8X4l24iJwpIstFZJWI3BRhnotFZImILBaRx7sS/N5qr1aqs+RgjDFhxRyyW0RuB6YAj3mTbhCRz6rqj2K8z4e7nejpQCUwT0RmquqSoHnGATcDn1PVGhEZsJefo0vyMi05GGNMNPHcCe4c4AhVDQCIyMPAfCBqcsAllFWqusZ735PANGBJ0DzXAPerag2Aqm7rWvh7p6C95GBtDsYYE1a81zn0CXpcFOd7hgAVQc8rvWnBxgPjReRdEZkrImHvTS0i14pIuYiUV1VVxbn6yNobpK3kYIwx4cVTcrgdmC8ib+C6s54AhG0/2Mv1jwNOwvWKmiMih6rqzuCZVHUGMAOgrKxM93WlGb40stLTrEHaGGMiiOc2oU+IyJtA+93ffqiqW+JY9kbc/abbDfWmBasE3lfVVmCtiKzAJYt5cSx/nxRkp1NrycEYY8KKq1pJVTer6kzvL57EAO4AP05ERolIJnAJMDNknmdxpQZEpBhXzbQmzuXvk7ysdCs5GGNMBAkbW0lV24DpwGxgKfCUqi4WkdtEZKo322xgh4gsAd4AblTVHYmKKVh+Vro1SBtjTATxtDnsNVWdBcwKmXZL0GMFvuv9JVVellUrGWNMJDFLDiLyaDzTepoCq1YyxpiI4qlWOjj4iXdx21GJCSd58rPTrSurMcZEEG3I7ptFpBY4TER2e3+1wDbguaRFmCDWIG2MMZFFTA6qeruqFgB3qWqh91egqv1V9eYkxpgQBVnpNiqrMcZEEE+D9IsickLoRFWdk4B4kiYvK53mtgCt/gAZdh9pY4zZQzzJ4cagx9m4MZM+pAffzwFcV1Zww3b3yc1McTTGGNO9xHOF9LnBz0VkGHBfwiJKkvZhu2ubLDkYY0yovalPqQQO2t+BJFtHyaHF2h2MMSZUPPdz+D3utqDgkskRQI+9f3S79uRgV0kbY0xn8bQ5lAc9bgOeUNV3ExRP0nTcR9q6sxpjTCfxJId/AGO9x6tUtSmB8SRN+w1/7FoHY4zpLNpFcOki8mtcG8PDwCNAhYj8WkQykhVgoli1kjHGRBatQfouoB8wSlWPUtXJwBjcXeHuTkZwiWR3gzPGmMiiJYcvANeoam37BFXdDXwTODvRgSVaviUHY4yJKFpyUG9I7dCJfj7tvdRj+dKEnAyfVSsZY0wY0ZLDEhG5InSiiHwZWJa4kJLHRmY1xpjwovVWug54RkS+hhsuA6AMyAHOT3RgydA/L5Oq2uZUh2GMMd1OxOSgqhuBY0TkFD69p8MsVX0tKZElwdC+uWyork91GMYY0+3EM7bS68DrSYgl6Yb1y+HdVdtRVUQk1eEYY0y30avHqh7WN5fGVj876ltSHYoxxnQrvTs59MsFoKK6IcWRGGNM95LQ5CAiZ4rIchFZJSI3hXn9KyJSJSILvL+rExlPqGH9cgCoqGlM5mqNMabbi2dspb0iIj7gfuB03BAc80RkpqouCZn1H6o6PVFxRDOsr5UcjDEmnESWHKbgBupbo6otwJPAtASur8vystLpl5dJZY0lB2OMCZbI5DAEqAh6XulNC3WhiCwUkae9u8x1IiLXiki5iJRXVVXt1yCH9c2hotqqlYwxJliqG6T/A4xU1cOAV3Cjv3aiqjNUtUxVy0pKSvZrAEP75VJhJQdjjNlDIpPDRiC4JDDUm9ZBVXeoavslyn8BjkpgPGEN65vLpp2N+AM9frgoY4zZbxKZHOYB40RklIhkApcAM4NnEJHSoKdTgaUJjCesYf1yaPUrW3YfEPcwMsaY/SJhvZVUtU1EpgOzAR/woKouFpHbgHJVnQl8S0Sm4m4/Wg18JVHxRBLcY2lIn5xkr94YY7qlhCUHAFWdBcwKmXZL0OObgZsTGUMswRfCHTu6fypDMcaYbiPVDdIpN7hPNiJ2IZwxxgTr9ckhK91HaWE2lXYhnDHGdOj1yQGsO6sxxoSy5IBrlLYL4Ywx5lOWHHDdWbfWNtHc5k91KMYY0y1YcsCVHFRhozVKG2MMYMkBCOrOasnBGGMASw5A0H0drMeSMcYAlhwAGFiQTaYvzXosGWOMx5IDkJYmDOmbYyUHY4zxWHLwjCnJY+XWulSHYYwx3YIlB8+EQQWs2V5v3VmNMQZLDh0mDCrEH1BWb6tPdSjGGJNylhw8EwcVALB86+4UR2KMMalnycEzqjiPDJ+wbEttqkMxxpiUs+TgyfClMaYkn+WWHIwxxpJDsImDCiw5GGMMlhz2MGFQIZt3NbGrsTXVoRhjTEpZcggysdQ1Si/etCvFkRhjTGpZcggyeVhfRKB8XU2qQzHGmJSy5BCkKDeDiYMK+WBtdapDMcaYlEpochCRM0VkuYisEpGbosx3oYirLk/wAAAZaklEQVSoiJQlMp54HDOqHx+ur6HVH0h1KMYYkzIJSw4i4gPuB84CJgGXisikMPMVADcA7ycqlq6YMqofja1+Ptlo7Q7GmN4rkSWHKcAqVV2jqi3Ak8C0MPP9HLgTaEpgLHE7emQ/AKtaMsb0aolMDkOAiqDnld60DiIyGRimqi9EW5CIXCsi5SJSXlVVtf8jDVJSkMXokjzet+RgjOnFUtYgLSJpwL3A92LNq6ozVLVMVctKSkoSHttnRvfng7XV1u5gjOm1EpkcNgLDgp4P9aa1KwAOAd4UkXXAscDM7tAofdzYYuqa21hYuTPVoRhjTEokMjnMA8aJyCgRyQQuAWa2v6iqu1S1WFVHqupIYC4wVVXLExhTXD4zpj8i8PbK7akOxRhjUiJhyUFV24DpwGxgKfCUqi4WkdtEZGqi1rs/9MnN5NAhRby7ypKDMaZ3Sk/kwlV1FjArZNotEeY9KZGxdNVxY4uZMWcNdc1t5GcldDMZY0y3Y1dIR3DShAG0BZQzfjOH5xduSnU4xhiTVJYcIpgyqh9/vvwocjN93PLcYgIBTXVIxhiTNJYcojjj4EF8/cQxVNe3sGxLLY+/v4EfPr0QVeW1pVu55+XlqQ7RGGMSwirTY/jc2P4AvL2yigffXcvW3c0M6ZvDA3PWUNfSxjUnjKYwOyPFURpjzP5lJYcYSotyGF2cx4w5a9i6u5n+eZnc+8oKapvbUIWPK+xaCGPMgceSQxw+O7Y/O+pb6JObwWPXHENJQRY/OecgROCj9TupqW9h3jobbsMYc+Cw5BCHz40pBmDq4YOZOKiQ928+lauPH834AQXMr6jhZ/9ZzMV/fi9iKeLPb63m5cVbOp43t/mZt64aVWvkNsZ0T5Yc4nDC+BKmHj6Yq44bBUBamgBw5PA+lK+r4YVFm1GFnzz7Cf6A0tzm5/mFm1i/o55PNu7i9heX8e1/LKCypoGFlTs59/fvcNGf3uP5hZtT+bGMMSYi6Wlnr2VlZVpenvIRNgB4al4FP/jXQkTgu6eN555XVjC6JI/apjaqapsZ0ieHMQPy+Wh9DQFVBhRkUVHTSEl+Fr40oTAng0evmsJf3l5LaVE2p08ayOA+OXuso9UfoL65jT65mQDUNrVy9+zlnHv4YMq84cVj+e2rK2n1B/j+GRP2+zYwxvQMIvKhqsY9dp31VtoHk0f0AeDkCQOYfspY0n1pzN9QQ5oIx48v5mf/WcLGnY1cd/IYivOz+Nl/lnBx2VB+fM4kXl68hRufXshZv32bqtpmAO57dQXPXvc5RvTPA2BbbRNfe2ge67c38MS1x1KUk8E1j5SzbEst5etreP764xCRqDE2tfqZMWc1ja1+LjxqKKOK89i2u4nbX1zG108czcRBhft1m6gqSzbvZuyAfAA+XFfDlFH9SPfFV0idu2YHI/vnMagoe7/GZYzpGksO+2B0cT7fOHEM5x85BBHhmyeN2eP1wuwM/vrOWr72uVH0z8/inMNKGVDgDnrTjhjCb15ZQXVDC49ffQx98zK59IG5fPWheTz0lSlUN7Qw/fGP2FHXQmFOOl/+6/s0tvhJTxMuOmoo//ywkvdW7+CzY4v3WKc/oHywtpoN1fUcPqwPm3c2Ud/iB+APb6zi5+cdwrWPfsiCip18XLmT568/jpwMHyKCqvLCos0cMawPQ/vmUlXbzIKKnbT5A5x1aCkADS1t5Gam8+bybcxcsIn/PfdginIz2LyrkUGF2Tw6dz23PLeY4vxMfGnC1t3NfOe08dxw2riw27CiuoFfvLCEcQMKyPCl8ZtXXelr5vTjmL+hhvdW76ClLcD1p46jKGfPLsPb65opzs/aI654qWrMxBpNmz9A+foaDiot7BRXqqgqO+pbOrZJb1Tb1ErBfuxa3tTqJzvDt9+Wty/2dZ/tKqtWSqE1VXUEVBk7oACA99fs4IoHP6AtoAgwsDCbP1w2mbwsH1c+OI8jh/fhprMmUpyfxXF3vsHwfjlMPXwwxQVZjOyfR3Obn9tnLaN8fQ0AgwqzKRvZlzeXVzHtiME8Oa+CgQVZbNrVxNdPGM2Mt9cwuCiHmoYWrjpuFEU5GfzihaUU5WRw1iGDeOajjbR497T4w2WTWbu9nrtmL2dUcR5rt9cD8PUTRjOyOI+bn1nESRNK+O+qHUwe0YfC7Axa/QEaWtwtV9/6wclkpKVRubMBf0ApLcrh+YWbuPeVFbT6AzS1uvUcN7aY/67eTmlRDht3NpKeJgRUOXpkPx7+2hSyM3y8tnQr976ygsWbdvPVz41kSJ8cbn9xGReXDeXWqQeTlb7nj3lR5S7mravmq58biYiwva6ZC/7wX/Ky0vnCYaVU1TZz7Oh+nHlIacd7tuxq4sf/XkRAlZ+fdwhD++bS0NLGvS+7hF6+roYN1Q30yc3gh2dO5NIpw7v8/b+9sorfvbaSLx0znPOOcCcYLW0B6prbyM304Q8oCmSnp5HuS2NHXTPzN+zkqBF96ZuX2Wl5v311Jfe9toK7v3g4EwYV8PzCzZwycQBHj+zb6aCyu6mVyupGmtr8LKrcRVVtM4U56RTlZDB2QAFHjegbNuZAQNnZ2IpPhKJcdxBubvOzpqqefnmZDCzMptUfIE0EX5pQ29SKQsf+UNPQQnFeFttqm6lrbmN0cV5HGx7A6qo63lxexRWfGUFGSGmzqdXPj/69iDa/Mv2UsYwfWLDH93XX7OU8M7+Sa48fzU1nTURE2LizkYrqBo4d3b/L38+z8zfyg6cXcseFh3LB5KEd06vrW1hYuZMpo/p16YQknCWbdhNQ5ZAhRVHn27q7iesfn88Pz5oY8buJpavVSpYcupmtu5t48J21NLcF+M7p4yOelf7prdXc8eKyTtMLstP56TmTKC7I5KqHy1GFcw8fzE/POYgbn15IfnY6n580kGlHDOFPb63mlSVbKchO583l7g57J00oYevuZpZt2c1FRw3l4rJh3Pb8EtZW1VPX0saxo/qTmZ7GIUMKqahu5KXFW0hPE0qLsqmoaaRvbgazvnU8/b2z19VVdXz+N3MoLcpm085GQkchOXZ0P+764uHUNbexeNNuLjhyCH99Zy13vbycb50ylmtOGM1Ln2zhhicXcM6hpXzv8+M5+3dvM7hPDocMLmLmx27cq4NKC1m6eTd9cjPI8KWhqgwqymbq4YO579WVNLT4ufGMCVx7wmgu/+v7zN+wkxH9c1mxtY4Mn9DqVy45ehh98zJZva2O99bsoM2vpAmICH++/CheXbqVv727jiF9chhUlM3/lA3jmfmVzF1Tza/OP5SSgizmratmUmkhJ08cQFFOBv6A8rvXVvLQf9cB0Cc3g8FFOfTNy2D24q1k+tJobPUzqDCb0j7ZLNtcS2Orv9P3mulLozUQQBWG98vlgSvKKM7PpF9eJiLCiq21nPO7t8nwpdHU6kdE8HsbOzfTR06Gj9rmNgqz0xnRP4+FlTtp9X/6ZaQJe3w3J00oYUxJPhuqG1i2ZTdNrYGOxOAPKOlpwj0XH872uhbufHEZLf4AIjCptJA1VfXkZfk4ecIAXvxkC/6AcsVnRzBr0WYqqhvxpX0aW0F2Ol84bDAXlw2lvtnP9U98RE1DK6dMHMAvzz8EgMUbd7NldxPPzt/IhxtqyMnw0dDiZ9yAfMpG9iM308fj72/AH1COHN6H99dW88WjhnLi+BL+d+ZiqutbuPGMCfy/k8bgDyjPzN/I7E+2sL2+hb9cUcbcNTv49exlXHDkUA4eXEhtUxt98zK47rH5tAXcScsvzz+UkvwsHp27njeXbyOgMG5APrdfcCjD++eyaWcTLy/ewr/nb2TioALOO3IIbX6lvqWN2ib3t2pbHVV1zXx+0kAuKhvKroZWpt3/Lg0tfk6dOIATxpfQ0OJnYeVOinIyKMxxCTUnw8fTH1ZS39zG7y49klMPGhj1GBKJJYdeIhBQtuxuIis9jS27m6iobgRcO0h71dVPn/2ER+eu53eXHsnUwwdHXJaqctfs5XxcuZM/X15Ghk/YXtfCEK9xfOXWWs75/TuMLcnnX9/8LDmZ7sx8485GTr77TbJ8abz0nRMQIE2kU3vBHS8uY9aizZx7eCmHDC5CBNbvaKBsZL+IZ0Gt/sAeZ44z5qzmV7OWUZidjiq88t0TGVSUzT/LK6hpaOHq40bz2rJtvLZ0KyJCmrj2i9VV9YwdkM+4Afm8tHgLfXIyqGlo5d6LD+f8I4dQXd9CQXYGv3hhCY+8t570NGF4/1wOG1LEDaeNJz1NuPrhctZX19PcFuDyY0dw27RDOuJq8we4+pHyjuTafpAtzE7nnMNKWVCxi6Wbd7vOBkXZVDe0smlnI5t3NlI2sh+/PP8QXlmylTkrqti0q4lJpYWM6J9LQ4ufDJ87o25qDdDY6icv08eo4nx++twnVNe3AHDyhBK+9/kJ/ODphWze1cjM6cfx8+eX0Dc3k++cPp63Vmxj5dY6Glr9FGSls6O+hdVVdZSN6Mvk4X3J8KUxsbSAIX1yqGtuY1djK7MWbeb/Xl9FW0ApLcrmoNJCCrIzEIF+uZn0z8/kpU+2dNxK99SJA5h25BCXVFfv4KDSAipqGnlj+TY+P2kgLW0B3lhexbgB+fzP0cOoqmtmcFEOORk+5q7dwfMLN9PS5g7Cw/rlcNFRw/jNqysIPTRlZ6Rxz0VH8Nkx/Xn6w0rmrKxiYeUudjW2cs6hpdx01kSG9s3hjpeW8de319IWUEYV53FQaQGzFm1h/MB8/AFldVU9w/vlsq22ifEDC1i5tY68rHS21zXvsb7+eZk8ee2xXPf4R6zYWtcx7ZIpwxg7IJ9fvrCU7XUtHfOLwPHjSliyadce04GO/aogO4OPK3aSm+mjMDuDtkCAS6cM54kPKjrWP6J/LvXNbdQ3u2rkhlY/I/rl8scvH8WEQQXsLUsOpkN9cxv/+qiSS6cM71RE76rVVXWUFGR1GirkjeXbKMhKj7vn1N5SVX7+/FIefHctd154KP9zdOxqnDZ/gLdWVDF5eF9yMn3c+PRCMn1pnHnIIE6f1Pnsq7apldzMdHxpe1bBbKtt4pI/z6XFH+Clb5/QaQj3+uY2bp25mMOGFnFR2TCWbN7N/a+v4r+rdzBhUAGXHzuCC48ayv5SUd3A7MVb2NXYyh/fXE1bQMnPSue+/zmC08J8rr0Rq367scXPj59dxNC+uXz71HF7VA218wcUX5p0dFIYP7Ag7H64va6Z8nXVNLUGOH5cMf3zs/hoQw1LN+8mEFAOKi1keL9cCnMyOtX/qyqNrf5O1TvV3oWpx4zqR2F2Bo99sIEXF21md1Mr3zplHKdPGsizCzbynX98THF+FrO+dRwNLX52N7WSm+lj6eZaxg8sYMKgAppa/SzfUktNQwvHjOrfcXK0o66ZuWuq2VHfzMDCbA4bWkRpUQ5NrX7Wbq8nN9NHflY6eVnpZKWndWzPNVV13PPyCl5btpW/fWUKnxnTH1Vl6+5m0n3Sqc2o/Ri9r+0NlhzMASsQUNbuqGdMSX7S193U6qe5LdClxudkNCC+v2YHL36yhW+cOMZ6eO2FmR9vYvzA/P3eay8ebf5A3L349gdLDsYYYzrpanKwK6SNMcZ0YsnBGGNMJ5YcjDHGdGLJwRhjTCcJTQ4icqaILBeRVSJyU5jXvyEii0RkgYi8IyKTEhmPMcaY+CQsOYiID7gfOAuYBFwa5uD/uKoeqqpHAL8G7k1UPMYYY+KXyJLDFGCVqq5R1RbgSWBa8AyqujvoaR7Qs/rVGmPMASqRo7IOASqCnlcCx4TOJCLXAd8FMoFTwi1IRK4FrgUYPrzrA5wZY4zpmpQP2a2q9wP3i8iXgJ8AV4aZZwYwA0BEqkRk/V6sqhjYvi+xJojF1TXdNS7ovrFZXF3TXeOCfYttRFdmTmRy2AgMC3o+1JsWyZPAH2MtVFVL9iYYESnvytWByWJxdU13jQu6b2wWV9d017ggubElss1hHjBOREaJSCZwCTAzeAYRCb4DzDnAygTGY4wxJk4JKzmoapuITAdmAz7gQVVdLCK3AeWqOhOYLiKnAa1ADWGqlIwxxiRfQtscVHUWMCtk2i1Bj29I5PpDzEjiurrC4uqa7hoXdN/YLK6u6a5xQRJj63GjshpjjEk8Gz7DGGNMJ5YcjDHGdHLAJ4dY4zslMY5hIvKGiCwRkcUicoM3/VYR2eiNL7VARM5OUXzrgsa5Kvem9RORV0Rkpfc//A2fExfThKDtskBEdovIt1OxzUTkQRHZJiKfBE0Lu33E+Z23zy0UkckpiO0uEVnmrf/fItLHmz5SRBqDtt2fkhxXxO9ORG72ttlyETkjyXH9IyimdSKywJuezO0V6RiRmv1MVQ/YP1wvqdXAaNwV2B8Dk1IUSykw2XtcAKzAjTl1K/D9brCt1gHFIdN+DdzkPb4JuDPF3+UW3IU8Sd9mwAnAZOCTWNsHOBt4ERDgWOD9FMT2eSDde3xnUGwjg+dLQVxhvzvvt/AxkAWM8n63vmTFFfL6PcAtKdhekY4RKdnPDvSSQ8zxnZJFVTer6kfe41pgKW6Ike5sGvCw9/hh4LwUxnIqsFpV9+bq+H2mqnOA6pDJkbbPNOARdeYCfUSkNJmxqerLqtrmPZ2Luwg1qSJss0imAU+qarOqrgVW4X6/SY1LRAS4GHgiEeuOJsoxIiX72YGeHMKN75TyA7KIjASOBN73Jk33ioUPJrvqJogCL4vIh+LGsgIYqKqbvcdbgIGpCQ1wF1EG/2C7wzaLtH262373NdwZZrtRIjJfRN4SkeNTEE+47667bLPjga2qGnxBbtK3V8gxIiX72YGeHLodEckH/gV8W92otH8ExgBHAJtxRdpUOE5VJ+OGWL9ORE4IflFdOTYl/Z7FXWE/FfinN6m7bLMOqdw+0YjIj4E24DFv0mZguKoeiRvw8nERKUxiSN3uuwtxKXuehCR9e4U5RnRI5n52oCeHro7vlFAikoH70h9T1WcAVHWrqvpVNQA8QIKK0rGo6kbv/zbg314cW9uLqd7/bamIDZewPlLVrV6M3WKbEXn7dIv9TkS+AnwBuMw7qOBV2+zwHn+Iq9sfn6yYonx3Kd9mIpIOXAD8o31asrdXuGMEKdrPDvTkEHN8p2Tx6jL/CixV1XuDpgfXEZ4PfBL63iTEliciBe2PcY2Zn+C2VfuQJlcCzyU7Ns8eZ3PdYZt5Im2fmcAVXm+SY4FdQdUCSSEiZwI/AKaqakPQ9BJxN+JCREYD44A1SYwr0nc3E7hERLJEZJQX1wfJistzGrBMVSvbJyRze0U6RpCq/SwZrfCp/MO16K/AZfwfpzCO43DFwYXAAu/vbOBRYJE3fSZQmoLYRuN6inwMLG7fTkB/4DXcgIivAv1SEFsesAMoCpqW9G2GS06bceOAVQJXRdo+uN4j93v73CKgLAWxrcLVR7fva3/y5r3Q+44XAB8B5yY5rojfHfBjb5stB85KZlze9IeAb4TMm8ztFekYkZL9zIbPMMYY08mBXq1kjDFmL1hyMMYY04klB2OMMZ1YcjDGGNOJJQdjjDGdWHIwPYaIqIjcE/T8+yJy635a9kMi8sX9sawY67lIRJaKyBtB0w4NGvWzWkTWeo9fTXQ8xkRiycH0JM3ABSJSnOpAgnlX1sbrKuAaVT25fYKqLlLVI1T1CFzf/xu956ftw3qM2SeWHExP0oa7h+53Ql8IPfMXkTrv/0negGnPicgaEblDRC4TkQ/E3b9iTNBiThORchFZISJf8N7vE3dvhHneYHFfD1ru2yIyE1gSJp5LveV/IiJ3etNuwV3o9FcRuSueDywip4nImyLyPO5CJ0TkSi/+BSLyBxFJ86afJSLvichH4u5PkOdNv0vcPQIWtsdiTCx2JmJ6mvuBhSLy6y6853DgINwwzWuAv6jqFHE3U7ke+LY330jcWD9jgDdEZCxwBW5YgqNFJAt4V0Re9uafDByibojpDiIyGHcPhaOAGtxot+ep6m0icgrufgblXYi/DHcfkg0icghu2InPqmqbiMzADTvxKm6s/1NVtcEbcO8GEfkr7irbg1VVxbvpjzGxWHIwPYqq7haRR4BvAY1xvm2eemPOiMhqoP3gvgg4OWi+p9QNCLdSRNYAE3HjTB0WVCopwo2v0wJ8EJoYPEcDb6pqlbfOx3A3mHk2znhDvaeqG7zHp3nLL3dD8ZCDGyajAXdjmP960zOBd3AJMQA8ICIvAM/vZQyml7HkYHqi+3Dj3PwtaFobXjWpV82SGfRac9DjQNDzAHv+BkLHklHc+DXXq+rs4BdE5CSgfu/C77Lg9QjwoKr+NCSe84GXVPXy0DeLSBlwOnAR8E1cwjMmKmtzMD2OqlYDT+Ead9utw1XjgLv3Q8ZeLPoiEUnz2iFG4waAmw18U9xQyojI+Pa6/Cg+AE4UkWJvRM9Lgbf2Ip5wXgUubm+UF5H+IjIc+K+3ztHe9DwRGSdutN1CVX0e11Zz5H6KwxzgrORgeqp7gOlBzx8AnhORj4GX2Luz+g24A3shbnTOJhH5C64t4iNvSOUqYtwuVVU3i8hNwBu4M/0XVHW/DHeuqotE5GfAq14JqdWLdZ6IXAX8Q9zw9AA/wlW9PeO1l6ThblhjTEw2KqsxxphOrFrJGGNMJ5YcjDHGdGLJwRhjTCeWHIwxxnRiycEYY0wnlhyMMcZ0YsnBGGNMJ/8fHXdKhl+IUioAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2955,7 +11673,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2967,7 +11685,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2979,7 +11697,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2991,7 +11709,7 @@
     },
     {
      "data": {
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\n",
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PaGoxsw3A/0d4oAfu/g1CXzXnEXoY7SF00SwiItNA2ZJDfIBJofFOeIj9lFi1bie/ebydD529lHTyoGhqERGpmBlzlLz/2V18/fY1DAyN94wOEREZNWOSQyoRPurgsJKDiEgxMyY5pJPhIWKDw+qFVkSkmBmUHFRyEBEp1YxJDqmYHIZUchARKWrGJIe91UojKjmIiBQzg5KDqpVEREo1Y5JDKhFKDqpWEhEpbsYkh3QqfNQBlRxERIqaOckhoQZpEZFSzZzksPc+B5UcRESKmTHJIaUGaRGRks2Y5JDRfQ4iIiWbMckhpWolEZGSzZjksO8mOJUcRESKmUHJIbY5qMtuEZGiZkxy2Nu3krrPEBEpasYkB3XZLSJSupmTHPSwHxGRks2Y5DB6tZIuZRURKW7GJIfRBmn1rSQiUtyMSw4qOYiIFFfW5GBm55jZ42a2xswuyzP+KDP7lZk9ZGZ3mNmCcsWSTBgJU5uDiEgpypYczCwJXA2cCywDLjKzZTmTfQm4wd1PBK4EPluueCBczqonwYmIFFfOksNpwBp3X+vuA8CNwAU50ywDfh1f355n/AGVSSZUrSQiUoJyJof5wPqs9xvisGwPAm+Mr/8caDCzObkLMrP3mVmbmbW1t7dPOqBU0lStJCJSgko3SH8MOMPM7gfOADYCw7kTufs17r7c3Ze3trZOemWpREI3wYmIlCBVxmVvBBZmvV8Qh+3l7puIJQczqwfe5O67yxVQRiUHEZGSlLPksApYamaLzSwDrABWZk9gZi1mNhrDJ4HryhgPqWSCISUHEZGiypYc3H0IuBS4FXgUuMndV5vZlWZ2fpzsTOBxM3sCOBz4TLnigdC/krrsFhEprpzVSrj7LcAtOcMuz3p9M3BzOWPIlk4m1GW3iEgJKt0gPaVSSWNIJQcRkaJmVHJIJxNqkBYRKcHMSg4JJQcRkVLMrOSQMt3nICJSghmVHFIJXcoqIlKKGZUcQpuDSg4iIsXMsOSgO6RFREoxo5JDKpnQpawiIiWYUckhnTQGdBOciEhRMys5JBIM6WE/IiJFzazkkDI97EdEpAQzKjmkEgkG1CAtIlLUjEoO6aRKDiIipZhhyUHdZ4iIlGJGJYfRS1ndVXoQESlkRiWHTNIAdJe0iEgRMyo5pJLh4+pyVhGRwmZUckjH5KCSg4hIYTMsOYxWK6nkICJSyIxKDqlErFZSyUFEpKAZlRxUchARKc0MSw6jbQ5KDiIihZQ1OZjZOWb2uJmtMbPL8ow/0sxuN7P7zewhMzuvnPGk916tpGolEZFCypYczCwJXA2cCywDLjKzZTmT/R1wk7ufDKwA/q1c8QCkYrWSuu0WESmsnCWH04A17r7W3QeAG4ELcqZxoDG+bgI2lTGevW0OKjmIiBRWzuQwH1if9X5DHJbtCuBtZrYBuAX4YL4Fmdn7zKzNzNra29snHZDaHERESlPpBumLgG+7+wLgPOA7ZjYmJne/xt2Xu/vy1tbWSa9s9FJWJQcRkcLKmRw2Aguz3i+Iw7K9G7gJwN3/AFQDLeUKKJNS30oiIqUoZ3JYBSw1s8VmliE0OK/MmeZZ4NUAZnYcITlMvt6oiH03wankICJSSMHkYGZJM7t9Mgt29yHgUuBW4FHCVUmrzexKMzs/TvZR4L1m9iDwA+CdXsb+tNW3kohIaVKFRrr7sJmNmFmTu++Z6MLd/RZCQ3P2sMuzXj8CvGyiy50s3SEtIlKagskh6gIeNrNfAt2jA939b8oWVZmoy24RkdKUkhx+HP8OentLDkOqVhIRKaRocnD362OD8rFx0OPuPljesMpjb5uDSg4iIgUVTQ5mdiZwPbAOMGChmV3i7neWN7QDb2/fSmqQFhEpqJRqpauA17r74wBmdizhyqIXlTOwckipQVpEpCSl3OeQHk0MAO7+BJAuX0jlk07oUlYRkVKUUnJoM7Nrge/G9xcDbeULqXx0KauISGlKSQ7vBz4AjF66+lvK3LV2uSQTsVdWJQcRkYIKJof4TIbr3P1i4MtTE1L5mBmZZIIBVSuJiBRUsM3B3YeBo+KlrIeEVNJUchARKaKUaqW1wF1mtpL975A+KEsS6WRCbQ4iIkWUkhyein8JoKG84ZRfVSpBvx4TKiJSUCltDg3u/rEpiqfsqtNJ+gaHKx2GiMi0Vkqbw5T1mjoVqtMJ+gZVchARKaSUaqUHYnvDj9i/zeGg7IyvOp2kb0glBxGRQkpJDtXADuCsrGHOQdpTa3VK1UoiIsWU0ivrX05FIFOlKp2gs2+o0mGIiExrRftWMrNjzexXZvan+P5EM/u78odWHmqQFhEprpSO974JfBIYBHD3h4AV5QyqnJQcRESKKyU51Lr7H3OGHbT1MtUpXa0kIlJMKclhu5kdTWiExsz+Athc1qjKSFcriYgUV8rVSh8ArgGeb2YbgacJ3XYXZWbnAF8FksC17v65nPH/Arwqvq0FDnP35hJjn5Rwn4OSg4hIIaVcrbQWONvM6oCEu3eWsuB4d/XVwGuADcAqM1vp7o9kLfv/ZE3/QeDkCcY/YaHNYQR3x8zKvToRkYNSKdVKALh7d6mJIToNWOPua919ALgRuKDA9BcRHj9aVtXpJID6VxIRKaDk5DAJ84H1We83xGFjmNlRwGLg12WMBwgd7wH0q1FaRGRc5UwOE7ECuDn25TSGmb3PzNrMrK29vf05rWi05KBGaRGR8RVtczCzN+YZvAd42N23FZh1I7Aw6/2COCyfFYSG77zc/RpCozjLly9/To9x25sc1CgtIjKuUq5WejfwEuD2+P5M4F5gsZld6e7fGWe+VcBSM1tMSAorgLfmTmRmzwdmAX+YWOiTU50OhSXd6yAiMr5SkkMKOM7dtwKY2eHADcCLgTuBvMnB3YfM7FLgVsKlrNe5+2ozuxJoc/eVcdIVwI3uPiUPdq5OqeQgIlJMKclh4WhiiLbFYTvNbLDQjO5+C3BLzrDLc95fUWKsB0RNRslBRKSYUpLDHWb2P4TnOQC8KQ6rA3aXLbIy2VutpEtZRUTGVeod0m9i3xPhbgD+M1YDvWrcuaapKlUriYgUVcod0g7cHP8OerpaSUSkuFKe53C6ma0ysy4zGzCzYTPrmIrgymG0Wkk3wYmIjK+Um+C+Tuja4kmgBngPoc+kg5JughMRKa6kO6TdfQ2QdPdhd/8WcE55wyofVSuJiBRXSoN0j5llgAfM7AuEZzlMl243Jqw6pZvgRESKKeUg//Y43aVAN6FLjDeVM6hySiUTpBKmkoOISAGlXK30THzZB/xDecOZGqPPdBARkfzGLTmY2VIz+7aZfdnMFpjZz+MVSw+a2alTGeSBVp1OqEFaRKSAQtVK3wJ+D2wC7gGuA1qAjxGuYDpoVaWSqlYSESmgUHKod/dr3P1LQK+7/8jd+9z9l0DVFMVXFtXphO5zEBEpoFByyD565t70dlAfWUObg0oOIiLjKdQg/Xwzewgw4Oj4mvh+SdkjK6PqdFJtDiIiBRRKDsdNWRRTrDqd0NVKIiIFjJscsi5hPeRUp5Ls6i74KAoRkRntoL3T+blQtZKISGEzMjlU6WolEZGCCt0E96v4//NTF87U0NVKIiKFFWqQnmtmLwXON7MbCVcp7eXu95U1sjKq1k1wIiIFFUoOlwN/DywAvpwzzoGzyhVUuYXuM1StJCIynkJXK90M3Gxmf+/u/ziFMZVddTrJ8IgzODxCOjkjm11ERAoqemR09380s/PN7Evx7/WlLtzMzjGzx81sjZldNs40bzGzR8xstZl9fyLBT9boo0JVtSQikl/RLrvN7LPAacD34qAPmdlL3f1TReZLEh4n+hpgA7DKzFa6+yNZ0ywFPgm8zN13mdlhk/wcE7LvaXAjNFRPxRpFRA4upTwJ7s+Ak9x9BMDMrgfuBwomB0JCWePua+N8NwIXAI9kTfNe4Gp33wXg7tsmFv7k1OhRoSIiBZVa4d6c9bqpxHnmA+uz3m+Iw7IdCxxrZneZ2d1mlvfZ1Gb2PjNrM7O29vb2Elc/vrqqkBO7B4ae87JERA5FpZQcPgvcb2a3Ey5nfSWQt/1gkutfCpxJuCrqTjN7gbvvzp7I3a8BrgFYvny5P9eV1mZCyaG7XyUHEZF8SnlM6A/M7A5g9Olvf+vuW0pY9kbC86ZHLYjDsm0A7nH3QeBpM3uCkCxWlbD8SRstOfSo5CAikldJ1UruvtndV8a/UhIDhAP8UjNbbGYZYAWwMmea/yaUGjCzFkI109oSlz9pKjmIiBRWtov83X0IuBS4FXgUuMndV5vZlWZ2fpzsVmCHmT0C3A583N13lCumUXWZUHLoHVTJQUQkn1LaHCbN3W8BbskZdnnWawc+Ev+mTG2VSg4iIoUULTmY2XdKGXYwqc2ozUFEpJBSqpWOz34Tb257UXnCmRqj9zmo5CAikl+hLrs/aWadwIlm1hH/OoFtwE+mLMIySCaMmnRSJQcRkXGMmxzc/bPu3gB80d0b41+Du89x909OYYxlUVeVpHtAJQcRkXxKaZD+uZm9Mnegu99ZhnimTG0mRa+Sg4hIXqUkh49nva4m9Jl0Lwfx8xwg3OvQ3a9qJRGRfEq5Q/oN2e/NbCHwlbJFNEVqM0l6VHIQEclrMjfBbQCOO9CBTLW6qpQ63hMRGUcpz3P4GuGxoBCSyUnAQfv86FG1mSTbOvorHYaIyLRUSptDW9brIeAH7n5XmeKZMnUZlRxERMZTSnL4IXBMfL3G3fvKGM+Uqa1K6molEZFxFLoJLmVmXyC0MVwP3ACsN7MvmFl6qgIsF5UcRETGV6hB+ovAbGCxu7/I3U8BjiY8Fe5LUxFcOdVmUvQNjjA88pyfHSQicsgplBxeD7zX3TtHB7h7B/B+4LxyB1Zuo890UBcaIiJjFUoOHrvUzh04zL6rlw5ao912614HEZGxCiWHR8zsHbkDzextwGPlC2lqjD7wR3dJi4iMVehqpQ8APzazdxG6ywBYDtQAf17uwMptX7WSSg4iIrnGTQ7uvhF4sZmdxb5nOtzi7r+aksjKrK5q9IE/Sg4iIrlK6Vvp18CvpyCWKTVactDlrCIiY02mb6VDwt5HheppcCIiY8zg5KCSg4jIeGZsctjb5qCrlURExihrcjCzc8zscTNbY2aX5Rn/TjNrN7MH4t97yhlPtr1XKw2qWklEJFcpHe9NipklgauB1xD6Z1plZivd/ZGcSX/o7peWK47xVKUSJBOmNgcRkTzKWXI4jdCL61p3HwBuBC4o4/omxMzCo0LV5iAiMkY5k8N8YH3W+w1xWK43mdlDZnZzfATpGGb2PjNrM7O29vb2AxZgXSalO6RFRPKodIP0T4FF7n4i8EtC1+BjuPs17r7c3Ze3trYesJU31aTZ3TN4wJYnInKoKGdy2AhklwQWxGF7ufsOdx99Vue1wIvKGM8YLQ0ZtnfpUaEiIrnKmRxWAUvNbLGZZYAVwMrsCcxsbtbb84FHyxjPGC31VWzvGpjKVYqIHBTKdrWSuw+Z2aXArUASuM7dV5vZlUCbu68E/sbMzic8m3on8M5yxZNPSA4qOYiI5CpbcgBw91uAW3KGXZ71+pPAJ8sZQyEt9VX0DAzTMzC0tzsNERGpfIN0RbXUZwDY3qmqJRGRbDM7OTRUAdCuqiURkf3M6OTQWh+Sg9odRET2N6OTQ0tMDu2dSg4iItlmdHKYM9rmoJKDiMh+ZnRySCcTzKpNKzmIiOSY0ckB4r0OulpJRGQ/Sg66EU5EZAwlhwYlBxGRXEoO9Rn1ryQikkPJob6Krv4h+vS4UBGRvWZ8cmjVvQ4iImPM+OQwf1YNAOt39VQ4EhGR6WPGJ4fFLXUAPL29u8KRiIhMHzM+ORzRWE11OsHadiUHEZFRMz45JBLG4pZ6lRxERLLM+OQAsKSlTslBRCSLkgOh3eHZnT0MDI1UOhQRkWlByQFY0lrH8IjriiURkUjJgawrltQoLSICKDkA+5LD2u1dFY5ERGR6KGtyMLNzzOxxM1tjZpcVmO5NZuZmtryc8YynuTbD7LqMGqVFRKKyJQczSwJXA+cCy4CLzGxZnukagA8B95QrllIsPayeRzZ3VjIEEZFpo5wlh9OANe6+1t0HgBuBC/JM94/A54G+Msb3ivBuAAAS8ElEQVRS1EkLm3l0Uwf9Q+qAT0SknMlhPrA+6/2GOGwvMzsFWOjuPytjHCV54cJmBoZHeEylBxGRyjVIm1kC+DLw0RKmfZ+ZtZlZW3t7e1niOWlhMwAPrN9dluWLiBxMypkcNgILs94viMNGNQAnAHeY2TrgdGBlvkZpd7/G3Ze7+/LW1tayBDu3qZrWhioeVHIQESlrclgFLDWzxWaWAVYAK0dHuvsed29x90Xuvgi4Gzjf3dvKGNO4zIyTFjar5CAiQhmTg7sPAZcCtwKPAje5+2ozu9LMzi/Xep+LkxY2s3Z7N3t6BisdiohIRaXKuXB3vwW4JWfY5eNMe2Y5YynFyUeGdod7n93JWc8/vMLRiIhUju6QznLKkbPIpBL8fs2OSociIlJRSg5ZqtNJXnTkLO56SslBRGY2JYccLztmDo9u7mBn90ClQxERqRglhxwvOboFgD+o9CAiM5iSQ44XLmiivirF79aU52Y7EZGDgZJDjlQywWuXHc5NbRu4/fFtlQ5HRKQilBzyuPLCE3je4Q184Hv38ejmjkqHIyIy5ZQc8qivSvHtvzyVuqoUH/j+fXT3D1U6JBGRKaXkMI7DGqv56oqTWLe9m4/e9KC68haRGUXJoYCXHt3Cp847jv9dvYW3X/tHdvfo8lYRmRmUHIp4zyuW8K8XncwD63dz8bX3KEGIyIxQ1r6VDhXnv3AeDdUp/uqGeznrqt9w3NwGjmmt56QjmznvBXOpSiUZHB5he1c/hzdUk0jYfvN39A1SnUqSSYVcvL2rn9se2crC2bUcP6+R5trMftNv2t1LwowjmqqLxtY3OEwqYaSS+fP8wNDI3vWKiJTK3L3SMUzI8uXLva2tIr16c8/aHfywbT1PbetizbYuugeGmVOXoTqdZPOeXkYclh81i4+89lh+1LaBuqokLfVVXHPnWo6aU8e/rjiJnz64iWt/9zQ9A/vaMBbMquGEeU0kEnDfM7vZ0tFHwuDCk+bz6uMOp7NvkJ88sIlEAuY21TC3qZojmqrZtLuXb921Dnc4fl4jTTVpTl08m7e++EiGhp0v3vo4N7Wt5+IXH8mpi2bz+6e2k0km6Oof5qn2LmozSebUVzGnLsOCWTXMbaohk0qwo6uf7oFh5jdXU5MJ5w8j7jyzvZvNe/pY0lpHU02GTMo4cUEzbet28tOHNrNsbiOtDVWs39nDMzt6qEkneekxcxgYGsHMOLq1jrvWbGf1pg6SCaNvcITu/iG6B4aYU5fhuLmNXHjyfHZ0DXDHE9tY297N8fMaecdLFvHbJ9tp7+zHzLj/2V30DgyzcHYtAE01aU4+spn6qhRd/UNs7ejDCAm6o2+Q9Tt72NUzSHNtmlm1GZIJY822LhprUrxwQTNnPK+V/sER2p7ZyZptXfQMDGMYG3f3kEklOW5uAwkzUgljTn2Glvoq6qv2nVe1d/bzp00dLG6pZUlLPas3hSvcZtWmaa7N0Dc4zLod3Tywfjfd/cM0VqdYMKuGhbNraaxJ07ZuJ3t6B5nXXMO8phoaa9KMuDM84nv/D4846WSCWXUZ3B0HGqvTgNPe2c/qTR00Vqc5bm4jiQRs7ehjy55+5jZVU51O0NE7REffIOlkgsMbq+gfGiFhRn1Vikc2d9DZN8Tzj2jADLbs6WPNti7qq1Msaa3nJUvmUF+VYnfvALu6B9nTO8CunkF2x96L66qS1GZS1GWS1FbF/5kUyYTx5LZOOvuGOKyhisMbq3GHZ3f2MKs2TWNNml09A+zsHqB3YJh0MkHf0DB7egfp7Buipb6KYw6rJ5UwuvuH6OgbImGwu2eQbZ39LJhVgwMbdvawpLWOZCLBnzbu4cjZtRxzWD0O7OkdZHBohNl1GebUh+9+465eUkmjNpOis2+IB9fv5sltnRzeWM385hrmNtdQm05SnU7iOI9t7mRgeIQjZ9eSThojDu5gBgkz3J1ndvawo6ufmkyKYw6r57gjGkgkjFVP72T1pg5m1aZpaaiioTpFV/8wrfUZXrCgmaaaNDu6+lmzrYvdPYMMu1NflaI2k6S+KkVVKhxbegaGaW2o4tjDG2htqJrU8cvM7nX3Mc/LGXd6JYfJcXfuWrODG1c9SyphLJxdS3U6ydd/vYbewWEaqlIMjTi9g8O8YmkL9z+7m6541dOfnTiX959xNLt7Bnl44x7+tGkPqzfuYdidkxfO4uQjm9m4q5fv3fMsvYMhiRzdWkdjTZrNu/vY1tnHSPza/uwFc2ltqOKRzR3s7hngia1de2NMGLx8aSu/fbIdd2isTmFmVKUSHHNYPf1DI+zsHqC9s39vbMWkEsbQyNh9ZlZtml3xYJFMGPOaq9ndE37kuZa01AFQlU5SX5WkJpNiW0c4II0uO5NMMH9WDU9v7x4z/9ymappq0mzc1YsZdPUPkSekveqrUsypz7C7Z5A9vSHG+c017OkdHPO5UwmjJpNkZMSZ21xD78AwG3f3lrRtijmisZpZdRn29AywpWPfd5hJJmiqTdPe2T/pZSeMgtugmHTSGBzet4DWhip6+ofoHqjMhRjJhDFc4ANlj8/eJ6vTCfoGRya8rqPm1LK9s5+OPPtrqcZbd31Viu6BIQ7EofafLjyBt51+1KTmVXKosMe3dHL749tYcepCMqkEW/b0saS1nse2dPDdu59hxalHcsL8ppKW1Tc4zJptXYy484L5TZiFs+Gh4RG2dfbjhINctoc37OEXj2yhvirFy45p4YT5TTyxtZPdPYO86KhZJHOqvCAkut09g2zt7KN/MJxl1VWl2LS7l/6hfTv7glk1tNZXsX5XD139Q/QMDNO2bhdzm6p5/Ylz2d07SHf/EPOaa0gnEwwOj/D4lk4aqlPxdRcnLmjae8afa3tXPz97aDOz6zKcfdzh1GSS3PvMTn6xeitnPK+V4+c1MTA0Qkt9Zu+2AOjsG2T1pg4Gh0eozSQ5vLEaM2NkxGmsTtNYk9pv2w2NONXpkAAe29LJHU9sC6Wco1tY0lpHOqeKrrNvkGTCGBxydnT3s6N7gK6+IWLhhMbqFMvmNvHktk427OrlhHlNZFIJdnYPsLtngOpMkvnNNRzeuK+acGBohE27e9nR3c+yuU3UZJL0Dw2zdU8/nf1hfUkzEvF/MmEMDI+wq3sAM8MMOnoHSZjRVJPmeUc00NE3yFPbunGc1voq5jbXsHl3L4PDTmNNiobqNANDI2zt6Auf3509vYMc01pPXVWKdTu6SSaMlroqmmrTuDvP7uzh7rU7GB6B5tp0+KvJ7H1tGN0DQ/T0D4f/A0N09w/TMzBE/9AIR7fWM6suw7aOvrDPurNwdi17egbp6Btidl2G2XVpajMpBoZGqE4naapJU51O0N7Vz7rtPbg7dVUpGqtDiaqhOsWs2gybO/owQtJdt6Ob4RHn6NZ6tnT0sXF3LwkLpat0MsHOngF2dg0wMDzC/OYaRtzpGRimrirFkta6WAoLJc2te/roHRymb3CEEXeWHlZPVTrJxl29jLiTiNt/xJ2REXCcBc21NNWmGYzPoV+7vYvBYWfZ3EaOm9vA8Iizs2eAzr4hGqpSbNjdy6ObO+jqG6KpJs3Swxtoqc+QsLA9u/uH6e4fondwmCMaq6mvTrG9s58j59Qyt2n/33yplBxERGSMiSYHtVSKiMgYSg4iIjKGkoOIiIyh5CAiImMoOYiIyBhKDiIiMoaSg4iIjKHkICIiYxx0N8GZWTvwzCRmbQG2H+BwDgTFNTHTNS6YvrEpromZrnHBc4vtKHdvLXXigy45TJaZtU3k7sCporgmZrrGBdM3NsU1MdM1Lpja2FStJCIiYyg5iIjIGDMpOVxT6QDGobgmZrrGBdM3NsU1MdM1LpjC2GZMm4OIiJRuJpUcRESkREoOIiIyxiGfHMzsHDN73MzWmNllFYxjoZndbmaPmNlqM/tQHH6FmW00swfi33kVim+dmT0cY2iLw2ab2S/N7Mn4f9YUx/S8rO3ygJl1mNmHK7HNzOw6M9tmZn/KGpZ3+1jwr3Gfe8jMTqlAbF80s8fi+v/LzJrj8EVm1pu17b4xxXGN+92Z2SfjNnvczF43xXH9MCumdWb2QBw+ldtrvGNEZfYzdz9k/4Ak8BSwBMgADwLLKhTLXOCU+LoBeAJYBlwBfGwabKt1QEvOsC8Al8XXlwGfr/B3uQU4qhLbDHglcArwp2LbBzgP+DnhQaKnA/dUILbXAqn4+vNZsS3Knq4CceX97uJv4UGgClgcf7fJqYorZ/xVwOUV2F7jHSMqsp8d6iWH04A17r7W3QeAG4ELKhGIu2929/vi607gUWB+JWKZgAuA6+Pr64ELKxjLq4Gn3H0yd8c/Z+5+J7AzZ/B42+cC4AYP7gaazWzuVMbm7r9w96H49m5gQbnWP5G4CrgAuNHd+939aWAN4fc7pXFZeNj4W4AflGPdhRQ4RlRkPzvUk8N8YH3W+w1MgwOymS0CTgbuiYMujcXC66a66iaLA78ws3vN7H1x2OHuvjm+3gIcXpnQAFjB/j/Y6bDNxts+022/exfhDHPUYjO738x+Y2avqEA8+b676bLNXgFsdfcns4ZN+fbKOUZUZD871JPDtGNm9cB/Ah929w7g34GjgZOAzYQibSW83N1PAc4FPmBmr8we6aEcW5Hrns0sA5wP/CgOmi7bbK9Kbp9CzOzTwBDwvThoM3Cku58MfAT4vpk1TmFI0+67y3ER+5+ETPn2ynOM2Gsq97NDPTlsBBZmvV8Qh1WEmaUJX/r33P3HAO6+1d2H3X0E+CZlKkoX4+4b4/9twH/FOLaOFlPj/22ViI2QsO5z960xxmmxzRh/+0yL/c7M3gm8Hrg4HlSI1TY74ut7CXX7x05VTAW+u4pvMzNLAW8Efjg6bKq3V75jBBXazw715LAKWGpmi+PZ5wpgZSUCiXWZ/wE86u5fzhqeXUf458CfcuedgtjqzKxh9DWhMfNPhG11SZzsEuAnUx1btN/Z3HTYZtF422cl8I54NcnpwJ6saoEpYWbnAJ8Aznf3nqzhrWaWjK+XAEuBtVMY13jf3UpghZlVmdniGNcfpyqu6GzgMXffMDpgKrfXeMcIKrWfTUUrfCX/CC36TxAy/qcrGMfLCcXBh4AH4t95wHeAh+PwlcDcCsS2hHClyIPA6tHtBMwBfgU8CdwGzK5AbHXADqApa9iUbzNCctoMDBLqdt893vYhXD1yddznHgaWVyC2NYT66NF97Rtx2jfF7/gB4D7gDVMc17jfHfDpuM0eB86dyrji8G8Df50z7VRur/GOERXZz9R9hoiIjHGoVyuJiMgkKDmIiMgYSg4iIjKGkoOIiIyh5CAiImMoOchBw8zczK7Kev8xM7viAC3722b2FwdiWUXW82Yze9TMbs8a9oKsXj93mtnT8fVt5Y5HZDxKDnIw6QfeaGYtlQ4kW7yztlTvBt7r7q8aHeDuD7v7Se5+EuHa/4/H92c/h/WIPCdKDnIwGSI8Q/f/5I7IPfM3s674/8zYYdpPzGytmX3OzC42sz9aeH7F0VmLOdvM2szsCTN7fZw/aeHZCKtiZ3F/lbXc35rZSuCRPPFcFJf/JzP7fBx2OeFGp/8wsy+W8oHN7Gwzu8PM/odwoxNmdkmM/wEz+zczS8Th55rZH8zsPgvPJ6iLw79o4RkBD43GIlKMzkTkYHM18JCZfWEC87wQOI7QTfNa4Fp3P83Cw1Q+CHw4TreI0NfP0cDtZnYM8A5CtwSnmlkVcJeZ/SJOfwpwgocupvcys3mEZyi8CNhF6O32Qne/0szOIjzPoG0C8S8nPIfkWTM7gdDtxEvdfcjMriF0O3Eboa//V7t7T+xw70Nm9h+Eu2yPd3e3+NAfkWKUHOSg4u4dZnYD8DdAb4mzrfLY54yZPQWMHtwfBl6VNd1NHjqEe9LM1gLPJ/QzdWJWqaSJ0L/OAPDH3MQQnQrc4e7tcZ3fIzxg5r9LjDfXH9z92fj67Lj8ttAVDzWEbjJ6CA+G+X0cngF+R0iII8A3zexnwP9MMgaZYZQc5GD0FUI/N9/KGjZErCaN1SyZrHH9Wa9Hst6PsP9vILcvGSf0X/NBd781e4SZnQl0Ty78CctejwHXufvf58Tz58D/uvvbc2c2s+XAa4A3A+8nJDyRgtTmIAcdd98J3ERo3B21jlCNA+HZD+lJLPrNZpaI7RBLCB3A3Qq830JXypjZsaN1+QX8ETjDzFpij54XAb+ZRDz53Aa8ZbRR3szmmNmRwO/jOpfE4XVmttRCb7uN7v4/hLaakw9QHHKIU8lBDlZXAZdmvf8m8BMzexD4XyZ3Vv8s4cDeSOids8/MriW0RdwXu1Rup8jjUt19s5ldBtxOONP/mbsfkO7O3f1hM/sH4LZYQhqMsa4ys3cDP7TQPT3ApwhVbz+O7SUJwgNrRIpSr6wiIjKGqpVERGQMJQcRERlDyUFERMZQchARkTGUHEREZAwlBxERGUPJQURExvj/Af3swi8tB7V5AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3003,7 +11721,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3017,10 +11735,11 @@
    "source": [
     "i = 0;\n",
     "j = 0;\n",
-    "total_rmse_per_fold_train = []\n",
-    "total_rmse_per_fold_test = []\n",
-    "total_oob_error_per_fold = []\n",
+    "best = []\n",
     "while i < len(features_array):\n",
+    "    total_rmse_per_fold_train = []\n",
+    "    total_rmse_per_fold_test = []\n",
+    "    total_oob_error_per_fold = []\n",
     "    while j < len(trees_array):\n",
     "        Num_trees = trees_array[j]\n",
     "        Num_features = features_array[i]\n",
@@ -3051,7 +11770,7 @@
     "        print('Out of Bag error = {}'.format(np.mean(oob_error_per_fold)))\n",
     "        print('-----------------------------------------------------')\n",
     "        j = j + 1\n",
-    "    i = i + 1\n",
+    "    best.append(np.argmin(total_rmse_per_fold_test))\n",
     "    plt.figure()\n",
     "    plt.plot(trees_array, total_oob_error_per_fold)\n",
     "    plt.title('Out of Bag error (y-axis) against number of trees (x-axis)')\n",
@@ -3062,7 +11781,29 @@
     "    plt.plot(trees_array, total_rmse_per_fold_test)\n",
     "    plt.title('average Test RMSE (y-axis) against number of trees (x-axis)')\n",
     "    plt.xlabel('Number of Trees')\n",
-    "    plt.ylabel('average Test RMSE')"
+    "    plt.ylabel('average Test RMSE')\n",
+    "    j = 0\n",
+    "    i = i + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[60, 19, 3, 18, 5]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best"
    ]
   },
   {
@@ -3074,18 +11815,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
     "depth_array = np.arange(1,41)\n",
-    "Best_num_features = 1\n",
-    "Best_num_trees = 1"
+    "Best_num_features = 3\n",
+    "Best_num_trees = 4"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -3123,13 +11864,35 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 1\n",
+      "RMSE Training = 0.09744766048996603\n",
+      "RMSE Testing = 0.09730819041210333\n",
+      "Out of Bag error = 0.9519461308140652\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 2\n",
+      "RMSE Training = 0.0867187586927833\n",
+      "RMSE Testing = 0.08653840903390667\n",
+      "Out of Bag error = 0.818206094149847\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3185,44 +11948,20 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 1\n",
-      "RMSE Training = 0.10029346959077583\n",
-      "RMSE Testing = 0.10013885582945978\n",
-      "Out of Bag error = 1.1901077092685042\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 2\n",
-      "RMSE Training = 0.09645719190220034\n",
-      "RMSE Testing = 0.09649147156766372\n",
-      "Out of Bag error = 1.162937607061139\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 3\n",
-      "RMSE Training = 0.09015842359813377\n",
-      "RMSE Testing = 0.09075260847907274\n",
-      "Out of Bag error = 1.1280663376781566\n",
+      "RMSE Training = 0.0783243175710688\n",
+      "RMSE Testing = 0.07809194419129804\n",
+      "Out of Bag error = 0.7177853657027449\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 4\n",
-      "RMSE Training = 0.08069265468092283\n",
-      "RMSE Testing = 0.08088446050083163\n",
-      "Out of Bag error = 1.066264783309252\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 5\n",
-      "RMSE Training = 0.07783628011360069\n",
-      "RMSE Testing = 0.07857923079210112\n",
-      "Out of Bag error = 1.0526418662210733\n",
+      "RMSE Training = 0.06198155089474257\n",
+      "RMSE Testing = 0.061474473608275605\n",
+      "Out of Bag error = 0.5492388665047793\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3248,6 +11987,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 5\n",
+      "RMSE Training = 0.04603255354045528\n",
+      "RMSE Testing = 0.04631528500563094\n",
+      "Out of Bag error = 0.41381642709133787\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3280,6 +12041,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 6\n",
+      "RMSE Training = 0.030998384504294912\n",
+      "RMSE Testing = 0.03223120902126097\n",
+      "Out of Bag error = 0.3223520748081723\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3303,28 +12086,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 6\n",
-      "RMSE Training = 0.0690794793243052\n",
-      "RMSE Testing = 0.06975234135026605\n",
-      "Out of Bag error = 1.0335571629009153\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 7\n",
-      "RMSE Training = 0.05332917861353219\n",
-      "RMSE Testing = 0.054182026304627665\n",
-      "Out of Bag error = 0.9633857220950377\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 8\n",
-      "RMSE Training = 0.05897010786136999\n",
-      "RMSE Testing = 0.061618071240896286\n",
-      "Out of Bag error = 0.9763287298527821\n",
+      "RMSE Training = 0.024498790136397004\n",
+      "RMSE Testing = 0.025070117485174542\n",
+      "Out of Bag error = 0.28817705722807047\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3345,11 +12112,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 8\n",
+      "RMSE Training = 0.017134900020134503\n",
+      "RMSE Testing = 0.018010377928302172\n",
+      "Out of Bag error = 0.24779290193445203\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3373,7 +12156,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 9\n",
+      "RMSE Training = 0.014425092319059848\n",
+      "RMSE Testing = 0.016452414524046496\n",
+      "Out of Bag error = 0.23687996859578453\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3391,30 +12194,14 @@
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 9\n",
-      "RMSE Training = 0.039404023321425515\n",
-      "RMSE Testing = 0.042329570563549294\n",
-      "Out of Bag error = 0.9043862011876141\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 10\n",
-      "RMSE Training = 0.036500285289040146\n",
-      "RMSE Testing = 0.04232661023657303\n",
-      "Out of Bag error = 0.8908124308392222\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 11\n",
-      "RMSE Training = 0.03259189704145247\n",
-      "RMSE Testing = 0.037750679330887965\n",
-      "Out of Bag error = 0.9064370455646227\n",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 10\n",
+      "RMSE Training = 0.012693519217498419\n",
+      "RMSE Testing = 0.014721250593285762\n",
+      "Out of Bag error = 0.2239169290944914\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3445,7 +12232,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 11\n",
+      "RMSE Training = 0.012017911382738533\n",
+      "RMSE Testing = 0.015466674282652157\n",
+      "Out of Bag error = 0.23260391008975842\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3473,20 +12280,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 12\n",
-      "RMSE Training = 0.028901397255712967\n",
-      "RMSE Testing = 0.03904937666239299\n",
-      "Out of Bag error = 0.9012538840250695\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 13\n",
-      "RMSE Training = 0.027102647504993537\n",
-      "RMSE Testing = 0.03987960637130426\n",
-      "Out of Bag error = 0.9050215895658885\n",
+      "RMSE Training = 0.011215324109979292\n",
+      "RMSE Testing = 0.014427438653534954\n",
+      "Out of Bag error = 0.2489874597029173\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3511,7 +12310,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 13\n",
+      "RMSE Training = 0.010194322616534534\n",
+      "RMSE Testing = 0.014549351750308479\n",
+      "Out of Bag error = 0.23266024877677527\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3537,20 +12356,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 14\n",
-      "RMSE Training = 0.030641476017787994\n",
-      "RMSE Testing = 0.047628547863471496\n",
-      "Out of Bag error = 0.9277031298924717\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 15\n",
-      "RMSE Training = 0.026030936694950817\n",
-      "RMSE Testing = 0.03882582236123748\n",
-      "Out of Bag error = 0.9040372880825542\n",
+      "RMSE Training = 0.009848162432703514\n",
+      "RMSE Testing = 0.015238253175404509\n",
+      "Out of Bag error = 0.2407605490512153\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3576,6 +12387,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 15\n",
+      "RMSE Training = 0.00939332899163858\n",
+      "RMSE Testing = 0.016117335925975907\n",
+      "Out of Bag error = 0.2470401864881699\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3599,20 +12438,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 16\n",
-      "RMSE Training = 0.02411660849715343\n",
-      "RMSE Testing = 0.039319600172386705\n",
-      "Out of Bag error = 0.9006216112544371\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 17\n",
-      "RMSE Training = 0.022993729795706276\n",
-      "RMSE Testing = 0.03386801958354739\n",
-      "Out of Bag error = 0.9080830329437533\n",
+      "RMSE Training = 0.009064652271374474\n",
+      "RMSE Testing = 0.015290090132875012\n",
+      "Out of Bag error = 0.24974216258790133\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3643,9 +12474,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 17\n",
+      "RMSE Training = 0.008462935651336396\n",
+      "RMSE Testing = 0.015588043773498284\n",
+      "Out of Bag error = 0.25370096219401617\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3661,7 +12510,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 18\n",
+      "RMSE Training = 0.008065137753239679\n",
+      "RMSE Testing = 0.015322555831926604\n",
+      "Out of Bag error = 0.23789689874771688\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3693,20 +12562,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 18\n",
-      "RMSE Training = 0.02346141112714842\n",
-      "RMSE Testing = 0.03469858098552804\n",
-      "Out of Bag error = 0.9039051233703495\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 19\n",
-      "RMSE Training = 0.01997969316574139\n",
-      "RMSE Testing = 0.03155916262425835\n",
-      "Out of Bag error = 0.8988605239942478\n",
+      "RMSE Training = 0.007747261245255854\n",
+      "RMSE Testing = 0.0158751930911148\n",
+      "Out of Bag error = 0.22994143116391452\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3720,6 +12581,36 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 20\n",
+      "RMSE Training = 0.008303238561576217\n",
+      "RMSE Testing = 0.01665362015873618\n",
+      "Out of Bag error = 0.23901452088456515\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3751,20 +12642,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 20\n",
-      "RMSE Training = 0.020634510766763068\n",
-      "RMSE Testing = 0.03462071906623213\n",
-      "Out of Bag error = 0.9075007533325692\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 21\n",
-      "RMSE Training = 0.0240448783345509\n",
-      "RMSE Testing = 0.03649476386042087\n",
-      "Out of Bag error = 0.8992892205852601\n",
+      "RMSE Training = 0.007757957133070115\n",
+      "RMSE Testing = 0.01610797487483718\n",
+      "Out of Bag error = 0.24661799893275468\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3780,6 +12663,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 22\n",
+      "RMSE Training = 0.007672386977117117\n",
+      "RMSE Testing = 0.015704665142253098\n",
+      "Out of Bag error = 0.23340437588035529\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3811,20 +12722,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 22\n",
-      "RMSE Training = 0.02014037834365806\n",
-      "RMSE Testing = 0.030916883386743317\n",
-      "Out of Bag error = 0.8828980839587295\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 23\n",
-      "RMSE Training = 0.019899025157352764\n",
-      "RMSE Testing = 0.03274213794137807\n",
-      "Out of Bag error = 0.9048034004286938\n",
+      "RMSE Training = 0.008186982421227862\n",
+      "RMSE Testing = 0.016096565867784976\n",
+      "Out of Bag error = 0.2523620297652452\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3840,6 +12743,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 24\n",
+      "RMSE Training = 0.00821365393489374\n",
+      "RMSE Testing = 0.01651123844877171\n",
+      "Out of Bag error = 0.2552094647342341\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3871,20 +12802,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 24\n",
-      "RMSE Training = 0.023221960748333832\n",
-      "RMSE Testing = 0.03485497658196572\n",
-      "Out of Bag error = 0.9049094310700309\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 25\n",
-      "RMSE Training = 0.024682403417431394\n",
-      "RMSE Testing = 0.04183962006870179\n",
-      "Out of Bag error = 0.9124508036943665\n",
+      "RMSE Training = 0.0075126627315455635\n",
+      "RMSE Testing = 0.01564842569578454\n",
+      "Out of Bag error = 0.23970480695034269\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3907,13 +12830,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 26\n",
+      "RMSE Training = 0.007841592274964008\n",
+      "RMSE Testing = 0.016305029823556375\n",
+      "Out of Bag error = 0.25319894940479143\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3937,7 +12874,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 27\n",
+      "RMSE Training = 0.007862998321788725\n",
+      "RMSE Testing = 0.01590058662975413\n",
+      "Out of Bag error = 0.2570075198325196\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3961,20 +12918,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 26\n",
-      "RMSE Training = 0.02276289334360069\n",
-      "RMSE Testing = 0.03862566986614009\n",
-      "Out of Bag error = 0.8917828428806907\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 27\n",
-      "RMSE Training = 0.026248142876969516\n",
-      "RMSE Testing = 0.038976037835335646\n",
-      "Out of Bag error = 0.9234429661916387\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 28\n",
+      "RMSE Training = 0.007714627404397531\n",
+      "RMSE Testing = 0.015629687540038573\n",
+      "Out of Bag error = 0.24341453388596848\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3998,6 +12947,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 29\n",
+      "RMSE Training = 0.008033825868003722\n",
+      "RMSE Testing = 0.01633489875186008\n",
+      "Out of Bag error = 0.24122756799485812\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4021,20 +12998,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 28\n",
-      "RMSE Training = 0.02839106879811119\n",
-      "RMSE Testing = 0.04270556407291485\n",
-      "Out of Bag error = 0.9294501445263105\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 29\n",
-      "RMSE Training = 0.023332052168506943\n",
-      "RMSE Testing = 0.040700754105578496\n",
-      "Out of Bag error = 0.8873795945608194\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 30\n",
+      "RMSE Training = 0.0077104130870081404\n",
+      "RMSE Testing = 0.015771666741426018\n",
+      "Out of Bag error = 0.2567786741325135\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4065,12 +13034,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -4079,20 +13042,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 30\n",
-      "RMSE Training = 0.02236605447800038\n",
-      "RMSE Testing = 0.03416835493274427\n",
-      "Out of Bag error = 0.885069165934133\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 31\n",
-      "RMSE Training = 0.02496493526117527\n",
-      "RMSE Testing = 0.037996154261883575\n",
-      "Out of Bag error = 0.9019860616608721\n",
+      "RMSE Training = 0.007667144076457697\n",
+      "RMSE Testing = 0.015605467622523087\n",
+      "Out of Bag error = 0.24127028987486848\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4115,11 +13070,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 32\n",
+      "RMSE Training = 0.007670658590843071\n",
+      "RMSE Testing = 0.015599814441104054\n",
+      "Out of Bag error = 0.25325850439731434\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4143,7 +13114,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 33\n",
+      "RMSE Training = 0.00793328493261828\n",
+      "RMSE Testing = 0.01593771589188875\n",
+      "Out of Bag error = 0.2467544782872491\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4167,20 +13158,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 32\n",
-      "RMSE Training = 0.024883128709612852\n",
-      "RMSE Testing = 0.04091260561404413\n",
-      "Out of Bag error = 0.8954030054160137\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 33\n",
-      "RMSE Training = 0.023337211710397175\n",
-      "RMSE Testing = 0.04058816987519835\n",
-      "Out of Bag error = 0.8860384825820613\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 34\n",
+      "RMSE Training = 0.007583663772697683\n",
+      "RMSE Testing = 0.015924560968099265\n",
+      "Out of Bag error = 0.24668070778833578\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4211,12 +13194,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -4225,20 +13202,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 34\n",
-      "RMSE Training = 0.022519219623101338\n",
-      "RMSE Testing = 0.038524823183353554\n",
-      "Out of Bag error = 0.8931254175025438\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 35\n",
-      "RMSE Training = 0.02160377854034624\n",
-      "RMSE Testing = 0.03595666705098065\n",
-      "Out of Bag error = 0.8921527805889182\n",
+      "RMSE Training = 0.007866777892508541\n",
+      "RMSE Testing = 0.015594649851681427\n",
+      "Out of Bag error = 0.24703162864670433\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4246,20 +13215,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4283,20 +13238,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 36\n",
-      "RMSE Training = 0.02392429891019801\n",
-      "RMSE Testing = 0.03895113079229779\n",
-      "Out of Bag error = 0.8934562360852791\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 37\n",
-      "RMSE Training = 0.02234566224119603\n",
-      "RMSE Testing = 0.037789210376496615\n",
-      "Out of Bag error = 0.897934910129373\n",
+      "RMSE Training = 0.007673803366320356\n",
+      "RMSE Testing = 0.015387818202939363\n",
+      "Out of Bag error = 0.23997080520167477\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4326,6 +13273,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 37\n",
+      "RMSE Training = 0.007729927958501977\n",
+      "RMSE Testing = 0.015285141406523603\n",
+      "Out of Bag error = 0.2377511173548088\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4340,6 +13309,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 38\n",
+      "RMSE Training = 0.007772351581268967\n",
+      "RMSE Testing = 0.01622127324186521\n",
+      "Out of Bag error = 0.25114435384335193\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4371,28 +13362,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 38\n",
-      "RMSE Training = 0.025561673675717007\n",
-      "RMSE Testing = 0.04074464394821514\n",
-      "Out of Bag error = 0.9124087932557327\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 39\n",
-      "RMSE Training = 0.022032302533764116\n",
-      "RMSE Testing = 0.035483243726283344\n",
-      "Out of Bag error = 0.8917040953701079\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 40\n",
-      "RMSE Training = 0.0235050672888152\n",
-      "RMSE Testing = 0.0379045751751005\n",
-      "Out of Bag error = 0.8898144155367556\n",
+      "RMSE Training = 0.00794054213200324\n",
+      "RMSE Testing = 0.016411747549574376\n",
+      "Out of Bag error = 0.23599223518809742\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4407,9 +13382,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 40\n",
+      "RMSE Training = 0.008190429742953536\n",
+      "RMSE Testing = 0.015841847782126283\n",
+      "Out of Bag error = 0.24495821052400749\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4424,13 +13417,13 @@
        "Text(0, 0.5, 'average Test RMSE')"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4442,7 +13435,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4488,6 +13481,8 @@
     "    print('Out of Bag error = {}'.format(np.mean(oob_error_per_fold)))\n",
     "    print('-----------------------------------------------------')\n",
     "    j = j + 1\n",
+    "    \n",
+    "best_depth = np.argmin(total_rmse_per_fold_test)\n",
     "\n",
     "plt.figure()\n",
     "plt.plot(depth_array, total_oob_error_per_fold)\n",
@@ -4503,6 +13498,26 @@
     "    \n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_depth"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -4512,14 +13527,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_depth = best_depth + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Feature Importances for best Random Forest Regression :  [0.39769433 0.100803   0.26573009 0.01952733 0.21624524]\n"
+      "Feature Importances for best Random Forest Regression :  [0.21463007 0.11396964 0.37938158 0.00538934 0.28662937]\n"
      ]
     },
     {
@@ -4533,7 +13557,7 @@
    ],
    "source": [
     "model = RandomForestRegressor(n_estimators = Best_num_trees, max_features = Best_num_features, \n",
-    "                              max_depth = depth_trees, bootstrap = Bootstrap, oob_score=True)\n",
+    "                              max_depth = best_depth, bootstrap = Bootstrap, oob_score=True)\n",
     "\n",
     "model.fit(X_encoded,Y_encoded_rf)\n",
     "\n",
diff --git a/Dataset1.ipynb b/Dataset1.ipynb
index 5484c98..5832b8b 100644
--- a/Dataset1.ipynb
+++ b/Dataset1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,33 +362,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "RMSE Training for fold 0 = 0.10400571565013786\n",
-      "RMSE Testing for fold 0 = 0.0997875718081956\n",
-      "RMSE Training for fold 1 = 0.10370147977329444\n",
-      "RMSE Testing for fold 1 = 0.10258680387540348\n",
-      "RMSE Training for fold 2 = 0.10403667023213119\n",
-      "RMSE Testing for fold 2 = 0.09948249179500962\n",
-      "RMSE Training for fold 3 = 0.10399692337273836\n",
-      "RMSE Testing for fold 3 = 0.09987071247691796\n",
-      "RMSE Training for fold 4 = 0.1023387014504197\n",
-      "RMSE Testing for fold 4 = 0.11425633876928286\n",
-      "RMSE Training for fold 5 = 0.10346837217623998\n",
-      "RMSE Testing for fold 5 = 0.10468309066373536\n",
-      "RMSE Training for fold 6 = 0.1041261226688795\n",
-      "RMSE Testing for fold 6 = 0.09864477362514056\n",
-      "RMSE Training for fold 7 = 0.10379373695227591\n",
-      "RMSE Testing for fold 7 = 0.10174577472738733\n",
-      "RMSE Training for fold 8 = 0.10307111007107508\n",
-      "RMSE Testing for fold 8 = 0.10815642854126484\n",
-      "RMSE Training for fold 9 = 0.10333463066100897\n",
-      "RMSE Testing for fold 9 = 0.10587579634157593\n"
+      "RMSE Training for fold 0 = 0.10303539344689733\n",
+      "RMSE Testing for fold 0 = 0.10845567097417186\n",
+      "RMSE Training for fold 1 = 0.10419089612177095\n",
+      "RMSE Testing for fold 1 = 0.09802045254832206\n",
+      "RMSE Training for fold 2 = 0.10349185745093904\n",
+      "RMSE Testing for fold 2 = 0.10447346688262305\n",
+      "RMSE Training for fold 3 = 0.10344469887189557\n",
+      "RMSE Testing for fold 3 = 0.10490538873653102\n",
+      "RMSE Training for fold 4 = 0.1025899769379141\n",
+      "RMSE Testing for fold 4 = 0.11222617684153978\n",
+      "RMSE Training for fold 5 = 0.1030302651199911\n",
+      "RMSE Testing for fold 5 = 0.10850493306300102\n",
+      "RMSE Training for fold 6 = 0.10450952572006245\n",
+      "RMSE Testing for fold 6 = 0.09492755358007658\n",
+      "RMSE Training for fold 7 = 0.10398159298299949\n",
+      "RMSE Testing for fold 7 = 0.10000759755069878\n",
+      "RMSE Training for fold 8 = 0.1036367084864887\n",
+      "RMSE Testing for fold 8 = 0.10317625379266929\n",
+      "RMSE Training for fold 9 = 0.10396055800882181\n",
+      "RMSE Testing for fold 9 = 0.10019529711302326\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/base.py:485: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
+      "  linalg.lstsq(X, y)\n"
      ]
     }
    ],
@@ -411,15 +419,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average RMSE Training = 0.10358734630082009\n",
-      "Average RMSE Testing = 0.10350897826239136\n"
+      "Average RMSE Training = 0.10358714731477806\n",
+      "Average RMSE Testing = 0.10348927910826566\n"
      ]
     }
    ],
@@ -437,22 +445,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x11b442c88>"
+       "<matplotlib.legend.Legend at 0x11430d3c8>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -464,7 +472,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -514,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -536,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -574,14 +582,14 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 0 = 0.06126071297842859\n",
-      "RMSE Testing for fold 0 = 0.05798243518318052\n",
-      "Out of Bag error for fold 0 = 0.34422750167498295\n",
+      "RMSE Training for fold 0 = 0.060342718323088906\n",
+      "RMSE Testing for fold 0 = 0.06600103122494523\n",
+      "Out of Bag error for fold 0 = 0.3422191688756486\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 1 = 0.06038490279774731\n",
-      "RMSE Testing for fold 1 = 0.06486151881898947\n",
-      "Out of Bag error for fold 1 = 0.3378527237237616\n",
+      "RMSE Training for fold 1 = 0.05999354404077284\n",
+      "RMSE Testing for fold 1 = 0.05318740413550893\n",
+      "Out of Bag error for fold 1 = 0.334175430446894\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -600,14 +608,14 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 2 = 0.06112971065222718\n",
-      "RMSE Testing for fold 2 = 0.05714984869304187\n",
-      "Out of Bag error for fold 2 = 0.34150480422816987\n",
+      "RMSE Training for fold 2 = 0.06070297993348546\n",
+      "RMSE Testing for fold 2 = 0.06200857336669701\n",
+      "Out of Bag error for fold 2 = 0.3418665442449461\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 3 = 0.06039092317950195\n",
-      "RMSE Testing for fold 3 = 0.05797000039547089\n",
-      "Out of Bag error for fold 3 = 0.336287156937473\n",
+      "RMSE Training for fold 3 = 0.060227733092934436\n",
+      "RMSE Testing for fold 3 = 0.06623514605665273\n",
+      "Out of Bag error for fold 3 = 0.3369150802713663\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -626,14 +634,14 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 4 = 0.06022803943783143\n",
-      "RMSE Testing for fold 4 = 0.06581867857608557\n",
-      "Out of Bag error for fold 4 = 0.34362030845707925\n",
+      "RMSE Training for fold 4 = 0.05982159473119974\n",
+      "RMSE Testing for fold 4 = 0.0622506437717224\n",
+      "Out of Bag error for fold 4 = 0.3403855824932449\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 5 = 0.06017914080444452\n",
-      "RMSE Testing for fold 5 = 0.06096465116727237\n",
-      "Out of Bag error for fold 5 = 0.3364687194970032\n",
+      "RMSE Training for fold 5 = 0.06030482955967637\n",
+      "RMSE Testing for fold 5 = 0.06633429241801071\n",
+      "Out of Bag error for fold 5 = 0.34033955939962013\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -652,24 +660,24 @@
      "output_type": "stream",
      "text": [
       "----------------------------------------------------\n",
-      "RMSE Training for fold 6 = 0.060143998555742206\n",
-      "RMSE Testing for fold 6 = 0.052439791284759806\n",
-      "Out of Bag error for fold 6 = 0.33367885942183073\n",
+      "RMSE Training for fold 6 = 0.06066202054205524\n",
+      "RMSE Testing for fold 6 = 0.053184772935539554\n",
+      "Out of Bag error for fold 6 = 0.33606049192430976\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 7 = 0.060881586887986665\n",
-      "RMSE Testing for fold 7 = 0.0599276734215616\n",
-      "Out of Bag error for fold 7 = 0.34238807095937507\n",
+      "RMSE Training for fold 7 = 0.060589565741104454\n",
+      "RMSE Testing for fold 7 = 0.05662312472349231\n",
+      "Out of Bag error for fold 7 = 0.3390706702553714\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 8 = 0.06041761265041053\n",
-      "RMSE Testing for fold 8 = 0.06449080352793264\n",
-      "Out of Bag error for fold 8 = 0.34241013974135526\n",
+      "RMSE Training for fold 8 = 0.06084161060273395\n",
+      "RMSE Testing for fold 8 = 0.06178033569929699\n",
+      "Out of Bag error for fold 8 = 0.3417962938503758\n",
       "----------------------------------------------------\n",
       "----------------------------------------------------\n",
-      "RMSE Training for fold 9 = 0.06007263987303771\n",
-      "RMSE Testing for fold 9 = 0.06408537393773711\n",
-      "Out of Bag error for fold 9 = 0.33692796236060274\n",
+      "RMSE Training for fold 9 = 0.06141660589675676\n",
+      "RMSE Testing for fold 9 = 0.05618073772151881\n",
+      "Out of Bag error for fold 9 = 0.3461750723884218\n",
       "----------------------------------------------------\n"
      ]
     },
@@ -677,8 +685,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
@@ -708,16 +714,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average RMSE Training = 0.060508926781735806\n",
-      "Average RMSE Testing = 0.06056907750060318\n",
-      "Average Out of Bag Error = 0.33953662470016344\n"
+      "Average RMSE Training = 0.06049032024638081\n",
+      "Average RMSE Testing = 0.06037860620533847\n",
+      "Average Out of Bag Error = 0.3399003894150199\n"
      ]
     }
    ],
@@ -736,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -802,8 +808,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -814,23 +818,23 @@
       "-----------------------------------------------------\n",
       "Number of trees = 1\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.08600269522770379\n",
-      "RMSE Testing = 0.08595696859673009\n",
-      "Out of Bag error = 1.1050466340290819\n",
+      "RMSE Training = 0.0823494804595731\n",
+      "RMSE Testing = 0.08283825378858026\n",
+      "Out of Bag error = 1.0798942445035107\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 2\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.08131789964855246\n",
-      "RMSE Testing = 0.081823250044802\n",
-      "Out of Bag error = 0.9309084353663628\n",
+      "RMSE Training = 0.08246242451041672\n",
+      "RMSE Testing = 0.08236891188157434\n",
+      "Out of Bag error = 0.9424330057816268\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 3\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07829344871000513\n",
-      "RMSE Testing = 0.0786995157685069\n",
-      "Out of Bag error = 0.81172920593767\n",
+      "RMSE Training = 0.07700968909257182\n",
+      "RMSE Testing = 0.07729087270993668\n",
+      "Out of Bag error = 0.8086883660957028\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -877,16 +881,16 @@
       "-----------------------------------------------------\n",
       "Number of trees = 4\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07582317293627978\n",
-      "RMSE Testing = 0.07573334498888282\n",
-      "Out of Bag error = 0.7328192851555144\n",
+      "RMSE Training = 0.07721935004206128\n",
+      "RMSE Testing = 0.07707896370361683\n",
+      "Out of Bag error = 0.7332061780309468\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 5\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07824559250021182\n",
-      "RMSE Testing = 0.07836107763487572\n",
-      "Out of Bag error = 0.7009958596982658\n",
+      "RMSE Training = 0.07863996660341205\n",
+      "RMSE Testing = 0.07854574300100362\n",
+      "Out of Bag error = 0.7091519144356438\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -935,8 +939,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -947,9 +949,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 6\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07640061404938889\n",
-      "RMSE Testing = 0.07632529246491367\n",
-      "Out of Bag error = 0.6462296226395466\n",
+      "RMSE Training = 0.0769363463805625\n",
+      "RMSE Testing = 0.07721853154745752\n",
+      "Out of Bag error = 0.652577364919106\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -984,9 +986,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 7\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07460925253711437\n",
-      "RMSE Testing = 0.07448477393934767\n",
-      "Out of Bag error = 0.6026901888399395\n",
+      "RMSE Training = 0.07817899517714692\n",
+      "RMSE Testing = 0.07830310363208996\n",
+      "Out of Bag error = 0.6411896230990621\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -994,6 +996,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1019,9 +1035,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 8\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07944487097181759\n",
-      "RMSE Testing = 0.07942807162849061\n",
-      "Out of Bag error = 0.6490891495948425\n",
+      "RMSE Training = 0.07638903068808353\n",
+      "RMSE Testing = 0.07636637230657327\n",
+      "Out of Bag error = 0.6078084706214314\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1029,20 +1045,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1068,9 +1070,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 9\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07597551510562257\n",
-      "RMSE Testing = 0.07652799581282663\n",
-      "Out of Bag error = 0.5885329341718089\n",
+      "RMSE Training = 0.0780297522723633\n",
+      "RMSE Testing = 0.07796337708701274\n",
+      "Out of Bag error = 0.6109831026909391\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1089,6 +1091,8 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1099,9 +1103,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 10\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07546305242371641\n",
-      "RMSE Testing = 0.07569678993183042\n",
-      "Out of Bag error = 0.5680485618581963\n",
+      "RMSE Training = 0.07801569170501642\n",
+      "RMSE Testing = 0.07816308286579876\n",
+      "Out of Bag error = 0.6053342525686438\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1142,9 +1146,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 11\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07566112894341903\n",
-      "RMSE Testing = 0.07583294135415959\n",
-      "Out of Bag error = 0.566820740869799\n",
+      "RMSE Training = 0.07488855239588874\n",
+      "RMSE Testing = 0.0750597476921647\n",
+      "Out of Bag error = 0.5603137511586556\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1173,6 +1177,8 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1183,9 +1189,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 12\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07715429465337018\n",
-      "RMSE Testing = 0.07743244318892788\n",
-      "Out of Bag error = 0.5785813405087349\n",
+      "RMSE Training = 0.07624367889486336\n",
+      "RMSE Testing = 0.07613668278127306\n",
+      "Out of Bag error = 0.5680860332335186\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1204,14 +1210,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1222,9 +1220,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 13\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07720024830430336\n",
-      "RMSE Testing = 0.07733590416674095\n",
-      "Out of Bag error = 0.5839544705074777\n",
+      "RMSE Training = 0.0750797633541285\n",
+      "RMSE Testing = 0.07519365927505199\n",
+      "Out of Bag error = 0.551779443641419\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1251,6 +1249,10 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1261,9 +1263,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 14\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07619605240453789\n",
-      "RMSE Testing = 0.0764184178141841\n",
-      "Out of Bag error = 0.564030868798622\n",
+      "RMSE Training = 0.07536183743217255\n",
+      "RMSE Testing = 0.07535394691109602\n",
+      "Out of Bag error = 0.5549605720167998\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1300,9 +1302,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 15\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07670317013050573\n",
-      "RMSE Testing = 0.07677319463516982\n",
-      "Out of Bag error = 0.5679379989471646\n",
+      "RMSE Training = 0.07743813429749472\n",
+      "RMSE Testing = 0.07722647518373568\n",
+      "Out of Bag error = 0.580024430246438\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1325,6 +1327,8 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1335,9 +1339,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 16\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0752049922106906\n",
-      "RMSE Testing = 0.07512678835322723\n",
-      "Out of Bag error = 0.5441951680201874\n",
+      "RMSE Training = 0.07618056512651251\n",
+      "RMSE Testing = 0.07630961188411545\n",
+      "Out of Bag error = 0.5583260317490131\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1360,6 +1364,14 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1370,9 +1382,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 17\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07674241632782196\n",
-      "RMSE Testing = 0.07693811162963506\n",
-      "Out of Bag error = 0.5646337147274499\n",
+      "RMSE Training = 0.07750537942593308\n",
+      "RMSE Testing = 0.07758514483885923\n",
+      "Out of Bag error = 0.5766389838922714\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1395,14 +1407,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1413,9 +1417,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 18\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07763879289249812\n",
-      "RMSE Testing = 0.07764892157557338\n",
-      "Out of Bag error = 0.5764361439446976\n",
+      "RMSE Training = 0.078374541301954\n",
+      "RMSE Testing = 0.07843413042284367\n",
+      "Out of Bag error = 0.5881840900894801\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1438,6 +1442,10 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1448,9 +1456,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 19\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.076519542272754\n",
-      "RMSE Testing = 0.07645544522728247\n",
-      "Out of Bag error = 0.559888257167874\n",
+      "RMSE Training = 0.07612144226345642\n",
+      "RMSE Testing = 0.0760127377219748\n",
+      "Out of Bag error = 0.5558869008133287\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1458,12 +1466,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1481,9 +1483,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 20\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0771206577092081\n",
-      "RMSE Testing = 0.07711920066633249\n",
-      "Out of Bag error = 0.5684832564441655\n",
+      "RMSE Training = 0.07562922962849987\n",
+      "RMSE Testing = 0.07548020647252797\n",
+      "Out of Bag error = 0.5462728530029385\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1502,10 +1504,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -1516,9 +1514,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 21\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07603220563891631\n",
-      "RMSE Testing = 0.07607224764076086\n",
-      "Out of Bag error = 0.551797214060229\n",
+      "RMSE Training = 0.07631871945238278\n",
+      "RMSE Testing = 0.07633118384670001\n",
+      "Out of Bag error = 0.5527747369459928\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1526,6 +1524,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
@@ -1537,9 +1543,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 22\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07502509151557815\n",
-      "RMSE Testing = 0.07498503632360716\n",
-      "Out of Bag error = 0.5363651863435039\n",
+      "RMSE Training = 0.07748464330222933\n",
+      "RMSE Testing = 0.07758514654811037\n",
+      "Out of Bag error = 0.5696295020902284\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1547,6 +1553,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1560,9 +1568,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 23\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07562849460346883\n",
-      "RMSE Testing = 0.07576027330252429\n",
-      "Out of Bag error = 0.5454331102816521\n",
+      "RMSE Training = 0.07658261461225616\n",
+      "RMSE Testing = 0.07660933485830759\n",
+      "Out of Bag error = 0.5557432169830344\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1570,6 +1578,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -1583,9 +1593,16 @@
       "-----------------------------------------------------\n",
       "Number of trees = 24\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07672960063549569\n",
-      "RMSE Testing = 0.07669246373780436\n",
-      "Out of Bag error = 0.558586290981216\n",
+      "RMSE Training = 0.07650060621082821\n",
+      "RMSE Testing = 0.07666614202120393\n",
+      "Out of Bag error = 0.5542164496720027\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 1\n",
+      "RMSE Training = 0.07548103514942568\n",
+      "RMSE Testing = 0.07560117094875803\n",
+      "Out of Bag error = 0.5415250778391896\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1593,6 +1610,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
@@ -1601,19 +1624,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-----------------------------------------------------\n",
-      "Number of trees = 25\n",
-      "Number of features = 1\n",
-      "RMSE Training = 0.0754951248960899\n",
-      "RMSE Testing = 0.07559725450022196\n",
-      "Out of Bag error = 0.543916016883893\n",
-      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 26\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07666511517549227\n",
-      "RMSE Testing = 0.07673950967716087\n",
-      "Out of Bag error = 0.5576725589079038\n",
+      "RMSE Training = 0.07632516787758371\n",
+      "RMSE Testing = 0.0763062921015964\n",
+      "Out of Bag error = 0.5513046957639924\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1632,9 +1648,9 @@
       "-----------------------------------------------------\n",
       "Number of trees = 27\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07584134859550855\n",
-      "RMSE Testing = 0.07565105617082121\n",
-      "Out of Bag error = 0.5459957269304607\n",
+      "RMSE Training = 0.07544981335578578\n",
+      "RMSE Testing = 0.07554968401705917\n",
+      "Out of Bag error = 0.5368631671022817\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -1653,1263 +1669,9965 @@
       "-----------------------------------------------------\n",
       "Number of trees = 28\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577555460251728\n",
-      "RMSE Testing = 0.07585899333572792\n",
-      "Out of Bag error = 0.5417646544943107\n",
+      "RMSE Training = 0.07525868403669272\n",
+      "RMSE Testing = 0.07524707605464637\n",
+      "Out of Bag error = 0.5358115785056431\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 29\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07581628675894876\n",
-      "RMSE Testing = 0.07588488729464553\n",
-      "Out of Bag error = 0.5442131311587797\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07665226834823699\n",
+      "RMSE Testing = 0.07675569718446554\n",
+      "Out of Bag error = 0.5546467958625863\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 30\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585628500256583\n",
-      "RMSE Testing = 0.07580125192402978\n",
-      "Out of Bag error = 0.5433968900015672\n",
+      "RMSE Training = 0.0761936567956908\n",
+      "RMSE Testing = 0.07611522902481274\n",
+      "Out of Bag error = 0.5495200058810872\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 31\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07505828310895313\n",
-      "RMSE Testing = 0.07515802950745977\n",
-      "Out of Bag error = 0.533308215969996\n",
+      "RMSE Training = 0.07651646260339953\n",
+      "RMSE Testing = 0.0766205511856137\n",
+      "Out of Bag error = 0.5524115904208368\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 32\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07499179151234613\n",
-      "RMSE Testing = 0.07509033705125467\n",
-      "Out of Bag error = 0.5323148868935752\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07584099063661298\n",
+      "RMSE Testing = 0.07580396577837398\n",
+      "Out of Bag error = 0.5414660381996333\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 33\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545091611345603\n",
-      "RMSE Testing = 0.07554299120785445\n",
-      "Out of Bag error = 0.537071634240084\n",
+      "RMSE Training = 0.07540698077485537\n",
+      "RMSE Testing = 0.07530173457795059\n",
+      "Out of Bag error = 0.5360907623890065\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 34\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07605370712659223\n",
-      "RMSE Testing = 0.07608082747656465\n",
-      "Out of Bag error = 0.5455795620097355\n",
+      "RMSE Training = 0.07581859200854627\n",
+      "RMSE Testing = 0.07591589243408299\n",
+      "Out of Bag error = 0.5407673686894209\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 35\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760790655576517\n",
-      "RMSE Testing = 0.0759896396798175\n",
-      "Out of Bag error = 0.5450167951004771\n",
+      "RMSE Training = 0.07529211184476055\n",
+      "RMSE Testing = 0.07526453446374134\n",
+      "Out of Bag error = 0.5328883064921333\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 36\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0763832149087087\n",
-      "RMSE Testing = 0.07657098783118405\n",
-      "Out of Bag error = 0.5470974498917556\n",
+      "RMSE Training = 0.07565883099995116\n",
+      "RMSE Testing = 0.07575255029759534\n",
+      "Out of Bag error = 0.5403240107848742\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 37\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585103393024265\n",
-      "RMSE Testing = 0.07597207242482736\n",
-      "Out of Bag error = 0.5408806502816212\n",
+      "RMSE Training = 0.07527823779501895\n",
+      "RMSE Testing = 0.0753704080364046\n",
+      "Out of Bag error = 0.5338463601038417\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 38\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07704133322450611\n",
-      "RMSE Testing = 0.07702469758149547\n",
-      "Out of Bag error = 0.558480493776463\n",
+      "RMSE Training = 0.07660899173333309\n",
+      "RMSE Testing = 0.0766329463278887\n",
+      "Out of Bag error = 0.5506570066586556\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 39\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07510800172778873\n",
-      "RMSE Testing = 0.07502000037436724\n",
-      "Out of Bag error = 0.5318724771491956\n",
+      "RMSE Training = 0.07502320533018259\n",
+      "RMSE Testing = 0.07499159734364955\n",
+      "Out of Bag error = 0.5286396564287894\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 40\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07482024911042545\n",
-      "RMSE Testing = 0.07505716705411222\n",
-      "Out of Bag error = 0.5263220655379305\n",
+      "RMSE Training = 0.07612961030986078\n",
+      "RMSE Testing = 0.07618291184907856\n",
+      "Out of Bag error = 0.5456000166522562\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 41\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07565229248383143\n",
-      "RMSE Testing = 0.07573382840549017\n",
-      "Out of Bag error = 0.5378017295522068\n",
+      "RMSE Training = 0.0765427627011733\n",
+      "RMSE Testing = 0.07659227616556834\n",
+      "Out of Bag error = 0.5512650999510613\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 42\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0756441779277265\n",
-      "RMSE Testing = 0.07564938816812526\n",
-      "Out of Bag error = 0.5387521446721655\n",
+      "RMSE Training = 0.07686109693591195\n",
+      "RMSE Testing = 0.07686636426964712\n",
+      "Out of Bag error = 0.5527309216599725\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 43\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07620754965113714\n",
-      "RMSE Testing = 0.07626757070947734\n",
-      "Out of Bag error = 0.5438703016358162\n",
+      "RMSE Training = 0.07652871189684493\n",
+      "RMSE Testing = 0.07649088488461826\n",
+      "Out of Bag error = 0.5493112087166441\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 44\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07621060932235915\n",
-      "RMSE Testing = 0.07631684343117422\n",
-      "Out of Bag error = 0.5434817455649771\n",
+      "RMSE Training = 0.07522467025546517\n",
+      "RMSE Testing = 0.07531022855947056\n",
+      "Out of Bag error = 0.52964624512371\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 45\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07520859511276996\n",
-      "RMSE Testing = 0.07530957205660843\n",
-      "Out of Bag error = 0.530727107287938\n",
+      "RMSE Training = 0.07612949007922372\n",
+      "RMSE Testing = 0.0759937047279512\n",
+      "Out of Bag error = 0.5444717752346881\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 46\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07591157075683377\n",
-      "RMSE Testing = 0.0759848743212733\n",
-      "Out of Bag error = 0.5402067865896735\n",
+      "RMSE Training = 0.07541565981639817\n",
+      "RMSE Testing = 0.07544438582003574\n",
+      "Out of Bag error = 0.5344008492972344\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 47\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07593536643772794\n",
-      "RMSE Testing = 0.0760731318453113\n",
-      "Out of Bag error = 0.5412497128561352\n",
+      "RMSE Training = 0.07619494517118065\n",
+      "RMSE Testing = 0.07630436807038969\n",
+      "Out of Bag error = 0.5443996309755301\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 48\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07445242759490078\n",
-      "RMSE Testing = 0.07437136246367622\n",
-      "Out of Bag error = 0.5204037218252701\n",
+      "RMSE Training = 0.07598770148342765\n",
+      "RMSE Testing = 0.07599742736215458\n",
+      "Out of Bag error = 0.5397128598198202\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 49\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07483012274719172\n",
-      "RMSE Testing = 0.07491465049757567\n",
-      "Out of Bag error = 0.5263393665006246\n",
+      "RMSE Training = 0.07551043966089613\n",
+      "RMSE Testing = 0.07564476353069619\n",
+      "Out of Bag error = 0.534088721401002\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 50\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07559214390492866\n",
-      "RMSE Testing = 0.07579410332482357\n",
-      "Out of Bag error = 0.5339587059739423\n",
+      "RMSE Training = 0.07559806415817905\n",
+      "RMSE Testing = 0.07556214130066383\n",
+      "Out of Bag error = 0.5366649623076131\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 51\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07582173920541133\n",
-      "RMSE Testing = 0.0758020848564908\n",
-      "Out of Bag error = 0.5376980190046557\n",
+      "RMSE Training = 0.07602184170838766\n",
+      "RMSE Testing = 0.07610694112909996\n",
+      "Out of Bag error = 0.5423616955376533\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 52\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07582585144192569\n",
-      "RMSE Testing = 0.07592949197508415\n",
-      "Out of Bag error = 0.5379845197585155\n",
+      "RMSE Training = 0.07568523136832765\n",
+      "RMSE Testing = 0.07569733219655689\n",
+      "Out of Bag error = 0.5376761856536418\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 53\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07579287176516017\n",
-      "RMSE Testing = 0.0758469986232887\n",
-      "Out of Bag error = 0.5387744730070622\n",
+      "RMSE Training = 0.07507825151669564\n",
+      "RMSE Testing = 0.0750519193872969\n",
+      "Out of Bag error = 0.5282498456722899\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 54\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577454216132688\n",
-      "RMSE Testing = 0.0758274548064323\n",
-      "Out of Bag error = 0.5369651141761546\n",
+      "RMSE Training = 0.07591437564008781\n",
+      "RMSE Testing = 0.07598371044142609\n",
+      "Out of Bag error = 0.5386091949291956\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 55\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07550097442010185\n",
-      "RMSE Testing = 0.07556900091663282\n",
-      "Out of Bag error = 0.5338330774200802\n",
+      "RMSE Training = 0.07613186576115889\n",
+      "RMSE Testing = 0.07612920180967472\n",
+      "Out of Bag error = 0.5432903153178866\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 56\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07638641210772812\n",
-      "RMSE Testing = 0.07646687465795936\n",
-      "Out of Bag error = 0.5461636438519452\n",
+      "RMSE Training = 0.07495153932224219\n",
+      "RMSE Testing = 0.07493114861778331\n",
+      "Out of Bag error = 0.5266503085857464\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 57\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07548364240275404\n",
-      "RMSE Testing = 0.07564412108878019\n",
-      "Out of Bag error = 0.534924705506598\n",
+      "RMSE Training = 0.07571481064790261\n",
+      "RMSE Testing = 0.07574777515631527\n",
+      "Out of Bag error = 0.5358272984801802\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 58\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754021688996431\n",
-      "RMSE Testing = 0.07546614426795636\n",
-      "Out of Bag error = 0.5321386378452462\n",
+      "RMSE Training = 0.07586757005448583\n",
+      "RMSE Testing = 0.07573512162316556\n",
+      "Out of Bag error = 0.5378214473792834\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 59\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07493517606964108\n",
-      "RMSE Testing = 0.07508249991053349\n",
-      "Out of Bag error = 0.5255926102510913\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07509053347002595\n",
+      "RMSE Testing = 0.07511705945122882\n",
+      "Out of Bag error = 0.5274328506173831\n",
+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 60\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585180175424878\n",
-      "RMSE Testing = 0.07586193526946577\n",
-      "Out of Bag error = 0.5380584797822021\n",
+      "RMSE Training = 0.07626584353720604\n",
+      "RMSE Testing = 0.07618656118212139\n",
+      "Out of Bag error = 0.5444086612856719\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 61\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0761469963529337\n",
-      "RMSE Testing = 0.07624659446725604\n",
-      "Out of Bag error = 0.5428789674695258\n",
+      "RMSE Training = 0.07476174069482866\n",
+      "RMSE Testing = 0.07475043297143982\n",
+      "Out of Bag error = 0.5237620544789783\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 62\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07596075152851363\n",
-      "RMSE Testing = 0.07605029886666986\n",
-      "Out of Bag error = 0.5396722245928266\n",
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+      "RMSE Testing = 0.07537214558299951\n",
+      "Out of Bag error = 0.5297687064746592\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 63\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07550718442783613\n",
-      "RMSE Testing = 0.07569368198586024\n",
-      "Out of Bag error = 0.5333404010023426\n",
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+      "RMSE Testing = 0.07556394874645363\n",
+      "Out of Bag error = 0.5330323429512324\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 64\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07558012580943793\n",
-      "RMSE Testing = 0.07558895944279784\n",
-      "Out of Bag error = 0.5337559307676754\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07594561665567989\n",
+      "RMSE Testing = 0.07610458108584198\n",
+      "Out of Bag error = 0.5398380725753009\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 65\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07513975503793238\n",
-      "RMSE Testing = 0.07515768203362276\n",
-      "Out of Bag error = 0.527923325941377\n",
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+      "RMSE Testing = 0.07561554860514196\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 66\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07548465874491544\n",
-      "RMSE Testing = 0.07553902108194152\n",
-      "Out of Bag error = 0.5318290099242506\n",
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+      "RMSE Testing = 0.07663150052446871\n",
+      "Out of Bag error = 0.5453992162027341\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 67\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07660241017332556\n",
-      "RMSE Testing = 0.07681983340554882\n",
-      "Out of Bag error = 0.5489015354390332\n",
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+      "RMSE Testing = 0.07603155367928206\n",
+      "Out of Bag error = 0.5386378825154263\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 68\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07619329397476624\n",
-      "RMSE Testing = 0.07633242948035629\n",
-      "Out of Bag error = 0.5428021190337917\n",
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+      "RMSE Testing = 0.0750154782682817\n",
+      "Out of Bag error = 0.5251934611185058\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 69\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07495083729726022\n",
-      "RMSE Testing = 0.075039700295273\n",
-      "Out of Bag error = 0.5256102845049964\n",
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+      "RMSE Testing = 0.07582509958583543\n",
+      "Out of Bag error = 0.5356233358507221\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 70\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545344805916489\n",
-      "RMSE Testing = 0.07538871723800124\n",
-      "Out of Bag error = 0.5319014932900549\n",
+      "RMSE Training = 0.0760922161246296\n",
+      "RMSE Testing = 0.07601276264360977\n",
+      "Out of Bag error = 0.541635280963265\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 71\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545028971186409\n",
-      "RMSE Testing = 0.0754579402042265\n",
-      "Out of Bag error = 0.5310897123411785\n",
+      "RMSE Training = 0.07517674616201744\n",
+      "RMSE Testing = 0.07524506845879798\n",
+      "Out of Bag error = 0.5272803345252208\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 72\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07448277032768796\n",
-      "RMSE Testing = 0.0745470566904322\n",
-      "Out of Bag error = 0.5197015194053779\n",
+      "RMSE Training = 0.07592215167868985\n",
+      "RMSE Testing = 0.07587861287875756\n",
+      "Out of Bag error = 0.5389963460126845\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 73\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07593203408003817\n",
-      "RMSE Testing = 0.07588500314149613\n",
-      "Out of Bag error = 0.5382300216062184\n",
+      "RMSE Training = 0.07597011142076651\n",
+      "RMSE Testing = 0.07594384750535285\n",
+      "Out of Bag error = 0.5384606660221232\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 74\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07578528770090459\n",
-      "RMSE Testing = 0.0758249179247527\n",
-      "Out of Bag error = 0.5363556410316042\n",
+      "RMSE Training = 0.07594940728321684\n",
+      "RMSE Testing = 0.07598255774959825\n",
+      "Out of Bag error = 0.5388670833079569\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 75\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07592941909059345\n",
-      "RMSE Testing = 0.07596584308494124\n",
-      "Out of Bag error = 0.5384862868978324\n",
+      "RMSE Training = 0.07541528922865218\n",
+      "RMSE Testing = 0.07540898008214722\n",
+      "Out of Bag error = 0.5316135644425752\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 76\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07526213540930335\n",
-      "RMSE Testing = 0.07523206053062247\n",
-      "Out of Bag error = 0.5290322183952324\n",
+      "RMSE Training = 0.07612664700804994\n",
+      "RMSE Testing = 0.07617729730126803\n",
+      "Out of Bag error = 0.5414460540061334\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 77\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07581902287505174\n",
-      "RMSE Testing = 0.07604925850444547\n",
-      "Out of Bag error = 0.5360983647116006\n",
+      "RMSE Training = 0.075100925098853\n",
+      "RMSE Testing = 0.07512724675031691\n",
+      "Out of Bag error = 0.5267425765905533\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 78\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07611262771855602\n",
-      "RMSE Testing = 0.07613596105448575\n",
-      "Out of Bag error = 0.5396876069448893\n",
+      "RMSE Training = 0.07553612695069506\n",
+      "RMSE Testing = 0.07557936836836371\n",
+      "Out of Bag error = 0.5325599588846209\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 79\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07530153417800604\n",
-      "RMSE Testing = 0.07545695904518662\n",
-      "Out of Bag error = 0.5311659372096275\n",
+      "RMSE Training = 0.07588207950525587\n",
+      "RMSE Testing = 0.0758868502349137\n",
+      "Out of Bag error = 0.5364755824404401\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 80\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760776163909794\n",
-      "RMSE Testing = 0.07593511740631925\n",
-      "Out of Bag error = 0.5404675137156849\n",
+      "RMSE Training = 0.07512372169600745\n",
+      "RMSE Testing = 0.07510751252222134\n",
+      "Out of Bag error = 0.5279026252040822\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 81\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075944695053495\n",
-      "RMSE Testing = 0.07618793716438117\n",
-      "Out of Bag error = 0.5394629415430467\n",
+      "RMSE Training = 0.07542207764271931\n",
+      "RMSE Testing = 0.07545375435421088\n",
+      "Out of Bag error = 0.53104337230695\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 82\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07546024443622673\n",
-      "RMSE Testing = 0.07550165088632846\n",
-      "Out of Bag error = 0.5309283741350678\n",
+      "RMSE Training = 0.07561189147374342\n",
+      "RMSE Testing = 0.07567440589969913\n",
+      "Out of Bag error = 0.5330229181493398\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 83\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07543336177360657\n",
-      "RMSE Testing = 0.07542615061990927\n",
-      "Out of Bag error = 0.5311846308298648\n",
+      "RMSE Training = 0.07598024790862987\n",
+      "RMSE Testing = 0.07602296151643718\n",
+      "Out of Bag error = 0.5385178952727574\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 84\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755677784392363\n",
-      "RMSE Testing = 0.07563728011260054\n",
-      "Out of Bag error = 0.5332201345469912\n",
+      "RMSE Training = 0.07525072695195001\n",
+      "RMSE Testing = 0.07524072121583276\n",
+      "Out of Bag error = 0.5290353013651472\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 85\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07526698325253305\n",
-      "RMSE Testing = 0.07537818519431129\n",
-      "Out of Bag error = 0.5287574244172433\n",
+      "RMSE Training = 0.07541048725338477\n",
+      "RMSE Testing = 0.07548883982515656\n",
+      "Out of Bag error = 0.5308605375592987\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 86\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07580928645392869\n",
-      "RMSE Testing = 0.07574575180992978\n",
-      "Out of Bag error = 0.5371757263765414\n",
+      "RMSE Training = 0.07561928119651404\n",
+      "RMSE Testing = 0.07563764864979002\n",
+      "Out of Bag error = 0.5328454671329259\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 87\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07514416069889376\n",
-      "RMSE Testing = 0.07518074805225035\n",
-      "Out of Bag error = 0.528045724680254\n",
+      "RMSE Training = 0.07488555989814716\n",
+      "RMSE Testing = 0.07491655223438118\n",
+      "Out of Bag error = 0.5241015227400045\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 88\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07515964460414426\n",
-      "RMSE Testing = 0.07510756254202518\n",
-      "Out of Bag error = 0.5272088989052488\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 89\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755033352778864\n",
-      "RMSE Testing = 0.07562223494186625\n",
-      "Out of Bag error = 0.5319465027282689\n",
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+      "RMSE Testing = 0.07624158498854411\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 90\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07607414322322624\n",
-      "RMSE Testing = 0.07622684493558891\n",
-      "Out of Bag error = 0.5398935150478943\n",
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+      "RMSE Testing = 0.0760381570747993\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 91\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075317083106847\n",
-      "RMSE Testing = 0.07552111717321952\n",
-      "Out of Bag error = 0.5288037381290077\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.0752930698293447\n",
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+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 92\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07620477267392281\n",
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-      "Out of Bag error = 0.5413247434960854\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 93\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07555096964349867\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 94\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07558232208408201\n",
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-      "Out of Bag error = 0.5321055859700753\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 95\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07559004488926935\n",
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-      "Out of Bag error = 0.5324249654259592\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 96\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07499471736854571\n",
-      "RMSE Testing = 0.07502742071490842\n",
-      "Out of Bag error = 0.5253866894493597\n",
-      "-----------------------------------------------------\n",
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+      "Out of Bag error = 0.5274652187961797\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 97\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07580647513012354\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 98\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 99\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 100\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07575919778747828\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 101\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07511759775444084\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 102\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07597171580695902\n",
-      "RMSE Testing = 0.07610354270394652\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 103\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07503511717273881\n",
-      "RMSE Testing = 0.07511560461693227\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 104\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07533730362556357\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 105\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07546039480263508\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 106\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760323930507647\n",
-      "RMSE Testing = 0.07601918285100154\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 107\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07528736216818574\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 108\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07562512449068448\n",
-      "RMSE Testing = 0.07571253087236926\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 109\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07568626072596554\n",
-      "RMSE Testing = 0.07573771839811165\n",
-      "Out of Bag error = 0.5331005256398983\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 110\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07564697191389236\n",
-      "RMSE Testing = 0.0757890927217293\n",
-      "Out of Bag error = 0.5326093959066616\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 111\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07570334223381936\n",
-      "RMSE Testing = 0.07588562091896942\n",
-      "Out of Bag error = 0.5340170463797541\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 112\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07535408815018987\n",
-      "RMSE Testing = 0.07550693048130673\n",
-      "Out of Bag error = 0.5304777075495626\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 113\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07522794975114078\n",
-      "RMSE Testing = 0.0752841401036194\n",
-      "Out of Bag error = 0.5263681497928017\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 114\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754482445066051\n",
-      "RMSE Testing = 0.07551892942489594\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 115\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07554038784803362\n",
-      "RMSE Testing = 0.0755593654294237\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 116\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07593183651893476\n",
-      "RMSE Testing = 0.07609500713506441\n",
-      "Out of Bag error = 0.5377758484907859\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 117\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07537914817146418\n",
-      "RMSE Testing = 0.07549641744127417\n",
-      "Out of Bag error = 0.5292622852957327\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 118\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07600657931592124\n",
-      "RMSE Testing = 0.07611905812620903\n",
-      "Out of Bag error = 0.5373814343488689\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 119\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07551092152880665\n",
-      "RMSE Testing = 0.07555840711358683\n",
-      "Out of Bag error = 0.5322863435650217\n",
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+      "RMSE Testing = 0.0758927594018813\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 120\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07516020719500298\n",
-      "RMSE Testing = 0.07524724905792793\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 121\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07579696497183885\n",
-      "RMSE Testing = 0.07588178792321262\n",
-      "Out of Bag error = 0.53552093295137\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 122\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0756635519030493\n",
-      "RMSE Testing = 0.0756766016763937\n",
-      "Out of Bag error = 0.533061642299923\n",
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+      "RMSE Testing = 0.07510217507803273\n",
+      "Out of Bag error = 0.5237888453882997\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 123\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07576103223788962\n",
-      "RMSE Testing = 0.07577012747352196\n",
-      "Out of Bag error = 0.5343184769789698\n",
-      "-----------------------------------------------------\n"
-     ]
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07489449028242154\n",
+      "RMSE Testing = 0.07487665522847722\n",
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+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 124\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07551382602247195\n",
-      "RMSE Testing = 0.07550459488608056\n",
-      "Out of Bag error = 0.5303192784883604\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 125\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754458075466175\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 126\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07609423196773961\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 127\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07557269734826384\n",
-      "RMSE Testing = 0.0757184736665953\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 128\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07580390703882564\n",
-      "RMSE Testing = 0.07575120819457035\n",
-      "Out of Bag error = 0.5347470374265648\n",
-      "-----------------------------------------------------\n",
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+      "RMSE Testing = 0.07551923096139883\n",
+      "Out of Bag error = 0.5304435570217383\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 129\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 130\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 131\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 132\n",
       "Number of features = 1\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 133\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07576654392600504\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 134\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07524632151986212\n",
-      "RMSE Testing = 0.07525243104706666\n",
-      "Out of Bag error = 0.5277877072000249\n",
+      "RMSE Training = 0.07559779884616169\n",
+      "RMSE Testing = 0.07560160816073516\n",
+      "Out of Bag error = 0.5316323503541571\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 135\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07509161727050231\n",
-      "RMSE Testing = 0.07513963400303762\n",
-      "Out of Bag error = 0.5253190489062248\n",
+      "RMSE Training = 0.07548493157234351\n",
+      "RMSE Testing = 0.07550529550182622\n",
+      "Out of Bag error = 0.530530780743536\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 136\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0758646717742697\n",
-      "RMSE Testing = 0.07598125914760724\n",
-      "Out of Bag error = 0.535353738046289\n",
+      "RMSE Training = 0.07559438594411785\n",
+      "RMSE Testing = 0.07558139085307813\n",
+      "Out of Bag error = 0.5319963526177556\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 137\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07469566627113436\n",
-      "RMSE Testing = 0.07470599671915548\n",
-      "Out of Bag error = 0.5191694669292335\n",
+      "RMSE Training = 0.07606719014891675\n",
+      "RMSE Testing = 0.07614977783627305\n",
+      "Out of Bag error = 0.538516903798707\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 138\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07549939817673074\n",
-      "RMSE Testing = 0.07550539161270639\n",
-      "Out of Bag error = 0.5306874027859225\n",
+      "RMSE Training = 0.07527633850845934\n",
+      "RMSE Testing = 0.07538600153441269\n",
+      "Out of Bag error = 0.5271633389723712\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 139\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07551857972192913\n",
-      "RMSE Testing = 0.07566801332268781\n",
-      "Out of Bag error = 0.5296049516802613\n",
+      "RMSE Training = 0.07508079882959375\n",
+      "RMSE Testing = 0.0751306768314934\n",
+      "Out of Bag error = 0.5251190439243001\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 140\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577547779604596\n",
-      "RMSE Testing = 0.0758243993451162\n",
-      "Out of Bag error = 0.534082059835969\n",
+      "RMSE Training = 0.07542300433417934\n",
+      "RMSE Testing = 0.07546419614000158\n",
+      "Out of Bag error = 0.5289013014862635\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 141\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07585299965186065\n",
-      "RMSE Testing = 0.07590018953192054\n",
-      "Out of Bag error = 0.5364312835156234\n",
+      "RMSE Training = 0.07496146313924734\n",
+      "RMSE Testing = 0.07501728904006791\n",
+      "Out of Bag error = 0.5228636526488961\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 142\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0752891530341262\n",
-      "RMSE Testing = 0.07531443855144906\n",
-      "Out of Bag error = 0.5270632600110174\n",
+      "RMSE Training = 0.07582489051612444\n",
+      "RMSE Testing = 0.07587593625796409\n",
+      "Out of Bag error = 0.5348630001531505\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 143\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07598059140573235\n",
-      "RMSE Testing = 0.07602898385823538\n",
-      "Out of Bag error = 0.5370152049887668\n",
+      "RMSE Training = 0.07582964507586387\n",
+      "RMSE Testing = 0.07584898816518688\n",
+      "Out of Bag error = 0.5350638384572969\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 144\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0760004348537188\n",
-      "RMSE Testing = 0.0760251055574471\n",
-      "Out of Bag error = 0.5370221993017881\n",
+      "RMSE Training = 0.07590722861885048\n",
+      "RMSE Testing = 0.07598903157237517\n",
+      "Out of Bag error = 0.5358977471658709\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 145\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07544556580476432\n",
-      "RMSE Testing = 0.075424112501326\n",
-      "Out of Bag error = 0.5294837070196066\n",
+      "RMSE Training = 0.07579313298178339\n",
+      "RMSE Testing = 0.0757610311132874\n",
+      "Out of Bag error = 0.534624516361842\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 146\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07553417183878355\n",
-      "RMSE Testing = 0.07552005883303728\n",
-      "Out of Bag error = 0.5315984491599296\n",
+      "RMSE Training = 0.07588930954424\n",
+      "RMSE Testing = 0.07593060451900172\n",
+      "Out of Bag error = 0.5362338069025142\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 147\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07519726183967043\n",
-      "RMSE Testing = 0.07527536806221209\n",
-      "Out of Bag error = 0.5244983503412483\n",
+      "RMSE Training = 0.07528990322756395\n",
+      "RMSE Testing = 0.07532430510630686\n",
+      "Out of Bag error = 0.5279142519574018\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 148\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07544945199263443\n",
-      "RMSE Testing = 0.0754918176011963\n",
-      "Out of Bag error = 0.5300994777086873\n",
+      "RMSE Training = 0.07528478324060857\n",
+      "RMSE Testing = 0.07523829848653447\n",
+      "Out of Bag error = 0.5271783010901802\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 149\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07508585385783412\n",
-      "RMSE Testing = 0.07523582814683968\n",
-      "Out of Bag error = 0.5240265428260947\n",
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+      "RMSE Testing = 0.07622900470040014\n",
+      "Out of Bag error = 0.5409951974556859\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 150\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07521615137357378\n",
-      "RMSE Testing = 0.07521525407032933\n",
-      "Out of Bag error = 0.526951859379193\n",
+      "RMSE Training = 0.07511941262921648\n",
+      "RMSE Testing = 0.07507060969089943\n",
+      "Out of Bag error = 0.5254623100098572\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 151\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07577782653552743\n",
-      "RMSE Testing = 0.07583342405047608\n",
-      "Out of Bag error = 0.5338842722823289\n",
+      "RMSE Training = 0.07569658460546116\n",
+      "RMSE Testing = 0.07569565822298001\n",
+      "Out of Bag error = 0.5329566382232824\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 152\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07542070987775251\n",
-      "RMSE Testing = 0.07545548905967299\n",
-      "Out of Bag error = 0.5289807143088211\n",
+      "RMSE Training = 0.07567326221973661\n",
+      "RMSE Testing = 0.07563943989042393\n",
+      "Out of Bag error = 0.5322388275286031\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 153\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07560066466630185\n",
-      "RMSE Testing = 0.07564597126841621\n",
-      "Out of Bag error = 0.5319648325949389\n",
+      "RMSE Training = 0.07569855952899127\n",
+      "RMSE Testing = 0.07579975522817625\n",
+      "Out of Bag error = 0.5327171169642767\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 154\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755750822692732\n",
-      "RMSE Testing = 0.07567669867277127\n",
-      "Out of Bag error = 0.5317477145185909\n",
+      "RMSE Training = 0.07572449128564218\n",
+      "RMSE Testing = 0.07578560164057171\n",
+      "Out of Bag error = 0.5341572307733209\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 155\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754501202190336\n",
-      "RMSE Testing = 0.0755117430392189\n",
-      "Out of Bag error = 0.52965065456122\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07521447659450417\n",
+      "RMSE Testing = 0.07525608874852636\n",
+      "Out of Bag error = 0.5270434844113645\n",
+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 156\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07536182430556722\n",
-      "RMSE Testing = 0.07536827712675692\n",
-      "Out of Bag error = 0.5287533671987179\n",
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+      "RMSE Testing = 0.0752291077844619\n",
+      "Out of Bag error = 0.525280084793879\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 157\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075751942221789\n",
-      "RMSE Testing = 0.07583432980155282\n",
-      "Out of Bag error = 0.5342336003814958\n",
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+      "RMSE Testing = 0.07542656285005786\n",
+      "Out of Bag error = 0.5285491168243385\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 158\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07518274840914574\n",
-      "RMSE Testing = 0.07517968710359282\n",
-      "Out of Bag error = 0.5250831459876747\n",
+      "RMSE Training = 0.07587849864445574\n",
+      "RMSE Testing = 0.07596933170925699\n",
+      "Out of Bag error = 0.536551127424307\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 159\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07542627032156865\n",
-      "RMSE Testing = 0.0754429814888806\n",
-      "Out of Bag error = 0.5287591468780638\n",
+      "RMSE Training = 0.07490980458357696\n",
+      "RMSE Testing = 0.07495831580481196\n",
+      "Out of Bag error = 0.5219740932053988\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 160\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07519152093241258\n",
-      "RMSE Testing = 0.07522233570176137\n",
-      "Out of Bag error = 0.5262431091182425\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07518923077766806\n",
+      "RMSE Testing = 0.07521600869869442\n",
+      "Out of Bag error = 0.526540284163709\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 161\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07575832497991737\n",
-      "RMSE Testing = 0.07582699444626298\n",
-      "Out of Bag error = 0.5332110273709982\n",
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       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 162\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07596837747034454\n",
-      "RMSE Testing = 0.07601815405789811\n",
-      "Out of Bag error = 0.5359393470891554\n",
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+      "Out of Bag error = 0.5318741887995464\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 163\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07564265403822314\n",
-      "RMSE Testing = 0.07563356984917972\n",
-      "Out of Bag error = 0.5331690703630836\n",
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+      "Out of Bag error = 0.5373396808352661\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 164\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07581940934346577\n",
-      "RMSE Testing = 0.0757669218381685\n",
-      "Out of Bag error = 0.5350139467189327\n",
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+      "RMSE Testing = 0.07507408400277832\n",
+      "Out of Bag error = 0.5235400877572312\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 165\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07561513691707351\n",
-      "RMSE Testing = 0.07571986160137059\n",
-      "Out of Bag error = 0.5328844473371355\n",
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+      "RMSE Testing = 0.075846739319953\n",
+      "Out of Bag error = 0.5357796986573564\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 166\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07555105954326045\n",
-      "RMSE Testing = 0.07567488872918465\n",
-      "Out of Bag error = 0.5300897564602116\n",
+      "RMSE Training = 0.07561116367808225\n",
+      "RMSE Testing = 0.07563572326930719\n",
+      "Out of Bag error = 0.5314604242478678\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 167\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07526493587225877\n",
-      "RMSE Testing = 0.07533641214001072\n",
-      "Out of Bag error = 0.5273907466353802\n",
+      "RMSE Training = 0.07526587965188429\n",
+      "RMSE Testing = 0.07529159847897292\n",
+      "Out of Bag error = 0.5277704431817356\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 168\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07548684344459546\n",
-      "RMSE Testing = 0.07554187381780311\n",
-      "Out of Bag error = 0.5309134178996355\n",
+      "RMSE Training = 0.07554905601683537\n",
+      "RMSE Testing = 0.07558880953250043\n",
+      "Out of Bag error = 0.5300329358682286\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 169\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755383282027333\n",
-      "RMSE Testing = 0.07565175173156433\n",
-      "Out of Bag error = 0.5307471695122156\n",
+      "RMSE Training = 0.07566820205932379\n",
+      "RMSE Testing = 0.07573499144100648\n",
+      "Out of Bag error = 0.5329091602602599\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 170\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0751491267525114\n",
-      "RMSE Testing = 0.07513617744587316\n",
-      "Out of Bag error = 0.5249455844361309\n",
+      "RMSE Training = 0.07526938640734773\n",
+      "RMSE Testing = 0.07525800036355565\n",
+      "Out of Bag error = 0.5278125477646618\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 171\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07464751572980277\n",
-      "RMSE Testing = 0.07465969887931148\n",
-      "Out of Bag error = 0.5186518381099707\n",
+      "RMSE Training = 0.07547934049658159\n",
+      "RMSE Testing = 0.07552550230911237\n",
+      "Out of Bag error = 0.5300264821753753\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 172\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.075449061049901\n",
-      "RMSE Testing = 0.07549111234965059\n",
-      "Out of Bag error = 0.5296850723642444\n",
+      "RMSE Training = 0.07576127636529946\n",
+      "RMSE Testing = 0.07571707019293546\n",
+      "Out of Bag error = 0.5342938429337921\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 173\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07503035248298005\n",
-      "RMSE Testing = 0.07513943844042792\n",
-      "Out of Bag error = 0.524090550348806\n",
+      "RMSE Training = 0.07563813500743709\n",
+      "RMSE Testing = 0.07570872435827306\n",
+      "Out of Bag error = 0.5322713500300403\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 174\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07524873107425531\n",
-      "RMSE Testing = 0.07521340865988332\n",
-      "Out of Bag error = 0.5264008961543701\n",
+      "RMSE Training = 0.0751760397867024\n",
+      "RMSE Testing = 0.07521002733111645\n",
+      "Out of Bag error = 0.5264731769782939\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 175\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0753535350749465\n",
-      "RMSE Testing = 0.07537311974400965\n",
-      "Out of Bag error = 0.528004230137531\n",
+      "RMSE Training = 0.07545431139865184\n",
+      "RMSE Testing = 0.07555768434216027\n",
+      "Out of Bag error = 0.529464082219694\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 176\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07552194774404555\n",
-      "RMSE Testing = 0.07560137843242248\n",
-      "Out of Bag error = 0.5309514611470927\n",
+      "RMSE Training = 0.07570976076499292\n",
+      "RMSE Testing = 0.07574034870286997\n",
+      "Out of Bag error = 0.5328394841428661\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 177\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07547346619592746\n",
-      "RMSE Testing = 0.0755432625647683\n",
-      "Out of Bag error = 0.5299019849871802\n",
+      "RMSE Training = 0.07567034042015638\n",
+      "RMSE Testing = 0.07573031097225968\n",
+      "Out of Bag error = 0.5323604448845918\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 178\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07521709772454141\n",
-      "RMSE Testing = 0.07526800517072217\n",
-      "Out of Bag error = 0.5253047572096603\n",
+      "RMSE Training = 0.07526038557058522\n",
+      "RMSE Testing = 0.07525650716176677\n",
+      "Out of Bag error = 0.526089110752282\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 179\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07568185576429258\n",
-      "RMSE Testing = 0.07573073583613842\n",
-      "Out of Bag error = 0.5320329099040034\n",
+      "RMSE Training = 0.07541305044277732\n",
+      "RMSE Testing = 0.07543332164547781\n",
+      "Out of Bag error = 0.5287226897221448\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 180\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07528925333014029\n",
-      "RMSE Testing = 0.07532925871595453\n",
-      "Out of Bag error = 0.5274126555294465\n",
+      "RMSE Training = 0.07535735959500761\n",
+      "RMSE Testing = 0.07528217901599711\n",
+      "Out of Bag error = 0.5281843928545433\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 181\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0752665607748491\n",
-      "RMSE Testing = 0.07523008960681767\n",
-      "Out of Bag error = 0.5263849539668879\n",
+      "RMSE Training = 0.07583166915872945\n",
+      "RMSE Testing = 0.07578634921392291\n",
+      "Out of Bag error = 0.5343280606978761\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 182\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0755097439837491\n",
-      "RMSE Testing = 0.07559701440754722\n",
-      "Out of Bag error = 0.5299322164182689\n",
+      "RMSE Training = 0.07600591391402833\n",
+      "RMSE Testing = 0.07606090810066596\n",
+      "Out of Bag error = 0.5366394855517144\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 183\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07571763359155233\n",
-      "RMSE Testing = 0.07582358743890905\n",
-      "Out of Bag error = 0.5335558241727628\n",
+      "RMSE Training = 0.07544775633330911\n",
+      "RMSE Testing = 0.07556391619806617\n",
+      "Out of Bag error = 0.5287867956132797\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 184\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07596139331313315\n",
-      "RMSE Testing = 0.07605458252988606\n",
-      "Out of Bag error = 0.5358848018193639\n",
+      "RMSE Training = 0.07548608528258915\n",
+      "RMSE Testing = 0.07553266702050462\n",
+      "Out of Bag error = 0.5300999201415089\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 185\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07568088627653713\n",
-      "RMSE Testing = 0.0756793860654808\n",
-      "Out of Bag error = 0.5322047536481868\n",
+      "RMSE Training = 0.07585327656401604\n",
+      "RMSE Testing = 0.07585056945901508\n",
+      "Out of Bag error = 0.5348833177792065\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 186\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07507514707213\n",
-      "RMSE Testing = 0.07513213752118848\n",
-      "Out of Bag error = 0.5240657408092674\n",
+      "RMSE Training = 0.07554914876761624\n",
+      "RMSE Testing = 0.07555626082162062\n",
+      "Out of Bag error = 0.5303077574084444\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 187\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07574663665272587\n",
-      "RMSE Testing = 0.07573871188987572\n",
-      "Out of Bag error = 0.5333884178348999\n",
-      "-----------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "RMSE Training = 0.07524189000258305\n",
+      "RMSE Testing = 0.07525693349135831\n",
+      "Out of Bag error = 0.5267442609800217\n",
+      "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 188\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07543836772090402\n",
-      "RMSE Testing = 0.07550089779341042\n",
-      "Out of Bag error = 0.5292383670784413\n",
+      "RMSE Training = 0.07530149647278658\n",
+      "RMSE Testing = 0.07533511890957867\n",
+      "Out of Bag error = 0.5272509807542989\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 189\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07542761003833338\n",
-      "RMSE Testing = 0.07547695903646885\n",
-      "Out of Bag error = 0.5292038764363014\n",
+      "RMSE Training = 0.07553079354365064\n",
+      "RMSE Testing = 0.07554667075646676\n",
+      "Out of Bag error = 0.5307557784708414\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 190\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07565283260323509\n",
-      "RMSE Testing = 0.07566057902458635\n",
-      "Out of Bag error = 0.5330652619357432\n",
+      "RMSE Training = 0.07525446135450481\n",
+      "RMSE Testing = 0.07521828171188447\n",
+      "Out of Bag error = 0.5266782390328086\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 191\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07541953382251464\n",
-      "RMSE Testing = 0.07539555471134284\n",
-      "Out of Bag error = 0.5293957845824322\n",
+      "RMSE Training = 0.07576889559332296\n",
+      "RMSE Testing = 0.07578884009444416\n",
+      "Out of Bag error = 0.5340800301289093\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 192\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07543344216446879\n",
-      "RMSE Testing = 0.075474890251476\n",
-      "Out of Bag error = 0.5292450426919462\n",
-      "-----------------------------------------------------\n",
+      "RMSE Training = 0.07554099617224819\n",
+      "RMSE Testing = 0.07554777691190619\n",
+      "Out of Bag error = 0.5296599095865839\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "-----------------------------------------------------\n",
       "Number of trees = 193\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07532046531609998\n",
-      "RMSE Testing = 0.07540775544829445\n",
-      "Out of Bag error = 0.5274217007429486\n",
+      "RMSE Training = 0.07585600595961418\n",
+      "RMSE Testing = 0.07586840577822077\n",
+      "Out of Bag error = 0.535299987894567\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 194\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07570966340398066\n",
-      "RMSE Testing = 0.07579886599744201\n",
-      "Out of Bag error = 0.5325330766487422\n",
+      "RMSE Training = 0.07543411816904709\n",
+      "RMSE Testing = 0.0754509029706389\n",
+      "Out of Bag error = 0.5299034118787473\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 195\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07575716116038005\n",
-      "RMSE Testing = 0.07577302255840493\n",
-      "Out of Bag error = 0.5341217708224284\n",
+      "RMSE Training = 0.0756142594705356\n",
+      "RMSE Testing = 0.07565605619613076\n",
+      "Out of Bag error = 0.5323111909474576\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 196\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07601031654619551\n",
-      "RMSE Testing = 0.07604723714506438\n",
-      "Out of Bag error = 0.5364578015696316\n",
+      "RMSE Training = 0.07503585863167599\n",
+      "RMSE Testing = 0.0751058599319214\n",
+      "Out of Bag error = 0.5232014304948289\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 197\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07545140434553027\n",
-      "RMSE Testing = 0.07551499640537154\n",
-      "Out of Bag error = 0.529292702199709\n",
+      "RMSE Training = 0.07533246819686014\n",
+      "RMSE Testing = 0.07534299616029376\n",
+      "Out of Bag error = 0.5277539388363498\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 198\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.0754492383733348\n",
-      "RMSE Testing = 0.07557081629340981\n",
-      "Out of Bag error = 0.5292337564559814\n",
+      "RMSE Training = 0.07522045040463712\n",
+      "RMSE Testing = 0.07523951896296646\n",
+      "Out of Bag error = 0.5260775320915072\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 199\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07560427538765978\n",
-      "RMSE Testing = 0.0756817560801624\n",
-      "Out of Bag error = 0.531347607452984\n",
+      "RMSE Training = 0.0759499813172658\n",
+      "RMSE Testing = 0.07600676361917018\n",
+      "Out of Bag error = 0.5352738806959693\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
       "Number of trees = 200\n",
       "Number of features = 1\n",
-      "RMSE Training = 0.07556424778431499\n",
-      "RMSE Testing = 0.07562011411256366\n",
-      "Out of Bag error = 0.5303383076185137\n",
+      "RMSE Training = 0.07553043961035649\n",
+      "RMSE Testing = 0.0755970238144939\n",
+      "Out of Bag error = 0.5307622758634305\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.07178652050458452\n",
+      "RMSE Testing = 0.07178200004422779\n",
+      "Out of Bag error = 1.0356267901315057\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0726697834068104\n",
+      "RMSE Testing = 0.07308400704216603\n",
+      "Out of Bag error = 0.8592995069973972\n",
       "-----------------------------------------------------\n"
      ]
     },
     {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06487242306434574\n",
+      "RMSE Testing = 0.06451966982478567\n",
+      "Out of Bag error = 0.6787140736816983\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0644133809511976\n",
+      "RMSE Testing = 0.064661660735457\n",
+      "Out of Bag error = 0.5829130983855079\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0663598040395997\n",
+      "RMSE Testing = 0.06682981965883114\n",
+      "Out of Bag error = 0.5553033135410621\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06519051388152254\n",
+      "RMSE Testing = 0.06462270035134408\n",
+      "Out of Bag error = 0.5105767700601486\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0662196346450886\n",
+      "RMSE Testing = 0.06632569903368388\n",
+      "Out of Bag error = 0.4909468012970565\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06622372357429944\n",
+      "RMSE Testing = 0.06615809942121673\n",
+      "Out of Bag error = 0.4684481073230691\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06434115889543943\n",
+      "RMSE Testing = 0.06428319661123529\n",
+      "Out of Bag error = 0.4369225878897531\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06512096032761854\n",
+      "RMSE Testing = 0.06530886838787078\n",
+      "Out of Bag error = 0.4334347210704312\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06430791734155433\n",
+      "RMSE Testing = 0.06412010408765621\n",
+      "Out of Bag error = 0.4175385710403421\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06505826462743876\n",
+      "RMSE Testing = 0.06478348740750697\n",
+      "Out of Bag error = 0.41916837216229197\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06484481459821478\n",
+      "RMSE Testing = 0.06480105566516324\n",
+      "Out of Bag error = 0.41579271024683007\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0642547747464278\n",
+      "RMSE Testing = 0.06401117330414106\n",
+      "Out of Bag error = 0.40910130060997174\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06436675614441278\n",
+      "RMSE Testing = 0.06407040976893327\n",
+      "Out of Bag error = 0.4037411212200661\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06390500870817678\n",
+      "RMSE Testing = 0.06396442350234294\n",
+      "Out of Bag error = 0.3993135463826897\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06511954501375637\n",
+      "RMSE Testing = 0.0650432562587933\n",
+      "Out of Bag error = 0.41099033717619193\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06487464959261156\n",
+      "RMSE Testing = 0.0648198658129944\n",
+      "Out of Bag error = 0.40680368135490824\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06362128189895408\n",
+      "RMSE Testing = 0.06365153654423275\n",
+      "Out of Bag error = 0.3908470464974198\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06215976611859999\n",
+      "RMSE Testing = 0.061999044921853964\n",
+      "Out of Bag error = 0.3713279696234391\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06557541526636546\n",
+      "RMSE Testing = 0.06573811694814168\n",
+      "Out of Bag error = 0.4118586293363878\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06616864279425463\n",
+      "RMSE Testing = 0.06636917574584694\n",
+      "Out of Bag error = 0.4205290160009133\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06459936873664758\n",
+      "RMSE Testing = 0.0646836264797117\n",
+      "Out of Bag error = 0.4001268313177896\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06480807125517422\n",
+      "RMSE Testing = 0.06479376896372703\n",
+      "Out of Bag error = 0.400757158343614\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06364801512176574\n",
+      "RMSE Testing = 0.06373716818129668\n",
+      "Out of Bag error = 0.3865154653635585\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06486145155546025\n",
+      "RMSE Testing = 0.06492146568425641\n",
+      "Out of Bag error = 0.4009342596891525\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06430086590674514\n",
+      "RMSE Testing = 0.0642827612311269\n",
+      "Out of Bag error = 0.3933189498615797\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06511240375853918\n",
+      "RMSE Testing = 0.06491288401663023\n",
+      "Out of Bag error = 0.40361938738509606\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06352262784653906\n",
+      "RMSE Testing = 0.06347215596536432\n",
+      "Out of Bag error = 0.3830497223854249\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06450185011422013\n",
+      "RMSE Testing = 0.06454044515427419\n",
+      "Out of Bag error = 0.39433857076111345\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06416644479346552\n",
+      "RMSE Testing = 0.06420587550875544\n",
+      "Out of Bag error = 0.39156595781872333\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 32\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06446683994288313\n",
+      "RMSE Testing = 0.06434757485427642\n",
+      "Out of Bag error = 0.3926509477259156\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 33\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06441247030027095\n",
+      "RMSE Testing = 0.06453441642732527\n",
+      "Out of Bag error = 0.39221102094753507\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 34\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06458472136515386\n",
+      "RMSE Testing = 0.06461250260835895\n",
+      "Out of Bag error = 0.39248598880930136\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 35\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06492953101299971\n",
+      "RMSE Testing = 0.06508067291025563\n",
+      "Out of Bag error = 0.3997254194143144\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 36\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06331290056253389\n",
+      "RMSE Testing = 0.06332521394662652\n",
+      "Out of Bag error = 0.3796634690808921\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 37\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06438373221214208\n",
+      "RMSE Testing = 0.06433133835911278\n",
+      "Out of Bag error = 0.3914653814300143\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 38\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06512173419363478\n",
+      "RMSE Testing = 0.0650264324805011\n",
+      "Out of Bag error = 0.39925938078390716\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 39\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06517220159094442\n",
+      "RMSE Testing = 0.06516749239784043\n",
+      "Out of Bag error = 0.4019999979376598\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 40\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0648387145506503\n",
+      "RMSE Testing = 0.06493096694285816\n",
+      "Out of Bag error = 0.397363636364967\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 41\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06474820110088149\n",
+      "RMSE Testing = 0.06464317926388186\n",
+      "Out of Bag error = 0.39507951038316297\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 42\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06334185538614115\n",
+      "RMSE Testing = 0.06344131044133298\n",
+      "Out of Bag error = 0.37845666719867366\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 43\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06408228384896968\n",
+      "RMSE Testing = 0.06414785015732677\n",
+      "Out of Bag error = 0.38534614022487435\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 44\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0641358167604925\n",
+      "RMSE Testing = 0.06401958892346403\n",
+      "Out of Bag error = 0.3876016131454201\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 45\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06425533878393569\n",
+      "RMSE Testing = 0.06423080606161576\n",
+      "Out of Bag error = 0.3884665558413696\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 46\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06422880448156444\n",
+      "RMSE Testing = 0.06430218243323338\n",
+      "Out of Bag error = 0.3884496811311422\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 47\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.0644130912147063\n",
+      "RMSE Testing = 0.06440838806845307\n",
+      "Out of Bag error = 0.39071957785973593\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 48\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06446185258265448\n",
+      "RMSE Testing = 0.06454226468213035\n",
+      "Out of Bag error = 0.3905410098668532\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 49\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06460046565121814\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 50\n",
+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "Out of Bag error = 0.37711608722313494\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 122\n",
+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
+      "RMSE Training = 0.06436073501109733\n",
+      "RMSE Testing = 0.06438803051921273\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 124\n",
+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "Number of trees = 125\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Out of Bag error = 0.38251274330219254\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "Number of features = 2\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "RMSE Testing = 0.06378819001862615\n",
+      "Out of Bag error = 0.3803633844231918\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 198\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06457782837636127\n",
+      "RMSE Testing = 0.06453505382232247\n",
+      "Out of Bag error = 0.38803048637685233\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 199\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06393745139275225\n",
+      "RMSE Testing = 0.06398972210665098\n",
+      "Out of Bag error = 0.3809930802135987\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 200\n",
+      "Number of features = 2\n",
+      "RMSE Training = 0.06430447695533062\n",
+      "RMSE Testing = 0.06429056587197565\n",
+      "Out of Bag error = 0.38463565335483946\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06553401012972591\n",
+      "RMSE Testing = 0.06550804447191212\n",
+      "Out of Bag error = 0.9908918423885418\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06387133042837698\n",
+      "RMSE Testing = 0.06451192024000502\n",
+      "Out of Bag error = 0.784177451957365\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061202182678221674\n",
+      "RMSE Testing = 0.06121238092300961\n",
+      "Out of Bag error = 0.6240928441127434\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.059894405324473654\n",
+      "RMSE Testing = 0.059656244194449384\n",
+      "Out of Bag error = 0.5281402117824234\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06138008488929333\n",
+      "RMSE Testing = 0.061709006034034855\n",
+      "Out of Bag error = 0.4874484801391616\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061923913272474165\n",
+      "RMSE Testing = 0.06164440082534928\n",
+      "Out of Bag error = 0.44906090621213773\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06164181287418358\n",
+      "RMSE Testing = 0.06149302032079954\n",
+      "Out of Bag error = 0.4200279728394487\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061698016479165264\n",
+      "RMSE Testing = 0.06150613201623101\n",
+      "Out of Bag error = 0.39892114483185404\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060935816472520256\n",
+      "RMSE Testing = 0.0609529284673679\n",
+      "Out of Bag error = 0.37978340465360527\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06127626635171092\n",
+      "RMSE Testing = 0.06135688488768567\n",
+      "Out of Bag error = 0.3777089714168403\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060073485315508136\n",
+      "RMSE Testing = 0.06009569282024065\n",
+      "Out of Bag error = 0.35819510706166297\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06141701157037845\n",
+      "RMSE Testing = 0.061365552055427555\n",
+      "Out of Bag error = 0.3718622843964467\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06132937024780869\n",
+      "RMSE Testing = 0.06105013722351488\n",
+      "Out of Bag error = 0.36387576408508016\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.0617761665389836\n",
+      "RMSE Testing = 0.06200491458424704\n",
+      "Out of Bag error = 0.371484348422994\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.059910818075094775\n",
+      "RMSE Testing = 0.05970959090253096\n",
+      "Out of Bag error = 0.348452090012462\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.061197908451467664\n",
+      "RMSE Testing = 0.06114343412960055\n",
+      "Out of Bag error = 0.3603435947838507\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060809465513254335\n",
+      "RMSE Testing = 0.06092085145838756\n",
+      "Out of Bag error = 0.3519935020158699\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06065813972233147\n",
+      "RMSE Testing = 0.06062475790828999\n",
+      "Out of Bag error = 0.35155417890115526\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06094977746338827\n",
+      "RMSE Testing = 0.06089806474481897\n",
+      "Out of Bag error = 0.3538944786111735\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06034146644632125\n",
+      "RMSE Testing = 0.06028089437117893\n",
+      "Out of Bag error = 0.34760298500235715\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06140212329287655\n",
+      "RMSE Testing = 0.06131345506190292\n",
+      "Out of Bag error = 0.3581428652125612\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06063846173170615\n",
+      "RMSE Testing = 0.060598510153520836\n",
+      "Out of Bag error = 0.35010134184588815\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06105689861615602\n",
+      "RMSE Testing = 0.0609852755258435\n",
+      "Out of Bag error = 0.3536024145647128\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06141989592914422\n",
+      "RMSE Testing = 0.06138488651079797\n",
+      "Out of Bag error = 0.35686888297736785\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06048517956058511\n",
+      "RMSE Testing = 0.060381439485088576\n",
+      "Out of Bag error = 0.3460494064825067\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06102609018043307\n",
+      "RMSE Testing = 0.06093999156621187\n",
+      "Out of Bag error = 0.35115264487337966\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06081501914167149\n",
+      "RMSE Testing = 0.06086725558003485\n",
+      "Out of Bag error = 0.34995281985083226\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060351737654026204\n",
+      "RMSE Testing = 0.06026179512820079\n",
+      "Out of Bag error = 0.34395089361486464\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06025591663894929\n",
+      "RMSE Testing = 0.06022577999308729\n",
+      "Out of Bag error = 0.341515111267\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060375685119655076\n",
+      "RMSE Testing = 0.06019375744622302\n",
+      "Out of Bag error = 0.3451857687826104\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
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+      "RMSE Testing = 0.060834482910836665\n",
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+      "-----------------------------------------------------\n",
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+      "Number of trees = 32\n",
+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "Number of trees = 37\n",
+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "Number of features = 3\n",
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+      "Out of Bag error = 0.3421095200871126\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 105\n",
+      "Number of features = 3\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "RMSE Training = 0.0601161569441027\n",
+      "RMSE Testing = 0.060039651872003594\n",
+      "Out of Bag error = 0.3363063784816626\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 180\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06037055517154151\n",
+      "RMSE Testing = 0.060244232798847906\n",
+      "Out of Bag error = 0.3386949425761038\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 181\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060449172638294336\n",
+      "RMSE Testing = 0.06031052428571754\n",
+      "Out of Bag error = 0.34027600801633806\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 182\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06014420794953651\n",
+      "RMSE Testing = 0.06007856140797372\n",
+      "Out of Bag error = 0.33641739483327104\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 183\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06014758697180652\n",
+      "RMSE Testing = 0.06010694939765578\n",
+      "Out of Bag error = 0.33591597328879075\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 184\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06023139907559093\n",
+      "RMSE Testing = 0.060173406414336605\n",
+      "Out of Bag error = 0.3367623394201132\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 185\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060370214939271596\n",
+      "RMSE Testing = 0.06031444824592823\n",
+      "Out of Bag error = 0.339010881179527\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 186\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060192776143814096\n",
+      "RMSE Testing = 0.06013591104827516\n",
+      "Out of Bag error = 0.3367175578655172\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 187\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06019330609577145\n",
+      "RMSE Testing = 0.06017079911524701\n",
+      "Out of Bag error = 0.3369526219175011\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 188\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060163587702625164\n",
+      "RMSE Testing = 0.060163186708657335\n",
+      "Out of Bag error = 0.3369376702658231\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 189\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060227918753759144\n",
+      "RMSE Testing = 0.06011396628218755\n",
+      "Out of Bag error = 0.3375215483558187\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 190\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060336974134015856\n",
+      "RMSE Testing = 0.06029178455444939\n",
+      "Out of Bag error = 0.3385432431311683\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 191\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06022098522327355\n",
+      "RMSE Testing = 0.06008635289680366\n",
+      "Out of Bag error = 0.3366435120814636\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 192\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.059928935199137556\n",
+      "RMSE Testing = 0.05985764204616641\n",
+      "Out of Bag error = 0.33383012697262415\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 193\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060524834600750546\n",
+      "RMSE Testing = 0.06049595406071562\n",
+      "Out of Bag error = 0.34036345732852596\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 194\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06030523233714351\n",
+      "RMSE Testing = 0.06021839551200955\n",
+      "Out of Bag error = 0.33772313714402846\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 195\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060226891126613336\n",
+      "RMSE Testing = 0.0601804804108637\n",
+      "Out of Bag error = 0.33676349174013376\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 196\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06030489654929433\n",
+      "RMSE Testing = 0.06030600425288481\n",
+      "Out of Bag error = 0.3380837995603131\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 197\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06023822339776932\n",
+      "RMSE Testing = 0.06017626884793763\n",
+      "Out of Bag error = 0.33749637250859343\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 198\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.06029938681903951\n",
+      "RMSE Testing = 0.060230350032389815\n",
+      "Out of Bag error = 0.3376266516997296\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 199\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060240773381599944\n",
+      "RMSE Testing = 0.06017051094806937\n",
+      "Out of Bag error = 0.337309379880469\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 200\n",
+      "Number of features = 3\n",
+      "RMSE Training = 0.060629429089982256\n",
+      "RMSE Testing = 0.060618103840524476\n",
+      "Out of Bag error = 0.3414631202236917\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06622081557708329\n",
+      "RMSE Testing = 0.06613917761372051\n",
+      "Out of Bag error = 0.9989227052768015\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0624149760545714\n",
+      "RMSE Testing = 0.0625513990489035\n",
+      "Out of Bag error = 0.768686850974345\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06247988883026455\n",
+      "RMSE Testing = 0.06204564554594899\n",
+      "Out of Bag error = 0.61496979821353\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0615014623675554\n",
+      "RMSE Testing = 0.06159492015431214\n",
+      "Out of Bag error = 0.5278360340266047\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06160913156835167\n",
+      "RMSE Testing = 0.061383865268429504\n",
+      "Out of Bag error = 0.46251462122022124\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06132364737970385\n",
+      "RMSE Testing = 0.06130166113151372\n",
+      "Out of Bag error = 0.42674838784162505\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06123026111351374\n",
+      "RMSE Testing = 0.06110618248553681\n",
+      "Out of Bag error = 0.3961969015271327\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06155600535835478\n",
+      "RMSE Testing = 0.06126807421089649\n",
+      "Out of Bag error = 0.3878838937484169\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06167336302116192\n",
+      "RMSE Testing = 0.06158846594006345\n",
+      "Out of Bag error = 0.3726181914456862\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091190741063583\n",
+      "RMSE Testing = 0.06086388827637057\n",
+      "Out of Bag error = 0.3610159741723852\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06130223343992548\n",
+      "RMSE Testing = 0.06128178958743534\n",
+      "Out of Bag error = 0.35927355812537504\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06116055054491849\n",
+      "RMSE Testing = 0.06106334541168342\n",
+      "Out of Bag error = 0.3549954834185199\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060998430514659675\n",
+      "RMSE Testing = 0.06079251958832349\n",
+      "Out of Bag error = 0.35299582332932067\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060836362126178455\n",
+      "RMSE Testing = 0.06066256261776468\n",
+      "Out of Bag error = 0.3498292952300833\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060873163507187744\n",
+      "RMSE Testing = 0.060808667149011286\n",
+      "Out of Bag error = 0.3497604866762827\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06061544757216536\n",
+      "RMSE Testing = 0.06057025144962257\n",
+      "Out of Bag error = 0.3458474368297687\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084252727042494\n",
+      "RMSE Testing = 0.06080246092048817\n",
+      "Out of Bag error = 0.34660515660765095\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06079823687803676\n",
+      "RMSE Testing = 0.06070200330774241\n",
+      "Out of Bag error = 0.3453835586718533\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060671217058017236\n",
+      "RMSE Testing = 0.06052508423873683\n",
+      "Out of Bag error = 0.3451124217227414\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06133128374509785\n",
+      "RMSE Testing = 0.06126421367209876\n",
+      "Out of Bag error = 0.3506528790102406\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084132600511309\n",
+      "RMSE Testing = 0.06071607826143214\n",
+      "Out of Bag error = 0.34544187451641445\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.061091267021912955\n",
+      "RMSE Testing = 0.06103577145569168\n",
+      "Out of Bag error = 0.3486954683569904\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06121797121682471\n",
+      "RMSE Testing = 0.06108657953810035\n",
+      "Out of Bag error = 0.3490989334514982\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096410487616257\n",
+      "RMSE Testing = 0.060961726092832166\n",
+      "Out of Bag error = 0.3466600878678811\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094814300147363\n",
+      "RMSE Testing = 0.060898995141142845\n",
+      "Out of Bag error = 0.3468295416027555\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.061004208841215025\n",
+      "RMSE Testing = 0.060921125221089424\n",
+      "Out of Bag error = 0.34684226925772954\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06113235948900906\n",
+      "RMSE Testing = 0.06102763495972748\n",
+      "Out of Bag error = 0.34804506463535495\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06090459775712141\n",
+      "RMSE Testing = 0.06084043281210004\n",
+      "Out of Bag error = 0.34542219151867265\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060708310704548255\n",
+      "RMSE Testing = 0.060637151549991705\n",
+      "Out of Bag error = 0.34320478303819374\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060988187086323566\n",
+      "RMSE Testing = 0.06086628412812807\n",
+      "Out of Bag error = 0.3463913425874309\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06095907901613247\n",
+      "RMSE Testing = 0.06079365754064634\n",
+      "Out of Bag error = 0.34664907979702814\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 32\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096464696630373\n",
+      "RMSE Testing = 0.06095371703892871\n",
+      "Out of Bag error = 0.3464643014684583\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 33\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06086266180185784\n",
+      "RMSE Testing = 0.060764766858585485\n",
+      "Out of Bag error = 0.34486150213068767\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 34\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06095714464053971\n",
+      "RMSE Testing = 0.06082074291319921\n",
+      "Out of Bag error = 0.3449102624618317\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 35\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06074326171351361\n",
+      "RMSE Testing = 0.06066023581446567\n",
+      "Out of Bag error = 0.34314904339924696\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 36\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096622595004042\n",
+      "RMSE Testing = 0.06089038333182094\n",
+      "Out of Bag error = 0.34622912011633733\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 37\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060985691100965946\n",
+      "RMSE Testing = 0.06087853093517955\n",
+      "Out of Bag error = 0.3471187906460547\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 38\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085587302042863\n",
+      "RMSE Testing = 0.060703517520556735\n",
+      "Out of Bag error = 0.3452735742882294\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 39\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091170459712678\n",
+      "RMSE Testing = 0.060830817126428814\n",
+      "Out of Bag error = 0.34517408323035487\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 40\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096999038724219\n",
+      "RMSE Testing = 0.06086437952795707\n",
+      "Out of Bag error = 0.345131248829713\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 41\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081101520606898\n",
+      "RMSE Testing = 0.060810976927114055\n",
+      "Out of Bag error = 0.3439693362626654\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 42\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087901355872799\n",
+      "RMSE Testing = 0.06079092227620173\n",
+      "Out of Bag error = 0.3442874174280868\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 43\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094233446051818\n",
+      "RMSE Testing = 0.06088310131159382\n",
+      "Out of Bag error = 0.345226366638104\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 44\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06092070722437748\n",
+      "RMSE Testing = 0.06075954145837166\n",
+      "Out of Bag error = 0.34497468250435565\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 45\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06089276516672583\n",
+      "RMSE Testing = 0.06078989599552405\n",
+      "Out of Bag error = 0.34475924105183436\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 46\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094984799062038\n",
+      "RMSE Testing = 0.06089880084940855\n",
+      "Out of Bag error = 0.344680490462824\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 47\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06094219814640235\n",
+      "RMSE Testing = 0.06083458156078762\n",
+      "Out of Bag error = 0.3449351950125156\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 48\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088401674427648\n",
+      "RMSE Testing = 0.060800812224900545\n",
+      "Out of Bag error = 0.34419154255250406\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 49\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0610020109190267\n",
+      "RMSE Testing = 0.06088586957703791\n",
+      "Out of Bag error = 0.3447302480650438\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 50\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060878064570425106\n",
+      "RMSE Testing = 0.060801196579623\n",
+      "Out of Bag error = 0.3444074427831239\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 51\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096278557838253\n",
+      "RMSE Testing = 0.060820274824663956\n",
+      "Out of Bag error = 0.3454697505132665\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 52\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06096978162253482\n",
+      "RMSE Testing = 0.06093085511950971\n",
+      "Out of Bag error = 0.34556644600555925\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 53\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085870219213346\n",
+      "RMSE Testing = 0.060817915650445566\n",
+      "Out of Bag error = 0.3440971324520146\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 54\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060854764191413356\n",
+      "RMSE Testing = 0.06074312349605038\n",
+      "Out of Bag error = 0.3443843295474925\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 55\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060932077157694685\n",
+      "RMSE Testing = 0.06080227653158256\n",
+      "Out of Bag error = 0.3447215495845441\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 56\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091473886990663\n",
+      "RMSE Testing = 0.06087773746319691\n",
+      "Out of Bag error = 0.34430633281462997\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 57\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087993650755384\n",
+      "RMSE Testing = 0.060745173856172584\n",
+      "Out of Bag error = 0.3443967680478708\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 58\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060881082024453206\n",
+      "RMSE Testing = 0.0607774610198097\n",
+      "Out of Bag error = 0.34386948980296017\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 59\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0609442675579695\n",
+      "RMSE Testing = 0.06081544737631931\n",
+      "Out of Bag error = 0.3444909230392723\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 60\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06091191272245984\n",
+      "RMSE Testing = 0.060757097023564556\n",
+      "Out of Bag error = 0.3448358413451369\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 61\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088583105337644\n",
+      "RMSE Testing = 0.06082686645694378\n",
+      "Out of Bag error = 0.3437289129625448\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 62\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060846337758554594\n",
+      "RMSE Testing = 0.06075384556867226\n",
+      "Out of Bag error = 0.34354834337194223\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 63\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06104941019136205\n",
+      "RMSE Testing = 0.060982411648140056\n",
+      "Out of Bag error = 0.3465583155648016\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 64\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060944312414737065\n",
+      "RMSE Testing = 0.06077570406568274\n",
+      "Out of Bag error = 0.34495995028114806\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 65\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06066086271218202\n",
+      "RMSE Testing = 0.060575294257608256\n",
+      "Out of Bag error = 0.3414713242143452\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 66\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060757381077378256\n",
+      "RMSE Testing = 0.06069288756181383\n",
+      "Out of Bag error = 0.3429616214390498\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 67\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060932352265966175\n",
+      "RMSE Testing = 0.06081438053984998\n",
+      "Out of Bag error = 0.34393790928729173\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 68\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060929834871926346\n",
+      "RMSE Testing = 0.06083920610277969\n",
+      "Out of Bag error = 0.3443783575166787\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 69\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06099840157811761\n",
+      "RMSE Testing = 0.0607838234519764\n",
+      "Out of Bag error = 0.3449747264743199\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 70\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060800272049978996\n",
+      "RMSE Testing = 0.06073180500792824\n",
+      "Out of Bag error = 0.3430160464489288\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 71\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06070983930408931\n",
+      "RMSE Testing = 0.06057529207086902\n",
+      "Out of Bag error = 0.342344260332444\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 72\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06092270673191047\n",
+      "RMSE Testing = 0.06081409054242182\n",
+      "Out of Bag error = 0.34442904521774836\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 73\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060995260399529495\n",
+      "RMSE Testing = 0.060932436663091075\n",
+      "Out of Bag error = 0.34479290854781536\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 74\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06071502880298243\n",
+      "RMSE Testing = 0.060586666688430116\n",
+      "Out of Bag error = 0.3421848466524061\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 75\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080599109303\n",
+      "RMSE Testing = 0.06076163128225607\n",
+      "Out of Bag error = 0.34318587629386427\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 76\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060890710954498495\n",
+      "RMSE Testing = 0.06081032897856836\n",
+      "Out of Bag error = 0.34381432650499827\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 77\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084619210670975\n",
+      "RMSE Testing = 0.06078689937883112\n",
+      "Out of Bag error = 0.34323646827541776\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 78\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0608453223373421\n",
+      "RMSE Testing = 0.06073382949842623\n",
+      "Out of Bag error = 0.3438515048255629\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 79\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06095845929514655\n",
+      "RMSE Testing = 0.06087158240714561\n",
+      "Out of Bag error = 0.3445310767556437\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 80\n",
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+      "Number of trees = 81\n",
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+      "-----------------------------------------------------\n"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 154\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060870415954317844\n",
+      "RMSE Testing = 0.060793815298246\n",
+      "Out of Bag error = 0.3432453779952319\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 155\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084843225359097\n",
+      "RMSE Testing = 0.060768465246795224\n",
+      "Out of Bag error = 0.3430080109235288\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 156\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081282021912525\n",
+      "RMSE Testing = 0.06073228280436834\n",
+      "Out of Bag error = 0.34269995209786897\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 157\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06078721248731548\n",
+      "RMSE Testing = 0.060707077963557586\n",
+      "Out of Bag error = 0.3424555024401913\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 158\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060825958241347844\n",
+      "RMSE Testing = 0.06077223262512689\n",
+      "Out of Bag error = 0.34297898457672293\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 159\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06071482830088064\n",
+      "RMSE Testing = 0.06062576597982824\n",
+      "Out of Bag error = 0.34174209241167414\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 160\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082084370344491\n",
+      "RMSE Testing = 0.06070802914868693\n",
+      "Out of Bag error = 0.34269315794986904\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 161\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082284235453397\n",
+      "RMSE Testing = 0.06076379620491856\n",
+      "Out of Bag error = 0.34287502526049796\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 162\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082149937610165\n",
+      "RMSE Testing = 0.06073583598231401\n",
+      "Out of Bag error = 0.34264088215380434\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 163\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060789935902683355\n",
+      "RMSE Testing = 0.060661590161132616\n",
+      "Out of Bag error = 0.34255908610010294\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 164\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06075076827794479\n",
+      "RMSE Testing = 0.06065692645489766\n",
+      "Out of Bag error = 0.3420224706126414\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 165\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0608285649152703\n",
+      "RMSE Testing = 0.060753799845022195\n",
+      "Out of Bag error = 0.3429329329132237\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 166\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084086980781807\n",
+      "RMSE Testing = 0.06077174183159937\n",
+      "Out of Bag error = 0.3430805960492944\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 167\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083254970673704\n",
+      "RMSE Testing = 0.06074492479008399\n",
+      "Out of Bag error = 0.3428896358906285\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 168\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06086529315132342\n",
+      "RMSE Testing = 0.06080795345906591\n",
+      "Out of Bag error = 0.3430581458659912\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 169\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088849596994317\n",
+      "RMSE Testing = 0.0607885579276104\n",
+      "Out of Bag error = 0.34344727228647215\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 170\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081266098944822\n",
+      "RMSE Testing = 0.060683374400124\n",
+      "Out of Bag error = 0.34278257828180364\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 171\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060881138636599365\n",
+      "RMSE Testing = 0.060783252916838504\n",
+      "Out of Bag error = 0.3431956000321273\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 172\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082839195621574\n",
+      "RMSE Testing = 0.06075421588621795\n",
+      "Out of Bag error = 0.3428692039927966\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 173\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081986285330182\n",
+      "RMSE Testing = 0.06068880409465124\n",
+      "Out of Bag error = 0.3428074513504944\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 174\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06098541568050677\n",
+      "RMSE Testing = 0.060947631293231885\n",
+      "Out of Bag error = 0.3443939239685229\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 175\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080340355580545\n",
+      "RMSE Testing = 0.06073413096490362\n",
+      "Out of Bag error = 0.34248260076862425\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 176\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060891593812645306\n",
+      "RMSE Testing = 0.060808588449915345\n",
+      "Out of Bag error = 0.34345928611778154\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 177\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081369119973475\n",
+      "RMSE Testing = 0.06073885085369231\n",
+      "Out of Bag error = 0.3430496429479596\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 178\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.0608711962357756\n",
+      "RMSE Testing = 0.06075865698531539\n",
+      "Out of Bag error = 0.34319660515873085\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 179\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060829785141136905\n",
+      "RMSE Testing = 0.060747276866154123\n",
+      "Out of Bag error = 0.34254603879678686\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 180\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083990508880686\n",
+      "RMSE Testing = 0.060767605434844184\n",
+      "Out of Bag error = 0.34297648289289767\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 181\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06088232776250392\n",
+      "RMSE Testing = 0.0608110083919992\n",
+      "Out of Bag error = 0.343490385964349\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 182\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060843006877505726\n",
+      "RMSE Testing = 0.06076021961854851\n",
+      "Out of Bag error = 0.34274114589109334\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 183\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.060784286930503605\n",
+      "RMSE Testing = 0.06066257622321708\n",
+      "Out of Bag error = 0.34266701641964564\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 184\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087391706089289\n",
+      "RMSE Testing = 0.06077850474899417\n",
+      "Out of Bag error = 0.3432137821730148\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 185\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083663571398519\n",
+      "RMSE Testing = 0.06077503627179157\n",
+      "Out of Bag error = 0.34282348309580063\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 186\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06081480231366606\n",
+      "RMSE Testing = 0.060690825478767293\n",
+      "Out of Bag error = 0.34274108998196906\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 187\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085438010823191\n",
+      "RMSE Testing = 0.06074995571867999\n",
+      "Out of Bag error = 0.3429869295015915\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 188\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085213590206372\n",
+      "RMSE Testing = 0.06075469391033729\n",
+      "Out of Bag error = 0.3432526164899947\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 189\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080761457377722\n",
+      "RMSE Testing = 0.06073977281040595\n",
+      "Out of Bag error = 0.3424491564209558\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 190\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084734610483842\n",
+      "RMSE Testing = 0.06077529381898443\n",
+      "Out of Bag error = 0.34323243900187766\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 191\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085405508502558\n",
+      "RMSE Testing = 0.060744580660350656\n",
+      "Out of Bag error = 0.3433797467117118\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 192\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06084729961712916\n",
+      "RMSE Testing = 0.06081111943799011\n",
+      "Out of Bag error = 0.34325989244403776\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 193\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06073716811603248\n",
+      "RMSE Testing = 0.06064307051832411\n",
+      "Out of Bag error = 0.3417887226645167\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 194\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06087244398860362\n",
+      "RMSE Testing = 0.060751995131652504\n",
+      "Out of Bag error = 0.3431860918055074\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 195\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06083526029252395\n",
+      "RMSE Testing = 0.06073824437620058\n",
+      "Out of Bag error = 0.3431224768756135\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 196\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06082897468868272\n",
+      "RMSE Testing = 0.06076472687598217\n",
+      "Out of Bag error = 0.3426771325451004\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 197\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085629286588542\n",
+      "RMSE Testing = 0.0607616161703697\n",
+      "Out of Bag error = 0.34314619175742916\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 198\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06090461198044198\n",
+      "RMSE Testing = 0.06082583043984453\n",
+      "Out of Bag error = 0.3434724491834149\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 199\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06085235758969537\n",
+      "RMSE Testing = 0.06074395447276561\n",
+      "Out of Bag error = 0.3432606196402245\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 200\n",
+      "Number of features = 4\n",
+      "RMSE Training = 0.06080671948089893\n",
+      "RMSE Testing = 0.060708451895735524\n",
+      "Out of Bag error = 0.342527180164462\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 1\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06130257795764323\n",
+      "RMSE Testing = 0.0611533218631935\n",
+      "Out of Bag error = 0.9816331427859183\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 2\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06127097816599222\n",
+      "RMSE Testing = 0.06108258033302315\n",
+      "Out of Bag error = 0.7457310331684974\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 3\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060535711467073774\n",
+      "RMSE Testing = 0.06059226282998461\n",
+      "Out of Bag error = 0.6046426749439033\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06094126619709735\n",
+      "RMSE Testing = 0.060788925161511985\n",
+      "Out of Bag error = 0.4968494152250399\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 5\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060940962970267284\n",
+      "RMSE Testing = 0.06080343877395228\n",
+      "Out of Bag error = 0.4414771142184938\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 6\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0601712392672092\n",
+      "RMSE Testing = 0.06008858822741235\n",
+      "Out of Bag error = 0.40815849584155883\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 7\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060426465117423646\n",
+      "RMSE Testing = 0.060320380081397965\n",
+      "Out of Bag error = 0.3847940722199362\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 8\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06067381489332881\n",
+      "RMSE Testing = 0.06053130610240319\n",
+      "Out of Bag error = 0.3656173828018863\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 9\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06085933781978516\n",
+      "RMSE Testing = 0.06075839412560078\n",
+      "Out of Bag error = 0.3599579305630957\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 10\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06074987350266488\n",
+      "RMSE Testing = 0.06064520869907915\n",
+      "Out of Bag error = 0.35327625816796504\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 11\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06072748011200113\n",
+      "RMSE Testing = 0.06065774630589947\n",
+      "Out of Bag error = 0.3490578324516167\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 12\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0604371404628397\n",
+      "RMSE Testing = 0.06034942133242186\n",
+      "Out of Bag error = 0.34406431686372546\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 13\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06061122647582209\n",
+      "RMSE Testing = 0.06051787334397015\n",
+      "Out of Bag error = 0.3432348129409606\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 14\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060575192958832236\n",
+      "RMSE Testing = 0.06046266195682688\n",
+      "Out of Bag error = 0.3424739389866447\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 15\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06041313027298841\n",
+      "RMSE Testing = 0.060317794691478265\n",
+      "Out of Bag error = 0.34022744854601134\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 16\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06056545366855113\n",
+      "RMSE Testing = 0.060429807660394796\n",
+      "Out of Bag error = 0.3409988449257531\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 17\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06068735121927106\n",
+      "RMSE Testing = 0.0605711324007566\n",
+      "Out of Bag error = 0.3415145507922735\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 18\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06024688737300401\n",
+      "RMSE Testing = 0.06016560955667929\n",
+      "Out of Bag error = 0.33755380707374505\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 19\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060510356857562154\n",
+      "RMSE Testing = 0.060478638430547904\n",
+      "Out of Bag error = 0.3402508808964576\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 20\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060359102304168244\n",
+      "RMSE Testing = 0.060260581846648634\n",
+      "Out of Bag error = 0.33855963191988886\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 21\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06072398954345293\n",
+      "RMSE Testing = 0.06062499305811937\n",
+      "Out of Bag error = 0.34150461427058093\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 22\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06023892748963681\n",
+      "RMSE Testing = 0.06014636951763388\n",
+      "Out of Bag error = 0.33719419036890647\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 23\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06049680098602008\n",
+      "RMSE Testing = 0.06037689130400801\n",
+      "Out of Bag error = 0.3399067148516989\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 24\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06080526074608326\n",
+      "RMSE Testing = 0.06074795066583531\n",
+      "Out of Bag error = 0.3424588040245041\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 25\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060690133842309944\n",
+      "RMSE Testing = 0.060572647044541725\n",
+      "Out of Bag error = 0.3414950141553378\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 26\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060610737883301236\n",
+      "RMSE Testing = 0.06053243159304309\n",
+      "Out of Bag error = 0.34026317626356584\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 27\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06051503720007836\n",
+      "RMSE Testing = 0.06039516344219642\n",
+      "Out of Bag error = 0.3395300723746448\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 28\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06054966284189127\n",
+      "RMSE Testing = 0.060457920224559904\n",
+      "Out of Bag error = 0.3404886185154773\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 29\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06076038934426713\n",
+      "RMSE Testing = 0.06067784818631734\n",
+      "Out of Bag error = 0.3417390466746008\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 30\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06049379008887886\n",
+      "RMSE Testing = 0.06038507416549031\n",
+      "Out of Bag error = 0.33926768855315753\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 31\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060459232578001974\n",
+      "RMSE Testing = 0.06035721448693619\n",
+      "Out of Bag error = 0.3383707639832075\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 32\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06063005199605759\n",
+      "RMSE Testing = 0.060555147907721076\n",
+      "Out of Bag error = 0.3403914238330229\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 33\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060555768286412195\n",
+      "RMSE Testing = 0.060450489094746485\n",
+      "Out of Bag error = 0.33995809286091905\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 34\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06055503253765725\n",
+      "RMSE Testing = 0.06049581738454146\n",
+      "Out of Bag error = 0.33986268179169177\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 35\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06044816131608165\n",
+      "RMSE Testing = 0.06036873160122502\n",
+      "Out of Bag error = 0.33873361188652523\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 36\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060723759496034854\n",
+      "RMSE Testing = 0.06062345125046305\n",
+      "Out of Bag error = 0.34134184635704645\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 37\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06059503347082114\n",
+      "RMSE Testing = 0.060511513959132526\n",
+      "Out of Bag error = 0.34018314246135645\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 38\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0605516297917527\n",
+      "RMSE Testing = 0.060420893165421194\n",
+      "Out of Bag error = 0.3399379877720743\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 39\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0603295444220258\n",
+      "RMSE Testing = 0.06027417224988659\n",
+      "Out of Bag error = 0.33758444208330796\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 40\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06039145661824165\n",
+      "RMSE Testing = 0.06030887407814689\n",
+      "Out of Bag error = 0.33801194236369403\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 41\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06073214013651117\n",
+      "RMSE Testing = 0.06061099737320362\n",
+      "Out of Bag error = 0.341268852698809\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 42\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060428431177237875\n",
+      "RMSE Testing = 0.060346047933824656\n",
+      "Out of Bag error = 0.33813919766261397\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 43\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06057858598049861\n",
+      "RMSE Testing = 0.06047870784602997\n",
+      "Out of Bag error = 0.339815311453847\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 44\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.0605189768313531\n",
+      "RMSE Testing = 0.060376577153751776\n",
+      "Out of Bag error = 0.33947036820372956\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 45\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06063391258566316\n",
+      "RMSE Testing = 0.06053614495425401\n",
+      "Out of Bag error = 0.34037771201938255\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 46\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06060801923294821\n",
+      "RMSE Testing = 0.06051020737324897\n",
+      "Out of Bag error = 0.34001838433690956\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 47\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060546854226931915\n",
+      "RMSE Testing = 0.06046569792440232\n",
+      "Out of Bag error = 0.3395360071596207\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 48\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060477422763570876\n",
+      "RMSE Testing = 0.0603601450128113\n",
+      "Out of Bag error = 0.3388421195510132\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 49\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06047082505421094\n",
+      "RMSE Testing = 0.06036841138483988\n",
+      "Out of Bag error = 0.33865739571052955\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 50\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06053067699893759\n",
+      "RMSE Testing = 0.060441975399971724\n",
+      "Out of Bag error = 0.3392701836060493\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 51\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060455961298320614\n",
+      "RMSE Testing = 0.06031735897143744\n",
+      "Out of Bag error = 0.3386378991949061\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 52\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060468921745249794\n",
+      "RMSE Testing = 0.060355525120081556\n",
+      "Out of Bag error = 0.3386073575036922\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 53\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06045247392808719\n",
+      "RMSE Testing = 0.06035175756960831\n",
+      "Out of Bag error = 0.33864291053355167\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 54\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06055179341471526\n",
+      "RMSE Testing = 0.060411713086356136\n",
+      "Out of Bag error = 0.3396126413182988\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 55\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06046340199885832\n",
+      "RMSE Testing = 0.06036526094172319\n",
+      "Out of Bag error = 0.3387249847001773\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 56\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06046611245591035\n",
+      "RMSE Testing = 0.060378683113944044\n",
+      "Out of Bag error = 0.3387983761411312\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 57\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06034031533295242\n",
+      "RMSE Testing = 0.060259715455692156\n",
+      "Out of Bag error = 0.3375305983363586\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 58\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06060618194930698\n",
+      "RMSE Testing = 0.06051260978083767\n",
+      "Out of Bag error = 0.34010628814728133\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 59\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06046600745556548\n",
+      "RMSE Testing = 0.06039513657239054\n",
+      "Out of Bag error = 0.33872651451725544\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 60\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.060538336617214875\n",
+      "RMSE Testing = 0.06044030235339774\n",
+      "Out of Bag error = 0.3392735879852906\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 61\n",
+      "Number of features = 5\n",
+      "RMSE Training = 0.06060200349036845\n",
+      "RMSE Testing = 0.06052377667364513\n",
+      "Out of Bag error = 0.3400636181435182\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "-----------------------------------------------------\n",
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+      "-----------------------------------------------------\n"
+     ]
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+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -2919,7 +11637,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2931,7 +11649,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2943,7 +11661,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2955,7 +11673,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2967,7 +11685,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2979,7 +11697,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2991,7 +11709,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3003,7 +11721,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3017,10 +11735,11 @@
    "source": [
     "i = 0;\n",
     "j = 0;\n",
-    "total_rmse_per_fold_train = []\n",
-    "total_rmse_per_fold_test = []\n",
-    "total_oob_error_per_fold = []\n",
+    "best = []\n",
     "while i < len(features_array):\n",
+    "    total_rmse_per_fold_train = []\n",
+    "    total_rmse_per_fold_test = []\n",
+    "    total_oob_error_per_fold = []\n",
     "    while j < len(trees_array):\n",
     "        Num_trees = trees_array[j]\n",
     "        Num_features = features_array[i]\n",
@@ -3051,7 +11770,7 @@
     "        print('Out of Bag error = {}'.format(np.mean(oob_error_per_fold)))\n",
     "        print('-----------------------------------------------------')\n",
     "        j = j + 1\n",
-    "    i = i + 1\n",
+    "    best.append(np.argmin(total_rmse_per_fold_test))\n",
     "    plt.figure()\n",
     "    plt.plot(trees_array, total_oob_error_per_fold)\n",
     "    plt.title('Out of Bag error (y-axis) against number of trees (x-axis)')\n",
@@ -3062,7 +11781,29 @@
     "    plt.plot(trees_array, total_rmse_per_fold_test)\n",
     "    plt.title('average Test RMSE (y-axis) against number of trees (x-axis)')\n",
     "    plt.xlabel('Number of Trees')\n",
-    "    plt.ylabel('average Test RMSE')"
+    "    plt.ylabel('average Test RMSE')\n",
+    "    j = 0\n",
+    "    i = i + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[60, 19, 3, 18, 5]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best"
    ]
   },
   {
@@ -3074,18 +11815,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
     "depth_array = np.arange(1,41)\n",
-    "Best_num_features = 1\n",
-    "Best_num_trees = 1"
+    "Best_num_features = 3\n",
+    "Best_num_trees = 4"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -3123,13 +11864,35 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 1\n",
+      "RMSE Training = 0.09744766048996603\n",
+      "RMSE Testing = 0.09730819041210333\n",
+      "Out of Bag error = 0.9519461308140652\n",
+      "-----------------------------------------------------\n",
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 2\n",
+      "RMSE Training = 0.0867187586927833\n",
+      "RMSE Testing = 0.08653840903390667\n",
+      "Out of Bag error = 0.818206094149847\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3185,44 +11948,20 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 1\n",
-      "RMSE Training = 0.10029346959077583\n",
-      "RMSE Testing = 0.10013885582945978\n",
-      "Out of Bag error = 1.1901077092685042\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 2\n",
-      "RMSE Training = 0.09645719190220034\n",
-      "RMSE Testing = 0.09649147156766372\n",
-      "Out of Bag error = 1.162937607061139\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 3\n",
-      "RMSE Training = 0.09015842359813377\n",
-      "RMSE Testing = 0.09075260847907274\n",
-      "Out of Bag error = 1.1280663376781566\n",
+      "RMSE Training = 0.0783243175710688\n",
+      "RMSE Testing = 0.07809194419129804\n",
+      "Out of Bag error = 0.7177853657027449\n",
       "-----------------------------------------------------\n",
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 4\n",
-      "RMSE Training = 0.08069265468092283\n",
-      "RMSE Testing = 0.08088446050083163\n",
-      "Out of Bag error = 1.066264783309252\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 5\n",
-      "RMSE Training = 0.07783628011360069\n",
-      "RMSE Testing = 0.07857923079210112\n",
-      "Out of Bag error = 1.0526418662210733\n",
+      "RMSE Training = 0.06198155089474257\n",
+      "RMSE Testing = 0.061474473608275605\n",
+      "Out of Bag error = 0.5492388665047793\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3248,6 +11987,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 5\n",
+      "RMSE Training = 0.04603255354045528\n",
+      "RMSE Testing = 0.04631528500563094\n",
+      "Out of Bag error = 0.41381642709133787\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3280,6 +12041,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 6\n",
+      "RMSE Training = 0.030998384504294912\n",
+      "RMSE Testing = 0.03223120902126097\n",
+      "Out of Bag error = 0.3223520748081723\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3303,28 +12086,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 6\n",
-      "RMSE Training = 0.0690794793243052\n",
-      "RMSE Testing = 0.06975234135026605\n",
-      "Out of Bag error = 1.0335571629009153\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 7\n",
-      "RMSE Training = 0.05332917861353219\n",
-      "RMSE Testing = 0.054182026304627665\n",
-      "Out of Bag error = 0.9633857220950377\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 8\n",
-      "RMSE Training = 0.05897010786136999\n",
-      "RMSE Testing = 0.061618071240896286\n",
-      "Out of Bag error = 0.9763287298527821\n",
+      "RMSE Training = 0.024498790136397004\n",
+      "RMSE Testing = 0.025070117485174542\n",
+      "Out of Bag error = 0.28817705722807047\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3345,11 +12112,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 8\n",
+      "RMSE Training = 0.017134900020134503\n",
+      "RMSE Testing = 0.018010377928302172\n",
+      "Out of Bag error = 0.24779290193445203\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3373,7 +12156,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 9\n",
+      "RMSE Training = 0.014425092319059848\n",
+      "RMSE Testing = 0.016452414524046496\n",
+      "Out of Bag error = 0.23687996859578453\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3391,30 +12194,14 @@
     {
      "name": "stdout",
      "output_type": "stream",
-     "text": [
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 9\n",
-      "RMSE Training = 0.039404023321425515\n",
-      "RMSE Testing = 0.042329570563549294\n",
-      "Out of Bag error = 0.9043862011876141\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 10\n",
-      "RMSE Training = 0.036500285289040146\n",
-      "RMSE Testing = 0.04232661023657303\n",
-      "Out of Bag error = 0.8908124308392222\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 11\n",
-      "RMSE Training = 0.03259189704145247\n",
-      "RMSE Testing = 0.037750679330887965\n",
-      "Out of Bag error = 0.9064370455646227\n",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 10\n",
+      "RMSE Training = 0.012693519217498419\n",
+      "RMSE Testing = 0.014721250593285762\n",
+      "Out of Bag error = 0.2239169290944914\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3445,7 +12232,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 11\n",
+      "RMSE Training = 0.012017911382738533\n",
+      "RMSE Testing = 0.015466674282652157\n",
+      "Out of Bag error = 0.23260391008975842\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3473,20 +12280,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 12\n",
-      "RMSE Training = 0.028901397255712967\n",
-      "RMSE Testing = 0.03904937666239299\n",
-      "Out of Bag error = 0.9012538840250695\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 13\n",
-      "RMSE Training = 0.027102647504993537\n",
-      "RMSE Testing = 0.03987960637130426\n",
-      "Out of Bag error = 0.9050215895658885\n",
+      "RMSE Training = 0.011215324109979292\n",
+      "RMSE Testing = 0.014427438653534954\n",
+      "Out of Bag error = 0.2489874597029173\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3511,7 +12310,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 13\n",
+      "RMSE Training = 0.010194322616534534\n",
+      "RMSE Testing = 0.014549351750308479\n",
+      "Out of Bag error = 0.23266024877677527\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3537,20 +12356,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 14\n",
-      "RMSE Training = 0.030641476017787994\n",
-      "RMSE Testing = 0.047628547863471496\n",
-      "Out of Bag error = 0.9277031298924717\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 15\n",
-      "RMSE Training = 0.026030936694950817\n",
-      "RMSE Testing = 0.03882582236123748\n",
-      "Out of Bag error = 0.9040372880825542\n",
+      "RMSE Training = 0.009848162432703514\n",
+      "RMSE Testing = 0.015238253175404509\n",
+      "Out of Bag error = 0.2407605490512153\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3576,6 +12387,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 15\n",
+      "RMSE Training = 0.00939332899163858\n",
+      "RMSE Testing = 0.016117335925975907\n",
+      "Out of Bag error = 0.2470401864881699\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3599,20 +12438,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 16\n",
-      "RMSE Training = 0.02411660849715343\n",
-      "RMSE Testing = 0.039319600172386705\n",
-      "Out of Bag error = 0.9006216112544371\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 17\n",
-      "RMSE Training = 0.022993729795706276\n",
-      "RMSE Testing = 0.03386801958354739\n",
-      "Out of Bag error = 0.9080830329437533\n",
+      "RMSE Training = 0.009064652271374474\n",
+      "RMSE Testing = 0.015290090132875012\n",
+      "Out of Bag error = 0.24974216258790133\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3643,9 +12474,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 17\n",
+      "RMSE Training = 0.008462935651336396\n",
+      "RMSE Testing = 0.015588043773498284\n",
+      "Out of Bag error = 0.25370096219401617\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3661,7 +12510,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 18\n",
+      "RMSE Training = 0.008065137753239679\n",
+      "RMSE Testing = 0.015322555831926604\n",
+      "Out of Bag error = 0.23789689874771688\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3693,20 +12562,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 18\n",
-      "RMSE Training = 0.02346141112714842\n",
-      "RMSE Testing = 0.03469858098552804\n",
-      "Out of Bag error = 0.9039051233703495\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 19\n",
-      "RMSE Training = 0.01997969316574139\n",
-      "RMSE Testing = 0.03155916262425835\n",
-      "Out of Bag error = 0.8988605239942478\n",
+      "RMSE Training = 0.007747261245255854\n",
+      "RMSE Testing = 0.0158751930911148\n",
+      "Out of Bag error = 0.22994143116391452\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3720,6 +12581,36 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 20\n",
+      "RMSE Training = 0.008303238561576217\n",
+      "RMSE Testing = 0.01665362015873618\n",
+      "Out of Bag error = 0.23901452088456515\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3751,20 +12642,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 20\n",
-      "RMSE Training = 0.020634510766763068\n",
-      "RMSE Testing = 0.03462071906623213\n",
-      "Out of Bag error = 0.9075007533325692\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 21\n",
-      "RMSE Training = 0.0240448783345509\n",
-      "RMSE Testing = 0.03649476386042087\n",
-      "Out of Bag error = 0.8992892205852601\n",
+      "RMSE Training = 0.007757957133070115\n",
+      "RMSE Testing = 0.01610797487483718\n",
+      "Out of Bag error = 0.24661799893275468\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3780,6 +12663,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 22\n",
+      "RMSE Training = 0.007672386977117117\n",
+      "RMSE Testing = 0.015704665142253098\n",
+      "Out of Bag error = 0.23340437588035529\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3811,20 +12722,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 22\n",
-      "RMSE Training = 0.02014037834365806\n",
-      "RMSE Testing = 0.030916883386743317\n",
-      "Out of Bag error = 0.8828980839587295\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 23\n",
-      "RMSE Training = 0.019899025157352764\n",
-      "RMSE Testing = 0.03274213794137807\n",
-      "Out of Bag error = 0.9048034004286938\n",
+      "RMSE Training = 0.008186982421227862\n",
+      "RMSE Testing = 0.016096565867784976\n",
+      "Out of Bag error = 0.2523620297652452\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3840,6 +12743,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 24\n",
+      "RMSE Training = 0.00821365393489374\n",
+      "RMSE Testing = 0.01651123844877171\n",
+      "Out of Bag error = 0.2552094647342341\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3871,20 +12802,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 24\n",
-      "RMSE Training = 0.023221960748333832\n",
-      "RMSE Testing = 0.03485497658196572\n",
-      "Out of Bag error = 0.9049094310700309\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 25\n",
-      "RMSE Training = 0.024682403417431394\n",
-      "RMSE Testing = 0.04183962006870179\n",
-      "Out of Bag error = 0.9124508036943665\n",
+      "RMSE Training = 0.0075126627315455635\n",
+      "RMSE Testing = 0.01564842569578454\n",
+      "Out of Bag error = 0.23970480695034269\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3907,13 +12830,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 26\n",
+      "RMSE Training = 0.007841592274964008\n",
+      "RMSE Testing = 0.016305029823556375\n",
+      "Out of Bag error = 0.25319894940479143\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3937,7 +12874,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 27\n",
+      "RMSE Training = 0.007862998321788725\n",
+      "RMSE Testing = 0.01590058662975413\n",
+      "Out of Bag error = 0.2570075198325196\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -3961,20 +12918,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 26\n",
-      "RMSE Training = 0.02276289334360069\n",
-      "RMSE Testing = 0.03862566986614009\n",
-      "Out of Bag error = 0.8917828428806907\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 27\n",
-      "RMSE Training = 0.026248142876969516\n",
-      "RMSE Testing = 0.038976037835335646\n",
-      "Out of Bag error = 0.9234429661916387\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 28\n",
+      "RMSE Training = 0.007714627404397531\n",
+      "RMSE Testing = 0.015629687540038573\n",
+      "Out of Bag error = 0.24341453388596848\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -3998,6 +12947,34 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 29\n",
+      "RMSE Training = 0.008033825868003722\n",
+      "RMSE Testing = 0.01633489875186008\n",
+      "Out of Bag error = 0.24122756799485812\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4021,20 +12998,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 28\n",
-      "RMSE Training = 0.02839106879811119\n",
-      "RMSE Testing = 0.04270556407291485\n",
-      "Out of Bag error = 0.9294501445263105\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 29\n",
-      "RMSE Training = 0.023332052168506943\n",
-      "RMSE Testing = 0.040700754105578496\n",
-      "Out of Bag error = 0.8873795945608194\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 30\n",
+      "RMSE Training = 0.0077104130870081404\n",
+      "RMSE Testing = 0.015771666741426018\n",
+      "Out of Bag error = 0.2567786741325135\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4065,12 +13034,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -4079,20 +13042,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 30\n",
-      "RMSE Training = 0.02236605447800038\n",
-      "RMSE Testing = 0.03416835493274427\n",
-      "Out of Bag error = 0.885069165934133\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 31\n",
-      "RMSE Training = 0.02496493526117527\n",
-      "RMSE Testing = 0.037996154261883575\n",
-      "Out of Bag error = 0.9019860616608721\n",
+      "RMSE Training = 0.007667144076457697\n",
+      "RMSE Testing = 0.015605467622523087\n",
+      "Out of Bag error = 0.24127028987486848\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4115,11 +13070,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 32\n",
+      "RMSE Training = 0.007670658590843071\n",
+      "RMSE Testing = 0.015599814441104054\n",
+      "Out of Bag error = 0.25325850439731434\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4143,7 +13114,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 33\n",
+      "RMSE Training = 0.00793328493261828\n",
+      "RMSE Testing = 0.01593771589188875\n",
+      "Out of Bag error = 0.2467544782872491\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4167,20 +13158,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 32\n",
-      "RMSE Training = 0.024883128709612852\n",
-      "RMSE Testing = 0.04091260561404413\n",
-      "Out of Bag error = 0.8954030054160137\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 33\n",
-      "RMSE Training = 0.023337211710397175\n",
-      "RMSE Testing = 0.04058816987519835\n",
-      "Out of Bag error = 0.8860384825820613\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 34\n",
+      "RMSE Training = 0.007583663772697683\n",
+      "RMSE Testing = 0.015924560968099265\n",
+      "Out of Bag error = 0.24668070778833578\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4211,12 +13194,6 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n"
      ]
     },
@@ -4225,20 +13202,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 34\n",
-      "RMSE Training = 0.022519219623101338\n",
-      "RMSE Testing = 0.038524823183353554\n",
-      "Out of Bag error = 0.8931254175025438\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 35\n",
-      "RMSE Training = 0.02160377854034624\n",
-      "RMSE Testing = 0.03595666705098065\n",
-      "Out of Bag error = 0.8921527805889182\n",
+      "RMSE Training = 0.007866777892508541\n",
+      "RMSE Testing = 0.015594649851681427\n",
+      "Out of Bag error = 0.24703162864670433\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4246,20 +13215,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4283,20 +13238,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 36\n",
-      "RMSE Training = 0.02392429891019801\n",
-      "RMSE Testing = 0.03895113079229779\n",
-      "Out of Bag error = 0.8934562360852791\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 37\n",
-      "RMSE Training = 0.02234566224119603\n",
-      "RMSE Testing = 0.037789210376496615\n",
-      "Out of Bag error = 0.897934910129373\n",
+      "RMSE Training = 0.007673803366320356\n",
+      "RMSE Testing = 0.015387818202939363\n",
+      "Out of Bag error = 0.23997080520167477\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4326,6 +13273,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 37\n",
+      "RMSE Training = 0.007729927958501977\n",
+      "RMSE Testing = 0.015285141406523603\n",
+      "Out of Bag error = 0.2377511173548088\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4340,6 +13309,28 @@
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 38\n",
+      "RMSE Training = 0.007772351581268967\n",
+      "RMSE Testing = 0.01622127324186521\n",
+      "Out of Bag error = 0.25114435384335193\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4371,28 +13362,12 @@
      "output_type": "stream",
      "text": [
       "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 38\n",
-      "RMSE Training = 0.025561673675717007\n",
-      "RMSE Testing = 0.04074464394821514\n",
-      "Out of Bag error = 0.9124087932557327\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
       "Depth of trees = 39\n",
-      "RMSE Training = 0.022032302533764116\n",
-      "RMSE Testing = 0.035483243726283344\n",
-      "Out of Bag error = 0.8917040953701079\n",
-      "-----------------------------------------------------\n",
-      "-----------------------------------------------------\n",
-      "Number of trees = 1\n",
-      "Number of features = 1\n",
-      "Depth of trees = 40\n",
-      "RMSE Training = 0.0235050672888152\n",
-      "RMSE Testing = 0.0379045751751005\n",
-      "Out of Bag error = 0.8898144155367556\n",
+      "RMSE Training = 0.00794054213200324\n",
+      "RMSE Testing = 0.016411747549574376\n",
+      "Out of Bag error = 0.23599223518809742\n",
       "-----------------------------------------------------\n"
      ]
     },
@@ -4407,9 +13382,27 @@
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
-      "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
-      "  warn(\"Some inputs do not have OOB scores. \"\n",
+      "  warn(\"Some inputs do not have OOB scores. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "Number of trees = 4\n",
+      "Number of features = 3\n",
+      "Depth of trees = 40\n",
+      "RMSE Training = 0.008190429742953536\n",
+      "RMSE Testing = 0.015841847782126283\n",
+      "Out of Bag error = 0.24495821052400749\n",
+      "-----------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
       "  warn(\"Some inputs do not have OOB scores. \"\n",
       "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
@@ -4424,13 +13417,13 @@
        "Text(0, 0.5, 'average Test RMSE')"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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nVDVLVbOSkgJuyzHGGNMMnvaeEpHDcMaiOU1Va4f9zsNn1EycaqsmjyprjDGm9XlW0hDnpvBv4tydbLXPrLlAhogMcEd3vQBnYC1jjDEeC2aX25dw7pyVKCK5wB9xbqiCqj6OM6prL5zx3AGq3GqmKhG5DmfE0HCce0Avq2cXxhhj2liHGkYkKytLm3qdRlllNc99u5GR6QkcfWhD9zgyxpiOSUTmqWpWoMt3qCvCmyNMhCdnrmNwSpwlDWOMaYTnvae81iUijEuP6c/MNUWs3FridTjGGBPSOn3SALjoqEPoGhnOUzND+t5GxhjjOUsaQPduXTgvK513FuZRUFLmdTjGGBOyLGm4Lhs3gKoa5bnZTRq7yxhjOhVLGq4BiTGcNDSZ52dvZF9FtdfhGGNMSLKk4ePnxx7Kzr2VvDE/1+tQjDEmJFnS8HFk/x4clp7A1K/XU1PTca5fMcaY1mJJw4eIcMX4AawrKuWLlQVeh2OMMSHHkkYdp4/sQ2pCNE99vc7rUIwxJuRY0qgjMjyMn43rz+x1O1iaV+x1OMYYE1IsadTjx0ceQkyXcJ6aaaUNY4zxZUmjHgldIzn/yL68t3gLW4r3eR2OMcaEDEsaDbh83ABqVHlmll3sZ4wxtSxpNKBvz25MGpHCi99tpLS8yutwjDEmJFjS8OOK8YdSUlbFa9mbvQ7FGGNCgiUNP47o14Mxh3Rn6jcbqLaL/YwxxpJGY6489lA27dhrF/sZYwyWNBp1yrBkesZ04e0FeV6HYowxnrOk0YiI8DBOH5nCZyu2sccaxI0xnZwljQBMHpVGeVUNny7f6nUoxhjjKUsaAcjq14M+CdFMW5jvdSjGGOMpSxoBCAsTzhyVysw1RewsrfA6HGOM8UzQkoaITBWRAhFZ2sD8ISLyrYiUi8jNdeZtEJElIrJQRLKDFWNTTB6VSlWN8sHSLV6HYowxnglmSeNpYJKf+TuA64H7G5g/UVVHq2pWawfWHMNT4zk0KcaqqIwxnVrQkoaqfoWTGBqaX6Cqc4HKYMXQmkSEyaNSmbNhB1uLy7wOxxhjPBGqbRoKfCIi80TkKn8LishVIpItItmFhYVBDWryqFRU4b3FVtowxnROoZo0xqvqGOA04FoROa6hBVX1CVXNUtWspKSkoAZ1aFIsI9LimbbIkoYxpnMKyaShqnnu3wLgLWCstxF9b/KoVBbnFrO+qNTrUIwxps2FXNIQkRgRiat9DpwC1NsDyws/PCwVgHettGGM6YQigrVhEXkJOB5IFJFc4I9AJICqPi4iKUA2EA/UiMiNwDAgEXhLRGrje1FVPwpWnE2V2r0rY/v3ZNqifH55wiDcOI0xplMIWtJQ1Qsbmb8VSK9nVgkwKihBtZIzR6fy+7eXsmLLboalxnsdjjHGtJmQq55qD04fkUJ4mFiDuDGm07Gk0Qy9YqMYPyiRdxflo2o3ZzLGdB6WNJpp8qhU8nbtY/6mnV6HYowxbcaSRjOdMjyZqIgwG1bEGNOpWNJoprjoSE4Y0pv3l2yhqrrG63CMMaZNWNJogcmjUinaU8G367Z7HYoxxrQJSxotMHFIb2KjIqyKyhjTaVjSaIHoyHBOGZ7MR8u2Ul5V7XU4xhgTdJY0WmjS8BR2l1WxOLfY61CMMSboLGm00Ki+3QFYlmdJwxjT8VnSaKHecVEkxkaxNL/E61CMMSboLGm0kIgwIi2epVbSMMZ0ApY0WsGI1ATWFOyhrNIaw40xHZsljVYwIi2B6hpl5dbdXodijDFBZUmjFYxIc4ZHtyoqY0xHZ0mjFaR170r3bpEsy7ekYYzp2CxptAIRYURqAkuspGGM6eAsabSSEWkJrNq6m4oqG7zQGNNxWdJoJSPS4qmsVlZvs8ZwY0zHZUmjlYxITQCwdg1jTIdmSaOVHNKzG3FREdauYYzp0CxptJKwMGFYajxL82w4EWNMx2VJoxWNTEtgxZYSu5OfMabDClrSEJGpIlIgIksbmD9ERL4VkXIRubnOvEkiskpEckTklmDF2NpGpCVQXlXD2sJSr0MxxpigCGZJ42lgkp/5O4Drgft9J4pIOPAYcBowDLhQRIYFKcZWVXtluLVrGGM6qqAlDVX9CicxNDS/QFXnApV1Zo0FclR1napWAC8DZwUrztY0IDGWbl3CbTgRY0yHFYptGmnAZp/Xue60eonIVSKSLSLZhYWFQQ/On/AwYVifeOt2a4zpsEIxaTSJqj6hqlmqmpWUlOR1OIxIS2BZfgk1Nep1KMYY0+r8Jg0RCReR6W0VjCsP6OvzOt2d1i4MT41nb0U164qsMdwY0/H4TRqqWg3UiEhCG8UDMBfIEJEBItIFuACY1ob7b5ERaXZluDGm44oIYJk9wBIR+RTY//NZVa/3t5KIvAQcDySKSC7wRyDSXfdxEUkBsoF4nMR0IzBMVUtE5DrgYyAcmKqqy5r8zjyS0TuWqIgwluYVc9boBptijDGmXQokabzpPppEVS9sZP5WnKqn+uZ9AHzQ1H2GgojwMIb0sSvDjTEdU6NJQ1WfcauJMt1Jq1S1bjdZ42NEajzTFuWjqoiI1+EYY0yrabT3lIgcD6zBueDuX8BqETkuyHG1ayPSEthdVsWmHXu9DsUYY1pVINVTDwCnqOoqABHJBF4CjghmYO3ZSLcxfGleCf16xXgcjTHGtJ5ArtOIrE0YAKq6GrdB29QvIzmWyHBhqfWgMsZ0MIGUNLJF5Cngeff1RTi9nkwDoiLCyUyOs+FEjDEdTiAljWuA5TiDC17vPr8mmEF1BCNSE1iaV4yqXRlujOk4/JY03BFnp6rqRcCDbRNSxzAiLZ5XsjeTX1xGWveuXodjjDGtIpArwvu5XW5NE4zY3xhuVVTGmI4jkDaNdcA3IjKNA68It5KHH0P7xBMeJizNK+bU4Sleh2OMMa0ikKSx1n2EAXHBDafjiI4MZ1BSrJU0jDEdSiBtGnGqerO/5Uz9hqfFM3NNkddhGGNMqwmkTWNcG8XS4YxMS6BwdzkFJWVeh2KMMa0ikOqphW57xmsc2KbR5EEMO5vaxvAlecWcGB/tcTTGGNNygSSNaGA7cILPNKUZI992NkP7xCPiDCdy4tBkr8MxxpgWC2SU28vaIpCOKDYqggGJMTaciDGmwwhklNtMEflcRJa6rw8TkTuCH1rHMDItgQWbdrG3osrrUIwxpsUCGUbkSeBWoBJAVRfj3ILVBOBHR6Szo7Sc615cQFV1jdfhGGNMiwSSNLqp6pw60+xnc4COzUjirikj+GJlAXe8vdTGojLGtGuBNIQXichAnMZvRORHwJagRtXBXHRUP7YWl/HIFzmkJERz40mZja9kjDEhKJCkcS3wBDBERPKA9TjDo5smuOnkTLYUl/HQZ2tIiY/mgrGHeB2SMcY0WSC9p9YBJ4lIDBCmqruDH1bHIyLce85ICneXc/vbS0mKi7JuuMaYdieQNg0AVLXUEkbLRIaH8a+LxjCsTzzXvjifBZt2eh2SMcY0ScBJw7SOmKgIpv7sSHrHRXPFM9msLyptfCVjjAkRQUsaIjJVRApqr++oZ76IyD9FJEdEFovIGJ951SKy0H1MC1aMXkmKi+KZy8cCcOnUORTuLvc4ImOMCUwgF/edU8/jRBHp3ciqTwOT/Mw/DchwH1cB//aZt09VR7uPyY3F2B4NSIxh6s+OpHB3Ob94YR41NdYV1xgT+gIpaVwBPIXTY+oinIv9fodzY6aLG1pJVb8CdvjZ7lnAs+qYDXQXkT4BR94BjO7bnbumjGDuhp28MGeT1+EYY0yjAkkaEcBQVT1XVc8FhuFcs3EUTvJorjRgs8/rXHcaQLSIZIvIbBGZ4m8jInKVu2x2YWFhC8Lxxrlj0hg/KJG/friSrcU2hLoxJrQFkjT6quo2n9cF7rQduEOLBEE/Vc0CfgI85F5cWC9VfUJVs1Q1KykpKUjhBI+IcPfZI6isruGP0+pt/jHGmJARSNKYISLvicilInIp8I47LQbY1YJ95wF9fV6nu9NQ1dq/64AZwOEt2E/I69crhhtPyuTjZdv4aOlWr8MxxpgGBZI0rsVp1B7tPp4FrnWv25jYgn1PAy5xe1EdDRSr6hYR6SEiUQAikohz58DlLdhPu3DlsQMY2ieeP05bSklZsApwxhjTMo0mDbeh+nVV/ZX7eF0DGHVPRF4CvgUGi0iuiFwhIleLyNXuIh8A64AcnMb1X7jThwLZIrIImA7cp6odPmlEhofx13OdK8b/9tFKr8Mxxph6NTqMiFsKeATnZN4FCAdKVTXe33qqemEj8xWnFFN3+ixgZGNxdUSHpXfnsnED+O/X65kyOo2s/j29DskYYw4QSPXUo8CFwBqgK3Al8Fgwg+rMbjo5k7TuXbnlzSWUV1V7HY4xxhwgoCvCVTUHCFfValX9H/4v2jMtEBMVwV/OHkFOwR4en7HO63CMMeYAgSSNvSLSBVgoIn8TkV8FuJ5ppomDezN5VCqPTc8hp8DGiDTGhI5ATv4Xu8tdB5TidJM9N5hBGfj9D4fRtUs4t7251IYYMcaEjEB6T21U1TJVLVHVP6nqTW51lQmipLgobj9jKHM27OCN+bleh2OMMYCfpCEiGSLytIg8KCLpIvKhiOwRkUUicmRbBtlZnXdEOuk9ujJjVfsbHsUY0zH5K2n8D5gF5APfAVOBROBmnB5VJshEhGF94lm5tcTrUIwxBvCfNGLdcZ3uxxmq/DW3mupTIKqN4uv0BqfEsWH7XsoqrfutMcZ7/pJGjc/zuj91azBtIjM5juoaZV2h3eHPGOM9f1eEDxGRxYAAA93nuK8PDXpkBnBKGgCrt+1mWKrfi/CNMSbo/CWNoW0WhWnQgMQYIsOFlVvteg1jjPcaTBqqurEtAzH1iwwPY2BSLKu3WdIwxnjPruxuBzKT41hlJQ1jTAiwpNEODE6JI2/XPnbbfTaMMR7zd3Hf5+7fv7ZdOKY+g5NrG8P3eByJMaaz89cQ3kdEjgEmi8jLOL2m9lPV+UGNzOzn24PqiH49PI7GGNOZ+UsafwB+j3Pv7gfrzFPghGAFZQ6U1r0r3bqEW7uGMcZz/npPvQ68LiK/V9W72jAmU0dYmJCRHGc9qIwxnmv0dq+qepeITAaOcyfNUNX3ghuWqWtIchyfrdjmdRjGmE6u0d5TInIvcAOw3H3cICL3BDswc6DMlDi2l1ZQtKfc61CMMZ1YoyUN4AxgtKrWAIjIM8AC4LZgBmYOtL8H1dbdJA6y8SKNMd4I9DqN7j7PE4IRiPEvMyUWgFXWrmGM8VAgJY17gQUiMh2n2+1xwC1BjcocJCk2ip4xXawHlTHGU4Hc7vUl4GjgTeAN4Aeq+kogGxeRqSJSICJLG5gvIvJPEckRkcUiMsZn3qUissZ9XBrY2+m4RITM5FgraRhjPBVQ9ZSqblHVae5jaxO2/zQwyc/804AM93EV8G8AEekJ/BE4ChgL/FFEOv1VbYOT41i9dTeq6nUoxphOKqhjT6nqV8AOP4ucBTyrjtlAdxHpA5wKfKqqO1R1J/Ap/pNPp5CZEkdpRTV5u/Z5HYoxppPyesDCNGCzz+tcd1pD0zu12h5U1q5hjPFKINdpPBfINK+IyFUiki0i2YWFhV6HE1SZ7hhU1q5hjPFKICWN4b4vRCQcOKKV9p8H9PV5ne5Oa2j6QVT1CVXNUtWspKSkVgorNMVHR5KaEM1qK2kYYzzib2j0W0VkN3CYiJS4j91AAfBOK+1/GnCJ24vqaKBYVbcAHwOniEgPtwH8FHdap5eZEscqGyLdGOMRfwMW3gvcKyL3quqtzdm4iLwEHA8kikguTo+oSHf7jwMfAKcDOcBe4DJ33g4RuQuY627qz6rqr0G90xicHMesnO1UVtcQGe51k5QxprMJ5OK+D0XkuLoT3Z5RfqnqhY3MV+DaBuZNBaYGEF+nMjgljorqGjZuL2VQ7zivwzHGdDKBJI3f+DyPxrluYh52Pw1PZO7vQbXHkoYxps0FMjT6mb6vRaQv8FDQIjJ+DeodS5g4PajOoI/X4RhjOpnmVIrnAkNbOxATmOjIcPr3imHV1hKvQzHGdEKNljRE5BGc27uCk2RGA3Z/cA8NToljpXW7NcZ4IJA2jWyf51XAS6r6TZCPtZBvAAAgAElEQVTiMQHITI7jo2VbKausJjoy3OtwjDGdSCBJ4xVgkPs8R1XLghiPCcDglDhUIadgDyPS7PYmxpi24+/ivggR+RtOG8YzwLPAZhH5m4hEtlWA5mC1PaisisoY09b8NYT/HegJDFDVI1R1DDAQ5y5+97dFcKZ+/Xt1o0tEGKttDCpjTBvzlzR+CPxcVfefmVS1BLgG5ypu45GI8DAGJcXaaLfGmDbnL2mo1nO3H1Wt5vveVMYjg1PiGi1pvLc4nxPun0HRnvI2isoY09H5SxrLReSSuhNF5KfAyuCFZAKRmRzHluIyivdV1jt/+soCbnx5IeuKSlm4aVcbR2eM6aj89Z66FnhTRC7HGTYEIAvoCpwd7MCMf0Pce2us3rabI/v3PGDed+u2c/Xz8xjUO5aVW3ezattuThqW7EWYxpgOpsGShqrmqepRwJ+BDe7jz6o6VlXrvbeFaTv7b8hUp11jSW4xVzyTTXqPrrxw5VH0SYhmjTWYG2NaSSBjT30BfNEGsZgmSE2IJjYq4oB2jZyC3Vz6vzkkdI3k+SuPoldsFBnJcay2+28YY1qJ3ZChnRIRMpO/70G1ecdefvrUHMJE3BJGVwAye8eytnAP1TXWd8EY03KWNNqxwSnxrNq2m4KSMn763+/YV1nN81eOpX9izP5lMpPjKK+qYdOOvR5GaozpKCxptGODk2PZtbeS8//zLYW7y/nfZUcyJCX+gGUykmMB7EJAY0yrsKTRjtU2hufvKuPJS7IYc0iPg5bJcIccWW0XAhpjWkEgAxaaEHVYeney+vXg/yYMZNygxHqXiY2KIK17V1YXWGO4MablLGm0Y7FREbx+zTGNLpeZHGvdbo0xrcKqpzqBzOQ41hWWUlVd43Uoxph2zpJGJ5CRHEdFdQ0btlsPKmNMy1jS6AQGu43hVkVljGkpSxqdwKDesYhgV4YbY1osqElDRCaJyCoRyRGRW+qZ309EPheRxSIyQ0TSfeZVi8hC9zEtmHF2dF27hNO3Rze7VsMY02JB6z0lIuHAY8DJOLeMnSsi01R1uc9i9wPPquozInICcC9wsTtvn6qODlZ8nU1mcqwlDWNMiwWzpDEWyFHVdapaAbwMnFVnmWF8Pxji9Hrmm1aSkRzH+qJSKqqsB5UxpvmCmTTSgM0+r3Pdab4WAee4z88G4kSkl/s6WkSyRWS2iExpaCcicpW7XHZhYWFrxd7hZCbHUlWjbNhe6nUoxph2zOuG8JuBCSKyAJgA5AHV7rx+qpoF/AR4SEQG1rcBVX1CVbNUNSspKalNgm6PMpO/v2mTMcY0VzCvCM8D+vq8Tnen7aeq+bglDRGJBc5V1V3uvDz37zoRmQEcDqwNYrwd2sCkWMKsB5UxpoWCWdKYC2SIyAAR6QJcABzQC0pEEkWkNoZbganu9B4iElW7DDAO8G1AN00UHRlOv14xdq2GMaZFgpY0VLUKuA74GFgBvKqqy0TkzyIy2V3seGCViKwGkoG73elDgWwRWYTTQH5fnV5XphkyeseyypKGMaYFgjpgoap+AHxQZ9offJ6/Drxez3qzgJHBjK0zykyO4/OVBZRXVRMVEe51OMaYdsjrhnDThjKSY6muUdYVWg8qY0zzWNLoRAanWA8qY0zLWNLoRAYkxhAeJqyxHlTGmGaypNGJREWE07+XjUFljGk+SxqdTGZyHGvs1q/GmGaypNHJZCTHsWF7KWWV1Y0vbIwxdVjS6GQyk2NRhRwrbZhWVFldw6tzN3PlM9kUlJR5HY4JoqBep2FCz/67+BXsZkRagsfRNGzR5l18vmIbvzo5ExHxOhzTgIqqGt6cn8tjM3LYvGMfAEcf2pMrjz3U48hMsFhJo5PpnxhDZLiE9BhU1TXK795YzD+/yOHzFQVeh2PqUVFVw4vfbWLi/TO45c0l9OjWhacuyWJQ71i+XG2jTXdkVtLoZCLDwxiQGNpjUL2zMI+VW3fTNTKcBz9dzQlDehMWZqWNUFBeVc1r2bn8e8Za8nbtY1Tf7vxlygiOH5yEiPDd+u08M2sjpeVVxETZ6aUjspJGJ5SRHBeyJY3yqmoe+GQ1w1Pj+cuUESzfUsLHy7Z6HZYB9lVUc9rDM7nj7aUkx0fxzOVjefsXxzBxSO/9VYgTB/emorqGWWu3exytCRZLGp1QZu84Nu3Yy96KKq9DOcgLszeRt2sfv5s0hCmHpzGodywPfrqa6hr1OrRO78OlW1hXWMo/fjyKN645hgmZSQe1N2X170lMl3Cmr7JqxY7KkkYnlJkcC4ReD6rdZZU8Oj2HYwb24tiMRMLDhBtPymBNwR7eW5zvdXid3qvZm+nXqxtTRqc12DmhS0QY4zMSmbGyAFVL9B2RJY1OKHP/GFShlTSenLmeHaUV/G7SkP0npdNH9GFIShwPfbaGqmq7v7lXNm4vZfa6HZx3RHqjvdkmDu5NfnGZXUTaQVnS6IT69exGl/CwkGoML9pTzlMz13H6yBRG9e2+f3pYmPCrkzNZX1TKWwvy/GzBBNPr83IRgXOPSG902QmDndsuT19pVVQdkSWNTigiPIxDk2JCagyqR7/Iobyqhl+fMvigeacMS2ZkWgIPf76GiiorbbS16hrl9Xm5HJeRRJ+Ero0u3yehK0NS4qxdo4OypNFJZYZQD6pN2/fywncbOT8rnYFJsQfNFxFuOiWT3J37eG3eZg8i7Ny+ziliS3EZ52f1DXidiUN6k71hJyVllUGMzHjBkkYnlZkcS96ufewp974H1YOfriJMhBtOzGxwmeMzkxhzSHce/SLHxs1qpp2lFdz21hJufXNJkxqpX83eTI9ukZw0rHfA60wc3JuqGuWbNUXNCbVTWV9UyhVPzyWnIHRK/v5Y0uikMmuHE/G4imp5fgnvLMrnsnEDSEmIbnA5EeHXpwxmS3EZL8/Z1IYRtn+qTvXSiQ9+yYvfbeKlOZv4eNm2gNbdWVrBp8u2MeXwtCbdInjMId2Ji45gxiq7Otyf4r2VXPH0XD5fWcDtby1tFz3OLGl0Ut8nDW+rqP728UrioiK4ZsLARpc9ZmAvjhrQk0enr2VfhZU2ApFTsIcLn5zNza8tYkBiDO9fP57M5Fj+8v7ygEps7yzMo6K6hvOOCLxqCpx2s+Mykpi+yrreNqSyuoZrXpjH5p17uXBsX75bv4P3l2zxOqxGWdLopPr27EZURJinjeGz121nxqpCfjFxEAndIhtdvra0UbSnnOdmb2jWPiura3j8y7UcdufHTH70a578ah35u/Y1a1v1UVWen72RRz5fQ3mVd4mtrLKaBz9ZxWkPf8Xy/BLuPWckr/3fDxiemsCdZw4nd+c+nvhqXaPbeTU7l5FpCQxLjW9yDMcPTqJgdznLt5Q05y10aKrKH6ctY9ba7dx7zmH8ZcpIhvWJ5573V4TkRbe+LGl0UuFhwqDesaxuZl/6krJKznzka85//FveX7yFyiZeQ6Gq3PfhSlLio/nZMf0DXm/sgJ4cm5HI41+ua3J7zNwNO/jhP7/mvg9XMvqQHgDc/cEKjrnvC857fBbPfbuBoj3lTdqmr4LdZfzsf3O54+2lPPDpas585GuW5BY3e3vN9fWaIiY99BX//CKHM0b24fNfH8+FYw/ZP37XMYMSOX1kCv+akUOen4S5NK+Y5VtKOD+r8W629antemtVVAd7etYGXvxuE1dPGMiPjkgnPEz401nDyS8u4/EZa70Ozy8bUawTy0yO45ucIqqqa4gID/z3g6py86uLWL6lhNTu0Vz74nxS4qP56dGHcOHYQ+gVG9XgesvyS/hk+TY+WbaVlVt3c985I4mODLyuHOCmkzM5+1+zeOCTVdxwYgbdu3Xxu/yO0gru+3AFr2bnkta9K09eksXJw5IB2FBUynuL85m2KJ/fv7OMP05bxrhBiZw5KpUzRvYJeNC9z1ds47evL2ZPeRV3TRlBeveu3PLmYqb86xuumziI604YRGQTjnFDluUXc/Xz89hVWkmNKjUK1aqo+7xGFVXo36sbz19xFOMzEuvdzm2nD+WLlQXc88EKHvvJmHqXeS17M10iwpg8Kq1ZsfaOi2ZkWgLTVxZw7cRBzdpGRzRjVQF3vbeck4cl89tTv+9ifmT/npw1OpXHv1rHeVl96duzm4dRNkyCWd8oIpOAh4Fw4ClVva/O/H7AVCAJ2AH8VFVz3XmXAne4i/5FVZ9pbH9ZWVmanZ3diu+gY3tvcT7XvbiAC8f25Z6zRwZ834rHv1zLfR+u5I4zhnLZuAHMWFXA07M2MHNNEV3CwzhzVCo/O6Y/I9MTqKyuYc76HXyybCufLt9GfnEZYQJZ/XpyxmF9+OnR/Qhvxgi21744n/cXbyFMYFTf7kzITGJCZhKHpXffv70a9/qCez9cwe6yKq489lCuP3EQ3brUnwhWbd3Nu4vyeXdxPhu37yUuKoJzj0jnp0f3Y1Dvg7sCgzOI3z0frOC52RsZ2ieef14wmgy3vah4byV3vruMtxbkMTw1ngfOH8WQlKZX89RSVS54YjZrCvYwZXQaYeJc/CgCYSKEixAmkBgXxflZfRtNxg9/toZ/fLaal35+ND8Y2OuAeWWV1Rx1z+dMyEzinxce3uyYH/xkFY9Oz2HB708JqArSn/KqasJFmvQDJ9Ss2babc/41i/Se3Xj96h8c9KNka3EZJzwwg2MzEvnPxVltEpOIzFPVgHcWtKQhIuHAauBkIBeYC1yoqst9lnkNeE9VnxGRE4DLVPViEekJZANZgALzgCNUdae/fVrSaLq/fbSSf81Yy69OyuSGkzIaXX7W2iJ++tR3TBqRwmM/GXNAoskp2M0zszbyxvxc9lZUM6xPPLk791JSVkVURBjHZiRxyvBkThzSu8HSSKCqa5RFubv4clUhX64uZFHuLlShR7dIjs1I4uhDe/HWglzmbtjJkf178JcpIxnsDp/SGFVl3sadPD97Ix8s2UpFdQ3jBvXi4qP7cdLQ5P0nreX5JVz/8gJyCvZw5fgB/GbS4Hp7GH20dCt3vL2Ekn1V3HhyBlcde2izTnyfLt/Gz5/N5q4pI7j46H5NXr+usspqTnzgS+KiI3jvl+MPiGnaonyuf2mB39JKIOZt3Mm5/57FIxcezpmjUpu9nT3lVUx+9GvKK2v49SmZTtJsZ8Pl7yit4KzHvmZfRQ3vXDeOtO71Xyj52PQc/v7xKp67YizHZiQFPa5QSho/AO5U1VPd17cCqOq9PsssAyap6mZxzj7FqhovIhcCx6vq/7nL/QeYoaov+dunJY2mU1Vufm0xb8zP5b5zRnLB2EMaXHZrcRk/fGQmCV0jeee68cQ2UHVTUlbJ69m5TFuUz6DesZw8LJljMxIb/IXfGnaWVjAzp2h/EinaU06PbpHcetpQfnREerNPMEV7ynll7mZe/M4ZfTclPpqfHHUIXSLCePCT1XTvFskD549q9Mu9fU85v39nKR8s2crovt15+ILR9OsVE3AcldU1nPrQVwB8fONxrVLVBfDR0i1c/fx8/jR5OJf6tC1d/N/vWFdYyszfTmzRybm6RjniL59ywpDePHj+6GZv59evLuKtBblkJsexcutuhvaJ55bThnBcRmK7uLNjeVU1Fz81h4W5u3jlqqM53G1Tq09ZZTWnPvQVkeFhfHjDsa32v25IU5NGMNs00gDfy3dzgaPqLLMIOAenCutsIE5EejWwbr0VqyJyFXAVwCGHNHzCM/UTEe47dyRFe8q5/e2lJMVFceLQ5IOWq6iq4doX57O3opqXfn50gwkDID46ksvHD+Dy8QOCGfoBesR0YfKoVCaPSqWmRskp3ENyfDQJXVtWJZIYG8W1Ewdx9YSBfLGygOdmb+TBT1cDcPKwZP567mH0jPHfpgLQKzaKx34yhncXb+H3by/l0qlzePeX44mLDiy+l+dsYl1hKU9ektWqJ5FTh6cwblAvHvhkFWeOSqVnTBdyd+7l65wirj8ho8W/5sPDhAmZSXy5qpCaGm3W9t5ZmMcb83O5/sQMbjwxg3cX53P/J6u4dOocxg3qxS2ThjIyPXRvXZy/ax/3friSORt28PAFo/0mDIDoyHB+f8Ywrnw2m2e/3cgVbfg9CoTXlYM3AxNEZAEwAcgDmtRPUVWfUNUsVc1KSgp+Ua4jigwP418XjWFYn3iufXE+8zcdXAt4zwcrmLdxJ38997D9dfahKixMyEyOa3HC8BUeJpw8LJlnLx/L9JuP58WfH8UTFx8RUMKoJSJMHpXKk5dksWnH3oAv5tpdVslDn63hqAE9OWlo4FdlBxrTnWcOp7Simvs/WQXAG/PyUIUfBTA4YSAmDu7N9tIKluY3vSfZ5h17ueOtpYw5pDvXnzCIsDDhrNFpfHbTBP7ww2Eszy/hzEe/5vqXFrBp+95Wibc1lJZX8ca8XC56ajbj/voF7y7K56aTMzlrdGCdCk4c2psJmUk89OnqFvXoC4ZgJo08wPeKoHR32n6qmq+q56jq4cDt7rRdgaxrWldMVARTf3YkyfHRXPH0XNYVft8Vd9qifJ6etYHLxvVvUb10RzEgMYZjBja/WmTsgJ7cdHIm0xbl82p242NpPf7lWraXVnD7GUODUhWTkRzHpT/oz0tzNrEkt5jX5m1m3KBerdZ757jMJERg+sqmdb2tqq7hV68sRIGHLzj8gDaXqIhwLh8/gC9/O5HrJg7ik+VbOfHBGTw2PYcaj27YVVOjzMop4tevLuLIuz/j168tYtOOvVx/QgZf/uZ4rj+x8TbDWiLCH84cxr7Kav7+0aogRt10wWzTiMBpCD8R54Q/F/iJqi7zWSYR2KGqNSJyN1Ctqn9wG8LnAbV9AefjNITv8LdPa9NouQ1FpZz771l07RLOm784huK9lZz12DcM6xPPS1cdHfT61c6iuka5ZOp3zNu4k2nXjd9/hX5d+bv2MfH+GUwakcLDFzS/F1NjivdVcsL9M4gMD2NrSRkPXzA64F/FgZjy2DcAvH3tuIDXeeiz1Tz02ZqAYtlWUsZd7y3nvcVbONFtP2lKb61tJWV8uaqQuOgIesR0oVdMF3rEdKFHty4H9e7bU17F1uJ9bC0uZ0vxPraVlJFfXMaMlQXkF5cRFxXBGYf14Zwx6WT169GiKr57PljBkzPX8fYvxh1wy4DWFDIN4W4wpwMP4XS5naqqd4vIn4FsVZ0mIj8C7sXpIfUVcK2qlrvrXg7c5m7qblX9X2P7s6TROhZt3sUFT8zm0KQY9lVWU7Kvkvd+eazfsaFM0xXsLuP0h2fSo1sXpl03nq5dDu55ddOrC3lv8RY+v2lC0PvtvzJ3E797Ywlx0RHMvf2kJl8/48/Dn63hoc9Xk337SQH1nMvesIPz//MtZ41O4x8/DqwBXVV5bvZG7npvOSkJ0fz7oiMYkea/raO8qpr/fr2eR7/IYW89Q9OIQELXSHrGdEGAbSXl9V5U2qNbJIeld+ecMWmcOjyl1Y7d7rJKJt7/JcnxUfzi+EEM7B1D/14xrfq/Camk0dYsabSe6asKuPIZ51g+f8VRB/XjN61j5ppCLpk6hx9n9eW+cw87YN7SvGLOfPRrrjruUG49bWjQY6mpUa58NptR6d0D6n7dFItzdzH50W/4x49Hcfbh/ttKSsoqOe2hmYSFwQfXHxtwZ4FaCzbt5NoX5lNUWsGfJw/nx0f2PahaT1X5fEUBd72/nI3b93LysGRuPCmDMBF2lFawvbSCnT5/d5RWUF2jpCREk5IQTZ+EaJLjv//bmifxut5fvIUbXl5AlVvtJgLpPboyMCnW5xHD2AE9m1V9aUnDkkar+XJ1IeWV1ZwyPMXrUDq02mtlfKthVJWLnvqOFVtKmPGbia3aqO+Fmhpl7D2fMW5Qot9qNlXlhpcX8v6SLbx29Q8Y00hPo4bsKK3ghpcXMHNNEecdkc5dU0bsP7GvLdzDn99dzperCxmYFMMfzxzOcZmh3Ylmb0UV64tKWVtYytqCPawt3MPawlLWFe6hvKqGxNgosu84qVnbDqUut6admxDiX6SO4qaTM5mzfge3vbmEUend6Z8Yw4xVhcxau50/njms3ScMcHq0TcjszSfLtvLgJ6sYlprA8NR40nt0PeDX8VsL8pi2KJ9fn5zZ7IQB0DOmC09fNpaHP1/DPz9fw9L8Ev7+o8N4Z2Ee//tmA10jw7njjKFcekz/dtFO161LBMNTExieemB1W02Nkl+8j8LdbdfDykoaxoSAvF37OP3hmfTt2ZXX/u8YJj/6NZXVNXzyqwl0iQj9k1og5m3cya1vLianYA+1HZzioyMYlhrP8NQEDk2K4Z73VzA8NYGXrjq6WcPL1Gf6qgJ+9cpCdu2tRATOP6IvN586mKS4lo1K0FFY9ZQlDdNO1Q4TMiTFuer58Z+OYdKIPl6H1er2VVSzcmsJy/JLWL7F+btySwnlVTXER0fw4Y3HNTjERnNt3rGX/369nnPGpHFYenB6IbVXljQsaZh27E/vLuN/32wgq18PXrv6B+1iiIzWUFVdw7qiUrpGhofs6K4dlbVpGNOO3XLaEGK6RHD2mLROkzDAudNfQ9eqmNBiScOYEBIVEc7NPvdYMCbUdIwWNmOMMW3CkoYxxpiAWdIwxhgTMEsaxhhjAmZJwxhjTMAsaRhjjAmYJQ1jjDEBs6RhjDEmYB1qGBERKQQ2NjA7EShqw3CawmJrHouteSy25umosfVT1YCHtO5QScMfEcluyvgqbcliax6LrXkstuax2BxWPWWMMSZgljSMMcYErDMljSe8DsAPi615LLbmsdiax2KjE7VpGGOMabnOVNIwxhjTQpY0jDHGBKzDJw0RmSQiq0QkR0Ru8TqeukRkg4gsEZGFIuLpvWpFZKqIFIjIUp9pPUXkUxFZ4/7tEUKx3Skiee6xWygip3sUW18RmS4iy0VkmYjc4E739Nj5iStUjlu0iMwRkUVufH9ypw8Qke/c7+wrItIlhGJ7WkTW+xy70W0dmxtHuIgsEJH33Ndtd8xUtcM+gHBgLXAo0AVYBAzzOq46MW4AEr2Ow43lOGAMsNRn2t+AW9zntwB/DaHY7gRuDoHj1gcY4z6PA1YDw7w+dn7iCpXjJkCs+zwS+A44GngVuMCd/jhwTQjF9jTwoxA4djcBLwLvua/b7Jh19JLGWCBHVdepagXwMnCWxzGFLFX9CthRZ/JZwDPu82eAKW0alKuB2EKCqm5R1fnu893ACiANj4+dn7hCgjr2uC8j3YcCJwCvu9M9+cz5ic1zIpIOnAE85b4W2vCYdfSkkQZs9nmdSwh9aVwKfCIi80TkKq+DqUeyqm5xn28Fkr0Mph7Xichit/rKk6ozXyLSHzgc55dpyBy7OnFBiBw3t5plIVAAfIpTM7BLVavcRTz7ztaNTVVrj93d7rH7h4hEeRDaQ8BvgRr3dS/a8Jh19KTRHoxX1THAacC1InKc1wE1RJ2yb0j82nL9GxgIjAa2AA94GYyIxAJvADeqaonvPC+PXT1xhcxxU9VqVR0NpOPUDAzxKpa66sYmIiOAW3FiPBLoCfyuLWMSkR8CBao6ry3366ujJ408oK/P63R3WshQ1Tz3bwHwFs4XJ5RsE5E+AO7fAo/j2U9Vt7lf7BrgSTw8diISiXNifkFV33Qne37s6osrlI5bLVXdBUwHfgB0F5EId5bn31mf2Ca5VX6qquXA/2j7YzcOmCwiG3Cq208AHqYNj1lHTxpzgQy3Z0EX4AJgmscx7SciMSISV/scOAVY6n+tNjcNuNR9finwjoexHKD2hOw6G4+OnVun/F9ghao+6DPL02PXUFwhdNySRKS7+7wrcDJOu8t04EfuYp585hqIbaXPjwDBaTdo02Onqreqarqq9sc5n32hqhfRlsfM614AwX4Ap+P0GlkL3O51PHViOxSnR9ciYJnX8QEv4VRXVOLUi16BU1/6ObAG+AzoGUKxPQcsARbjnKD7eBTbeJyqp8XAQvdxutfHzk9coXLcDgMWuHEsBf7gTj8UmAPkAK8BUSEU2xfusVsKPI/bw8qj43c83/eearNjZsOIGGOMCVhHr54yxhjTiixpGGOMCZglDWOMMQGzpGGMMSZgljSMMcYEzJKGafdEREXkAZ/XN4vIna207adF5EeNL9ni/ZwnIitEZLrPtJE+o6nu8Bld9bNgx2NMQyxpmI6gHDhHRBK9DsSXzxW6gbgC+LmqTqydoKpLVHW0OkNZTAN+474+qQX7MaZFLGmYjqAK5x7Jv6o7o25JQUT2uH+PF5EvReQdEVknIveJyEXuPRSWiMhAn82cJCLZIrLaHfundjC7v4vIXHfwuv/z2e5MEZkGLK8nngvd7S8Vkb+60/6AcyHef0Xk74G8YRE5SURmuPdTWOJOu9SNf6GI/EtEwtzpp4nItyIy373XQow7/e/i3GtjcW0sxjTGfqGYjuIxYLGI/K0J64wChuIMub4OeEpVx4pzs6JfAje6y/XHGWNoIDBdRAYBlwDFqnqkO9LpNyLyibv8GGCEqq733ZmIpAJ/BY4AduKMbjxFVf8sIifg3OOiKTfiysK5P8wmdzC9s4FjVLVKRJ4ALnCrsm4BTlTVvSJyO3CDiPwX5+rw4aqqtUNmGNMYSxqmQ1DVEhF5Frge2BfganPVHbpcRNYCtSf9JcBEn+VeVWdwvzUisg5nlNNTgMN8SjEJQAZQAcypmzBcRwIzVLXQ3ecLODeXejvAeOv6VlU3uc9Pcref7QyLRFec2wLsxbnx0ix3ehfga5xEWQM8KSLvA+81MwbTyVjSMB3JQ8B8nNFHa1XhVsO61TW+t8Es93le4/O6hgO/G3XH2lGcO7v9UlU/9p0hIscDpc0Lv8l89yPAVFX9fZ14zgY+UtWL664sIlk4A/GdB1yDkwiN8cvaNEyHoao7cG57eYXP5A041UEAk3HuwNZU54lImNvOcSiwCvgYuMYdehwRyaxtK/BjDjBBRBJFJBy4EP+mB9AAAADNSURBVPiyGfHU5zPg/NrOACLSS0QOAWa5+zzUnR4jIhnu6MrxqvoeTlvQ4a0Uh+ngrKRhOpoHgOt8Xj8JvCMii4CPaF4pYBPOCT8euFpVy0TkKZy2jvnuMNmFNHKLTVXdIiK34AxjLcD7qtoqQ1ir6hIR+RPwmVuiqnRjnSsiVwCviHN7AIDbcKrw3nTbY8Jw7jltTKNslFtjjDEBs+opY4wxAbOkYYwxJmCWNIwxxgTMkoYxxpiAWdIwxhgTMEsaxhhjAmZJwxhjTMD+H2gnHIEF4v8eAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4442,7 +13435,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -4488,6 +13481,8 @@
     "    print('Out of Bag error = {}'.format(np.mean(oob_error_per_fold)))\n",
     "    print('-----------------------------------------------------')\n",
     "    j = j + 1\n",
+    "    \n",
+    "best_depth = np.argmin(total_rmse_per_fold_test)\n",
     "\n",
     "plt.figure()\n",
     "plt.plot(depth_array, total_oob_error_per_fold)\n",
@@ -4503,6 +13498,26 @@
     "    \n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_depth"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -4512,14 +13527,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_depth = best_depth + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Feature Importances for best Random Forest Regression :  [0.39769433 0.100803   0.26573009 0.01952733 0.21624524]\n"
+      "Feature Importances for best Random Forest Regression :  [0.21463007 0.11396964 0.37938158 0.00538934 0.28662937]\n"
      ]
     },
     {
@@ -4533,7 +13557,7 @@
    ],
    "source": [
     "model = RandomForestRegressor(n_estimators = Best_num_trees, max_features = Best_num_features, \n",
-    "                              max_depth = depth_trees, bootstrap = Bootstrap, oob_score=True)\n",
+    "                              max_depth = best_depth, bootstrap = Bootstrap, oob_score=True)\n",
     "\n",
     "model.fit(X_encoded,Y_encoded_rf)\n",
     "\n",