From 4403420cbf13ee6b665dd00f1ebc93cb743f7b00 Mon Sep 17 00:00:00 2001 From: thecyclone Date: Tue, 5 Mar 2019 12:26:01 -0800 Subject: [PATCH] changed RF --- .DS_Store | Bin 6148 -> 6148 bytes .ipynb_checkpoints/Dataset1-checkpoint.ipynb | 11300 +++++++++++++++-- Dataset1.ipynb | 11300 +++++++++++++++-- 3 files changed, 20324 insertions(+), 2276 deletions(-) diff --git a/.DS_Store b/.DS_Store index 632e16948d5fec43ea0fe95027f94df80576990e..4c1ecd0d60ff41d26546c0c782021f2de657e26d 100644 GIT binary patch delta 146 zcmZoMXfc@J&&ahgU^gQp*JK_hdqF-17luTJ5+E*SNCnbHlNFex6AzLWE-Y{$t#(5>T@v!0d=PVaS;&bF(d=YV$YoXlO?vk`Cr 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": [ - "" + "" ] }, - "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", 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\n", + "image/png": 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sj9sqh3Nb4zCyRT50+/BjDN/WmQGVn4bM/ZB2AlQY/AnQMgtap0PWQYjxQncGxDeK9Z5YD22TIK4FAnEQiIXYFqhfAi9vgk47LNgMCx+B+FawdEJXJnTloE6sRs/bDInVULuCSZfVk96whmsXr2PyZPjRb0upGV9M8O0i4jryMad48C3fQEx2BfE9qwj3pBJ+z0l/s50rulZxzwahhBzRTnbsgPLQfNZ+wUn5PjvtM4DELPQUSPE6Kfy0Hbt9qBYYGS4XbN0qv4cKf1eli61HZMO6Bf8LtcIHIKI0IjgvlJbCigdvITj7NyKUwgxaygMBTCPeCZzNmY8VQ/eZN+RY1hDv48U/DgF/LhplJAE9EoZa50N/N84Fkx+Sa6HqSkjyyHJ8swh171QYfxhivSKQW2dAXxrk7gRrH1SvEM597hOiUD2XQzAGsveCKSxB2t3fgt13ioCfuAe6c4lvu4INKzaSNstOejr8689KqUgpJvDuarjscfEE6vMx1VxF+FQBtmw3k+McdES5iap10uS3069AVzgxW8A0/UUCk54D20kscT50YBehlCpJRY1uoac+m63fd5A/EW69Fbzv5jPLnk9UFgTHw/TpdvIWbyR5kYuCLCfu3XYcjqG0kZ18w0PInwUD3gIrBwZ40IMYKwYC08MD1M4pzrP+nw+GUlbwwfTVpUhxRZTG3wlmLPVQPmEDzPyjBPf64wGzYSFGQ0IdoESwAKgAWP2Dgi9sgpACS0iWAxbJcjExpI0SrtoRGp1aGR6gHI16ORdG22ckrnv4+pGUyGhcObzfwvdHQV86RPWApUd4+eZZcHoOpJ0Ua/z0XEipku1JHojqhtap8PLDMGsbxDbDFBd02lF/eRg9+3GY/BfYVySZQBllktuftV+ybVpnSl9sPrLLfsVXvgI7GoupbdG0Zb4APWmynyUAyR7wpYF3iuw/4QB0ZYuCsfRDxhEo/zS058BpB7TMAXchphQPKt5L6NhqUNAbSObJenjmMRFWjx7ZyfH6PXBZj2Q8WXswJZ5GzdwOPalEHV1HRxyEw/n0m8BmA5MJejvthPetw9TsgNgqSGgkVDcfSougNRvGlRPnyyN7yR7czW7yJ+ZTVAQnT0IgAP/4j5CaOqgYOEsxDKONLgAG4hvt7ZyJf+zcKduGxkJA2u3ZA9VtHnZ2i1f08lOiCUajsYZ6O07npaFAIkrjAmD76x6+/viD1MU9L4ZzZw6kHwVtgZjTEIyV9MTucWACdcqJbe/d9PuBBcWQtwNS34OATRSECsOKdKERzIYlHttlnK19bJ0yhc9+GqKCg7/PKAg9eHwYzFQZKqiHegmjCeoPwvuOaYJQFDQsgAw3UVWfJ0FNpCP9RQKJR0SYBqMMz8ckArY/SbKF/Cmw7w6Y9DqkVAiN0pcCnitQOW+hrd1wepYI295Uub79t8t5lz9MQlQ82b1fJ3B0Hd3d0Oz3EJ5ZIjROShVY/GLxx7TB9bcJF6/CopBTK9DXfh3MfpkrMKcEGhbKBdpLxXuIbxYhbQpBuJK6jmJ+tht6srejk2Mguh0a50iqZ1QbZB6C3DfA2ilKwtojQd+qT4K1F9LLhWpKOyljuP9rMNVFTLqXngkvw+E1AFgWbqWqOpWSknXceSfMnw9/8YKvNQ/VsJTY2a/jSzxKZmgx3a1Ounqga+CRSvJgnuFios9JdQ3oKS50tRP++AzmPBehE05Ul50Mfz633Qar1npw+6acsdzz8+GJJ84tIEtrSyneV0zR4iLyJ4rm8HR4cFWOHGvweKDkBQ/VCSU0NcH6awvJyuJMezrtuFzgcIjQb2uDQ4egvBy8XrjzzsFjDaWnOLiOLVukfUqKKIk9e2ST2w11dYP7bdkiinD8tS4qYrbimAJr1qw7Z6rtUG9nNLrsYkNEaVwAPPC0i7q030J0h6xIqzhbSNqM9bFeAHRCI/0NC2Tdwi0Q3yDto4bkmVuaRIloRACFgHC0WKWqH+JbJP1Q2ySH3tL/fmEfNAsFNEAzmRhZASijfV8aHPmSTGSy9UDjfHh9I2rJI+hF/yXCrDsDojuFpvAni3BrmS7CL/MQqi+TjH0/p2BKPr0WD+80u6DSybJZdubPhw7t4ajfBRVOHLMh5yoXhYsHhMB9vFlRym9PbEJHtUJKpVjg/WnQkgdz/wDxp2HaizIO/iSouoyc9ExCeZU0l1+LvzlXePfxx6ArCxIbRVD7kqFsHSkZydS96aSnAcKZpfC59ZBYJ+MHoqz6UiG2VZQFCuoXQcp7YO2G+Cbx0ECW8/4sRkHAAkGbBIJNClQQvNNIrSqiqAh+44b6BtCTX4K65ah3RKrpZQ9hziwHs4mQbhSlpTRRfdOZE1xF1TE33qgySKqW+MPyTTChjL4eOyZfGrrFga0/i8kzIdQ2KM3uuKKQ8kOpvFjuJOi1M3UuNCdXMd+0lKNmO30WiWmbzRCY6kLN2crUVKACGtK2kjQOUqvXMTd3HUe6RIibUjy80eliFU6c6etwPW0I7UMemOqi8AujB5qL9xXz7PFnOdl6kie++AT2JPsZYe71yrmGKhyXC7a86aIudwuBAMS5Uiko4Czhv3UrZGeL0G9rG7ye4TiLnkoXpTLU03A4pN1wT8PrNdb/gxO3T/a3Xz3i5Z2B3T6oMBwOWLPm/XTZxYaI0vgI8EE85nDe8u4vOPn6418+L0/D0uYgMGEnuL8gluZwT6NtOnRnoVI85HTeSGdzMn19MDu+gIrATjraIaaqEEtKPf1XfBfr+Ari4yz0HluBzzOHm+YXUvRPdjb9ykPG5S6SWpwkm+zk5cE9xaWczPsG/bEVhAMmVOMyxofm096YjK28kPC7kL7UxVWTnWTNsdOl7uDtpilUlTroWbGe/ugGCFvAswxr81ICji0Q10JKkpnLVSErp+VTWAhgp6RETKzBVMfhXLX83vy0WGVlZfno5ich0QMrN0DOWxBuhNqlotQSa4Xf78nEVH8Fse9spO8zG2gx7QbTOLKr76R3/kO096dgaV6GtWoq4SgvwZkvo4+sIal5HSYrxI2H0wWbCGftJ6pvEn7vFEwmsDUtp+/kUlj6U0itACxQu4z0fT8nbfkOPIGD6J7x9AV6UJXXihKw9sJ4NwR94EvBfHoJ02KWck1WITlftlNwlQdyHbxT1k5H07do7ygkeSH09EBDbSFhayopWa3U5PwIFdUPSXXo2Gdp90zFUu3ElOnFevgb0JeMJbGN3uhdEHsE3ZtOSp6bxIp8pnauY+UajHEHe5KdTWvXkdcj1vfawkJaklJxRDvZVCZtZs+W/4eqnGRa4Ee3yQO//pdQ1+TEbIZPfxoefliompJyF261lWIXFMQPEdoBF3i3kpo6eqC5aHERJ1tPEggHcFW6WLdg3Rlh7t3rZOtTxtNg7O50gjfopDrBS1MTFDmdZGUZ2wzhDyKYHQ5RGkqJIhgYgwHYk+yD/UoSL2TgHW5qEqWTlzeYdTVAYbW1iTd2770w+VNw8CmoOgQxGR5OBF3kRDto0m6KrnHSdsrOkbZSdvmKaXmxiN6mLMzzSkjLbue/T2h0Xwot+wswZ7n5hxlO7rrDftFQVpF5Gh8SAw+RwwEbN8qDNGMG9PdDb6+4sps2SdtvfANaW+HrXz/bDR7zuTo8XHX/eip690KzA7yTIbEJyq+FWc9AXJNQIJ12yH0LDt1MbNcCemc9AtGt4I+HxgXCqV+zXoKhKiBex7tfgu1bmDsX3n139GstecHDs++VUFYG8yyFrP6k/X357QN46CFx1SdNgoOnS2mdsxF/QKEO3E5Mdjl+UzsZGTB/ZjK3X15IS6X9vHncgfFvbYWf/lR48YSJHvzTS+jqgstMhbT66+lwbKK5KpPYQA7rlhZy9B07s68p5XfHikk4UcRV0/OZPN9D8csulo1zcneRnfpuD/f+Qbyb29fYKS+HPe3becF6Oz46oT0XTQilrZhL15Nw2klgZgkdMXsgqwyzsnJD5nqe/M46HnyilA1/KiZUWkRsHHTM2gT+WEh7D2JaiGtfTl9nPFG5B/CbvWjPMpJya2iPfweTKUyoNReOfhFOXA8Zbqw1TgKtdvj8rTBrKworV035BJ2HVxIoK8RjdeHN2gpH1jA3uA6V5OGEtYTsaW2kxqRQVFDIrj/bqaiAW24ZGw0yFqNopHkNpcc8FLtcIsDjh9BDA57G4nOntI5ER3k6PJTsk3sjWVIj9/GRR6Cs0sPk66VjTX8pZO0qOy0t0BpTyoO7NtHfmkGKKZclMYV426AuyoVnv4MouxvzKSdp6XAy5CK600HCVDft9emE87YROLKa6JQW8pIdfHadG1XppPgBO514CMwogZzXsU50E66VLDM92UXYsRX6kmG8m+zTtxLedSfeglvpn7Qdjq8CT4F4hdFt4sn7UsCzFJLqSPKs4eGb1/3NKauxztOIKI0PAU+Hh1vuc7H7cSeWHjtdSl4CwhoWbIG37oaTqzAZ9HooBNHR8K1vwQ9/eP7n+5fNm/l52f3ihYSixFK19kJ/Iti6hW4CEUamMDTNgRd+Lgoi4zD0jBOrO2SV9oQhZBNF89p9cHIV//Iv8LOfDblGQ1G0pbpI8TpJSYV7f+8iVO5k3mQ77loPphUPoqa6yIjNIvnENzl0soV5ExzsD/yepujXUY0L0R0TIfc16UfbJCnRUHYztv13Y5kvL/SknkLWf9X+vpdiuICo7/bwrc0lnDwJmS2F2Hx22tshNxe+9DUPu+pdrMgyZuXmwfXXi1IbmoVT3+1h4x9cNB12UGfeSdjWTrg3ma53ColOr6dndjFZHatpiy0lkH6IKO88zO+twrvyJomJgHiA9UvFI3x7PeRth2X/BWELtqbLWWFfyY++VEhTE9zwh38ilHZIaLqWmTD5NQibJfYBMkchuVLSXNGStKCtYO0yKEET9GTAe9dCUh0cWQMVTilRkVFGtmU+T3/3jjNCWSd6+P5vXPQcdnLFHDu1tdDdLdb/qlWD93akeMK5Ygbni+HnGL5cWioWeVOTWOdTp0JFBaSliRXf3O9h8qdcqN50KmzbmOpfTWdiKa9UvUxnXy89rSlY3bfCzG34dR+x3uVEVV1PTK6bzMR0ysKPE6IXNb4cpa2Ed60nusuB/7JiwrZmSUxQGvqTMJXeRTgMzN8iyQMx7TLOAHO2Qke2jL25V2jM07Mwq1hMXdmkT61jRdIaXvzxOrqmbRbBH9OCOaofeseTcOjb2DxO/HYX/rgKwnnPssh8K6efvZNxC0o5mVhMx1+KiPKLpxGX1s6ECRfG07gkJvcppa4Dforky/yP1vr+YdujgN8Ci4BWoFBrferj7udwuCpd7OnZSt8E5AV2rocJZWDphcQGSP46lJUS7k+B6gLIcGNrdpKSYlhLw1+gIRZZ/qz3Pxnv/N4JPi9EGUHtWU9DWjkQFmEU1SHUVk+6vAjKBBluyaVfXAzHV2O9bBuh1KMo7wJsuzbSN8ElL0R8C4sWwV13caZvDz4Iv/2Th46l6yVIW/0m5O6C8QngO0jtxDchp18Ks3X0UdF+HHqbYZKPOtUPE6R2j049jtQT8ktsZPxh+T/3d/invII/pQoIc7Tmeb55z2/49rft5MzxcFiX4I+uEWGcWA/dqdzljgZbl8ROZplobv8fGevW6VS88Ate3fx7uOwxHtu3BF56DF6GTbtLUApmhQtpOG7nYIWHN8wbOFxXAXoqZO7BFN9GuCcFPKn0T30Rpv+ZmuYjEreIbcGXvguiDkkabNAi2WXBWKheCdNelnFOOykF84LRjO9byU2T7yR/Fqz45WZCKiApsPHNkiV18h8gphVSy8GXycK+uznWtIeQ/TX8Ji/ULyMnI542y1ESU32cro8iUHk56r3riba7mYCTqukuoqbvwXp8DbEN63Dvhvx1A16DnWvy152x6ouLRRi3tAw+T6NNSvug+QmjWfnDt7fvd1L2pv1MkLmgANavl6yibz7mYsNNTt54A55vcKHfk/hUeTnoBA/Eunj7FQdqSTFqd4U84+nHsHacRKVW4qMNQgmQVkf/oh9DUg2YwvSkHqPHVIdKrKMxqpdQ+kHwx6Orr0TXLYUKJ76VG2DadqhZBs1zhE40KKr2dmNivjePmP4pBKqdpKVB81EhfG5jAAAgAElEQVQwtzpgvJtAZzpmxzYm9a5mxXUttLgdzFvsZpXDyfRueP2gk6YuL+NT28mbrun1prD2c05aKu04HOsMD2sqBVlOdk7zUG1xk/LKRub9k51Vaz3srE+FikLy8mDbuy6KvpZF/qwLnD42Ai6Y0lBKmYFfAE6gFtirlNqutT46pNltQJvWeppSag3wH0Dh+4/28cI5xck8M7xV4RQPY0KZWPDeyRIQtfVKwNqXIjNdk+rIi4bCQnkRh2dJFLtcbK/cCi7In/X+l/Web9p54IE7uXI5PPccdB+5nrqcTVja88hPuZ7ZK93sf9FBm8VN/mQHCQvcNGmn4Y7n4/w51KuMM9ko1NnZ9CsnGZeDynFSWSbu/B13CM3w37/3EFyxASa+A9FecJRIQba+VJj1R0hoko6FzBKTCdpA+UV5xTQPBs2DVojulbYDGVEKiO6CmC4jcAxM3EP34g107yuiOaEYpvwF4hqMTCSEioMhwXgF40/IcvZ+uHwTZO2R1NesvXJPAN/MLQBUHkvF37GOd5pdNCRXSHrsviIS/A6uXdXO6y8m01LpRKVWovt3kdK7nKmpCRzpeQ6f7pK4UigB68lP4k94jxzP3cSEFnDqZCrZOInrcXC6HQpm55E/ufAMRXL3F5x86xcQrE7HMmcbn76siB3F+cy53ENshov1nxfapuSFBbSlTkFVOukw2SX7Z/3ZfHlNu4fGODdrl0N5uZMjPninzEnaNA8vdJawcV0bXV2K7LRkxp0uJDoavvvKI1imvUZLYhfV27/JT95Q3Herk1UrRzZZh89PGG7cuCpdbNm7FY5AquVsusTjgQ3bXPyl9TGay3cSfWQjtj47e/bA6wc87A+7CKR5CU16mfv/CONyvHD5FsxxXnj7TuKzPHQs2CCzxUNWtDWAbpoNx1djnrONmLrVOJyldNlOYmm8lsMduzDVrTjjaaT1LSd//PXU+NzMGJfOiajHqXLnMbHpDlLNdpgDrd1FnKqG4O4i6M4i9zMlzJoFt68v5Hd/qufVTjdFX7ielN582ldCRwccPbqO+uRS6ibsZIm/iK5jW5g5E06WQJPPQ5vVTVcZJCZ5WPhFFwdeKqC64fcc8b8OzTN4rep1+mvmEfjxKoIpbjJ6nPyHD0LO9bTF7oaEn/By+UIeeLSUUEwDuj0Hnrge8p7nsaqNJHlXkmmewwNfLhz1vn3cuGD0lFJqOfADrfW1xvJ3ALTWPx7S5iWjzW6llAVoBMbpc3T644ppPPSQWORtIQ+2RSUkxIOtoYDxS3ZiTWwn4Nd4TqbQeaSA2Ve6+cW3Br2I8/U0RsJIFMNo3LLHA0WPbmZ372MkmjIJVi8ltrKQmBhoinfRXOrEbIKE5SWYzdDcjKEc6iD2tAhvrWT+gjkg8xO0krTSoE3mLZgDQn/FN4iV3p0O4RhROv4YOYZ3KuYYP6p1BuEoLwnjOunXXfgas2ROiHeqWH+JNZDQCL44IzvIUC6Wfgmot02GzgmiMFqnQ9V1WGc/R8DSjq1qFYmH76KrG2IWl5CRAbflSyxj9Vc8lLa4eOcPTmL8du65RwRzaanEn3SCBCxnWCRldO3XPTzzros/b3EQNdFNbsjJoml2yCql0V7M9I4ifv9APitXgmO5h1+86MIxzoHKcDOuy8npk3bi4uC114RHj/7kJoKN08lpLsJigWqzC3rTCSx9AMwBPpHtpLS6jN7+Pkyts7HYFP6YColH+ZJhxnYZo9c3SvwKiLl8M8Fl9xOwnoaQReZ9lH5L0oev+p5BewHeKajOKVyeuIZd/zU2cnzzZjFu1qwR42YkT+PXT3m49/cuouud1NaCXrmB3pijWDpmk3O6iLqAm0kzvdTGb8fSl0nXqelERSsC43cTTD0CB74K+4vgyg0w/qgYGuYAtMxmRsNG0ix2oqNh2TIoKhqkuu65B7Ztg74+WLwY+idtp3LCA4TevBtLxSp6LR4CM0sIx1cTPaGKqL7JdASaIPttqL0cvHkyV0Yp+Z95SNKV37sO0t1CRWoLnPy0pFZPfQX642DXtyWxojsDUqvEKKxbgsmXAY6nCdcshJxdkhkZVvKehG2Sap1aBQfXQfsUuOJ+Sd5QQTmPOQAqJPOCjn1e5t+kVkotrfbJXGFaP+b79mFxKdBT2YBnyHItMNwXO9NGax1USnUAaUDL0EZKqduB2wFycnL+Vv09C4WFA5OS7NjtQ6PbI7mTRp75EKFeUDD4Oy/PDgfX0TQJNu9m2EzYkc8/EsXgckkAGqRvA9tdLti31UnbnJ202d+C8W7iG1Px+SA8dSt6CgTC4M2RneN6b6XXOxsd1ywPvCksPHxHDlRcC75kokihf04x6b5l5HaupSK5mC5rBeroLZBYR07PaiYtKic1FfY+VUB/kpv71jm57Yvvv6Dtr3t44GkXVy5xsKPUjf9UOqfTtpF4ooiVi7NImOfiwEsOKgI7adfVxGU08aX8tbxSVk5fuJ30xZCU+FlmT0kmKa+AgwWP8GZ5GZb2+fj23UHJu3aYWMprz23C15pBOOzlyoxCip+AwvtdtNSk48t7nFBrHFh7OPyehpnP8MctPlSLA51cAa0p1Lmh4gC0fWoT/q4/w6nTkHUVv6lpg+6TkFZNdcCKqdGE1Q2B406Z0zG3HaZvp2/8MZgQQ8XBTsmAS66FQJTM8ehN5zXPdsg8DipIOPdN/GFEmU52QdNciGsFW6d4Ugfl5sY1OlF9L9Ic3Af+BJStj4QECFU56X23kuT5r+Gni4yGbxIXp7j7C2PP55SMJA/eXBeeDolzFOatw1UN9fXy/P74RSPwHgDq18He1fDp1wgmNFBJD8Q3cqIrE3yJMG4f5FbRF9MhXmnYLHNmZpfIPJqweVCI+uKpDR6katIG/CdW8Gr5Szz4r5pA/Cl4/R6J86z6LiTUsbt2GWTvgDgvXHMDtOdCfyqMOwbmfnzmIL6wUd7XrCHhaZkMGTIq21r6BxXuxN0w4aAIcAXE/g48K2Q51gsr7pcaWqFoCMRISfUJZYT7ksDSAQqsx79MYOozknJu9ouXnlQNUR1ETyzHfOIOgm4vwfhqYjKrsAXS6Y3yEJ/kI9CVTOee9dhSm/Avu5fEPgeZ5jnc/eWLJw/3QnoaXwSu01p/1Vi+GcjXWn9jSJsjRptaY7nCaNMy0jHh4q5y+73vSUAyEOMheZGLYLmkKUbPdtG618kcu51ArIf6aBdZPif/5yvvDw6fC+fyNIqL4Z1jHjrtJfj9sHZ+IW+8AX1ZLuwBJydPQl1yCdHRcNdnCqmqhKdPlFDvbSc+rZNAVCM5deuZm5JPT88gfTKA0tpSvvXUJqoP5PHvq+7gGic88HwJLhckVBeSmATRi0o4dAguTyyEhHp2mjYyebLitmn3sO23WbRNfYSuuDJqTqTjj/bQTwdh/CScuhmduZfu1liY/DrENUvA2KxFyPSmQU82JkyEm6fCjB0yc7s3Ff7ygAjYVbeC40mx5Lqyse5bj9kMvhmPiWeTWC8TA7UZuibIzGuCIlyCsUI17l5PzPF19C37npTkqDesx+g2yVDrHg/mAOa22cxv20hZqITQ4oc580EgazcE4sVTmnBIEhj80UbZ7lg5ly9BvtEQ14J8Q8LwsoJRgAmaHHC0ENyF0GnnK1+BhGyZ33LNfAdvHHNz65VOqsrsHDwoAeaEhLPTS0cKTA8say2Gx623yj4VGQ/xW/ejBFonMjE1ncAb64luzWf2bMkYbOzz0JnuIljvgJydQmWOP2x4hFOhwy73S4Ul/hCIg7rF4iEqY/7K9B2SKKFNMscI5P56p0NyjXizUd2S8WcKg2e5bLvst7IcjJaYk9kYK42cX+nBeUvaJLP9o/qgJwUCSRL0BvFwmmfLF/da8iD/p2d7GofXwtzHha5tnwST3sTSNodwzXImToT+jmTGm/NIWLKNlIoibv+HfPYc91DW5UL3pOMObeOmFSuot+6iaHERWTr/opz5fdFnT13q9NSHwY03wh//CCzYjJq7FdvJNdisYFmwlfChNXx57jqeb9jMqfitTOpew2sPrzvnQzUiRWVkwKSHHTz+sps8s5M7/sl+1vYBiqFgnp0dO2D3UQ84SkhNgwO/LaSnXrKS+qM92K58AJXzDgkniggEFB0HhLqJnV5KaNkmfC0ZxPhzSRzXTlPWZghEkdyxkrSwg6qkLZKVsnu9nLzgXgkC13wCQrGQ9ycgTHJvPh2ln0cvexhiWozsIsNdR0mA39ZjVEE16imZ/CJ0AgmSlTVQHXVfEVx/u9AGpx2w9TmhcrJKYfkmzP0ZpJpzuTKtkPgE2N61AW/SKxDdgdU7l3BHNvFN19JtfwaT1YfJ66C/IwFTIIXp/YVkx9vRiR4OdriYmJqOJ/VxMuMyOHUol+yQ1GjyHXXyz2vtlFoe4qmqR8FrN2iGqeKt9abA5Q9JHKhtJiR44PQi6MgiWqUQ7EwhuORBSXywBKDTKBMCYu3auqF2OZa964mf4sbncWDLdpNsS6dm3COgFFHv3EOgKp9wvMfItDoIfeOJy64hxhJDbvU9nD6QT1yWh6wVLnxHnby3347VKhlNubmQNdNDX8F6Dne/QX+wD2UOYTlxI0sbtrBpk8ys/vWv4cQJ0PONzCFLL/Smi4WdXCOJIv44iVV05ELd0jMKjwXGPqknZCa+3yZKW5sk4aDucjD1y4eUgrEisJNPwZv3CD30SfE0qF0mccTc10XxB+KwtS0kMb2PzgYxQAjFYKtcDTm7iKpczYwFLcyJdVJTA0yRkh85yfYzczZGM75KXpBMyYH6WBeb4P9rcCkoDQtQDlwN1AF7gS9prd1D2vwzMFdrXWQEwm/QWt94ruNezEqjtFRy40+1ecgucPGZGU4SE6UMg6qUyXQpkzwU7yzB4YBEbwGV4Z2kT2yn15vMdFsBv/7LTqKjYOW4Qlqa4Wi/i8naKRYdwnE3Z2yl7lg2Df4KrJ1TyTy2kXu+aaeq1cNTvq9RE9qL7d3/j/i999HeDn0zN8Plm0TAH7gVfKli0S4ulkwtW49Yxx1TJBVxBMud0w7ILEP5k0iMjaFjz6rBj/ccKZTfN94gs8D9KZgOfpXQuDIsqbXERUfTu/cf0VHtpDnK8LW939OInryXztZYouJ7iam/ltOJL2ELZJIZncOchAIqutz0HnbSWG4nc1Ep1suLCbxdROP+fOxzPCQvdBE47mTtWnixtoRTje3EZdbijTpIe8UMfKZWQgFEqMc1k9A7D/+RVejpO/CnHBSrGYTvjuqQtExrt3gFYSVpsq150GWHzP1EBSYSqFpOuC0XU2wb4dl/FOUBErdJqBfl6IuHuHaxdN+9WeIW8fVi6Q6k3DY5RIj6Y2HckTOpopy6EmLbz+T/44+FzHdBh2V2/lFDAi5/WBI0QiZRQmET5hM3YX1uC/2OzZjmbsVyYg16/zoWLxZdfOutsKtnM0etjxGvMqk7kUF8ZhOZleu5Z13+GS/zs5+Fl14Ca7qHvsmGpHUXiue2qBj2FmH2ZcFUF/GNkimVnCwU7PEGD6GZJQSzXqPR9g7pp4pQ7VNZODOduEXbmG5bwa/ffAlzXwaTUnNZlFpAZ+pOdu2CYFkhZgs0pZcwZQqsmlJ4lnE0gEuxIOCFwkWvNACUUp8BfoKk3G7WWv9QKbUR2Ke13q6UigZ+BywAvMAarXXluY55MSqNgQc3PR0efXSwjs3keWL1F17l4IWjO6lorcGU4SYxoxlCMQSac/FlvAG2Pmz9maim+fSm7QJ/PNH7v01iIvRP30r43TX0vrUOrSF9iofp17k4/KqDzunFqLQKKLuFKR3r6J2xmaY530FHtcOJz2F6+kmio2Giw0NrZgktzUaHp70suelpFcREWdG2TuKPGZ7GQZmbQnYpXL6JYId4Gpa6Arpj3SzISydz5Taani/CX5nPzTfD3r3QY/GwJ/QIcVNkbkH9jjtwTLQz/xMeOuwl7HizmrreKlKYTMibi7UzD2Zuo+3kbPxTnwTvJJFmh9diTmohFNKwsFgszsx9IhT9cVKSY6DYos+gICbuldIsfSlSiNDUC9bwYDbW8Eq8Z9YrI4XZWD+0JtbwKrcD24du6xkPb98lfH3Om3Isa5/hQRkIW2S5LxXe+rZ4SHOfEJ5dI9RMzQopb1J5tXyHOqYVkj2oY/9IjG8q/vhKQtO3YfVcTSixglBcLdi6UKEYbEdvpV+1E5V7EFtgPFEZNRCIYaXpHiaE8tn/ngdvsou5sU56GuwsWCBZdHb72fM26LSfFY/buVMyutz94rWmpsBL3kfospST1boWX1w5/aY2LIEUJvgL6Bu3E59qp7dPEwwq/H5NyNwFaW5Irgezj5jTV9KX4MZUuwzL8bX4V64Xqqs3Re5rbwIkNch4ddjlb7wbrP38Q+q3+O+b74wohr8Cl4TS+FvgYlQamzfD/b/0cDKqRCiH/mSxvh0lsOQXgx+oMYUk31+biWpZxiRWUpvxK8yWEHmWTzLemoer7kkSeuZzfdQmJmaLl9JVJq4ywPLlMqnt3nslhjHpk+LFFH1JPI3dgUeo7y+n9dW12FJauGGek5tugu9ulsmKqalgyXNh9jr4zFfc3P0FJwffsHPPgx7S810sm+Kg+IWd9PfDp+2F7H4HgksfoCnehQrEY7YFCPpNxHbN56r4Imp8bhYmO/HYXLwb2kqabylVfWX4Q31Quxzia2H+bwaFbxjhknUYLAZHPVRohw36QptlrIZuH47h36IYqaT5WDBS+9G+cxE2DSYOVFwnPPmiR8U7aZoj7aI7IfGUZIadnoM5lEJ0x3yS37sDmw1qp91DIPdFrH12Lou7lq998npeOugms0eEd2WLTHzLDQrFuPPQ4CTMgnl2drzhYXdXCf20oX0p9J8soD1xJ7GpbfQEu7CmNtL+/HqydT6HD0Mg1gNTXFgD6QTmPirXtPd2iG/B1OogPHWHBIjN/fJn64aqTwndNGM7dGeKIrO/JYPQaZcxiG2RYHHdEsg6YMQdjGy4gWrLtl651/5EyRBL8ojyb7pMzmPrFgVq6xIFazaOYQqLEdCRA9EdjK+9nR9ff+dFXejvYkdEaVxE8Hhg9k2b6Z5rlAkwgqpEe6HgPlEYIMFUaw8WfyZfS3mamz6XxU5vCe0dkFxbSN6ierZ5illtL6LlUP6ZGc6bnnWR2TNIdXW952B/rZvGww7iFmynpvckHc+vJysxC2+yi9njHHiyN+GN24eqW0y4P5bw9GckQ6f6KindPek1IDQoAL3TweaTQGHO2zI71v1PEuSc+uJgADNkEv65KxNMGvrTiD5yB7o3nf7F94oQSDsp7XvTpDjg8Mq5HyTYhz6y2misOFs5vG8fJYFqUxh0EOKMcg1Bi0y+i+oaLMQYtEmAnSgJVLfMhNSTosyUFku3Ph/Sj0O8BzCLIGxYRLQtmvEZ0P/echZxBz4f4CjBboeW1wuZN9lOfj48vl08wiLnkBIZwxIZ6rs9bHpJ0oaTPEI1JS+S8hsHGw5y76sPMKnubtTJVWcSEwZKuCQnQ6n5IUKLN0FUp1xHdI8xVgo6JsJLDxtVcfdAcrWUo0mqkXHoyIKebEmBTTspnhxhI66E0GOl/wyT3hAqKva0eFH9iRJ7CFkkVRUkJhXXIpUJ4ptEUQRipbpwjFe8rIaFohgmHJDUblsPNM/GVFaEdepOYlPbaWvTch+jO7CMqyLLNI/eslVMWuLm2mnOEempCMaOSyHl9n8N7HbYcJOTjc94Mce1M21iMkcbnAQDoBPasU7aTWpiDKmnV0PuLu67sQjq8rnlBggE7sTng7ouD1yzCZVdxm+OZKHDO+ClcqkYO3Ev5qMQTqpEL/iVzItI8Eo9hrijEBeG66qo78mAzEMcCJtFaJs7IPtVsQCjuoRymrAfrMMq4BKQ0uH+RPnEprVPLM5Jr8lvkKyf1jyZFxDTIX/WfsyhFBZe4eV414v0px6VGIg2JLw2QcN8+S7EUE9DKUPwa6GNgibJjAlYIZAsKT6nLxNLtmEBBBLlo0Hx9TLh0PMJ8MejMo+iTT4IRRPXtowZXUVclmunud9D+soSTr4H3bsLycu08/xbHqY6XdS8JrWdZuZ7qMh8EF+Wi+ikGHyv/prESeXYEtoIhhR9uoM+awUBbZMgd9sM6E/GF1VHTYMFGuGVw6C67Kyy3Mm9t4PLMljRtLHcTm+vk1tKXcTUOVl9tZ0tW6TUttUqKdc9eS7+3LYFazPElqVKVVavzAp99tRmDjSXUtbyALY/S32Q666Tmc0A1dUQykHGyBKU9E+TFiUwMEP9igekZLspMEShKlGAth7oMOZM1C8Bc588F7Z+MHeL0hx30ti+WDyNjCNM6Sukp2Y+3ZUOesftxGTfQyjrHbD5UW2p6E6bTADtyRYDZeIeOLJGPuQ0dyuBvf+XKTjpm+Diiwuc5BTYgfxRs74EF9+s6b9nRDyNC4hz1QAq+FoJ1Y1GiW53oeTmX3G/WOqnZ0LObhG0x6+HyuskS+Wa9TDzT0DQcN+T5EX3x0pZcGuP5PmbkDIKneMhe5/QY5Z+6EuQoOxAqmfQDKizPQ33jUK1ZL8jmUqpVVLS5O31IkiySlFLi0lsX0HMjF1ce3kW9WovveVL2H3yJOGU45B+AgiDLwnlTyW6Yz59jRkw3UVUahO21oUUTF5Ge7vCW59MakMhRw5DVoEL/3En1e/aURNLMRdsIiMuk+ZWTZ9qgtolxC7+E/bquwm6V6ETPVSmPCIlO46shZRy+SaFkeppsYLyJ6NqCvBnGF/W0RqW/kIyljIPSQxEI55T2CIl5kOGZ2Ht44xbpBX0joP+BEiolTz+nd/Duu9O7rpLUl8HJso5nbBhAzzfsJnWCVsxHVtDTss6Wlpg+nSYPBkaG+ETn/VQbhv0NMrKoDzk4varneR94mxPIy9PUmCvuUbm6GgN33/YQ9OsDQQnvkZc5U30xJwknTwCp5YSytuGOr6a1GnlrLwKqC6gtHEn/pgaWkIVLMpegCq/Hl+imxTvYOHG9rCH8pCLP79dSe+kJ8XzbJovY2x/m6imT5F+6F6ayiUTK2euh4qp6zFNfkM+c9KVBN3jsVpMWCtX0duaiqpykp4G3Zku+g47yYixc+ONsHu3TOIbqJkVwd8WEXrqEsbmg5v5wUub8LS0CW+7e70oBUcJZiuEc19H218RLvmpEmLbJW++wldK++xNaA3m9CqWxt7IoYajZLatpjlcDjFtLL68ixZfI4v619PTmMXbnSXMy28nzpxMhjmPP7gfZ97UDNKtufIhm4HqpMsHv9E8WqrhuVKAK11OnvwfO+Fp26nPu5dw6yT6g31C140zysJbuyRW0ThP8vKjujAd/GcsxwrxX7leZtqGbPDCzyROMP1Fo5RJlHhO/jixnD358MwTUip9+vOiaBsWyLbotrM77UuRMuoTjS/rxDQL5RaMkoyx9BNCyQRihV5JNBSCL16s+K6JMqegL13qUdUuhSvvhdhWYo5+ndtm3sndd8uhh8+LeOQJD7ubBz2NXbtk1jPIvJqiIim/PbR6a0kJ1ARLqczcRLppOie3FuH1wpxVLuoPOagw76Ajpoyo4Dh84R6Cx64lNPklokyxaGsv4V3roSsL03QXMV0OurJ2EB5XBk3zxAjIOAif+ldRiJ3ZxkejwJTchKnyWoLzisWbDJslHhWKhv4UGTdrv1B7lU5onIcpmMKXFxXS0w0ZV5dAdBtVx1IY11tAT5ybtcud3HuXnQMH5LsWOTniaa1ZI1/2Ky0Vym3Xro/+HYvg/YgojUsYng4PJe4SjrwndZF8+6UKbFqaUalVlbLx+WLUfplI1NJytlCB9+eXnyvt8HzSEoeXoB7pfCPtM9CubdJmHn17Ky01yVLsLxwarCM1kC0UipJZtP5Y2Pk9SQG+4n5IrhIPqGa5JBNMe0FmVNculyq+/6+9Ow+PsrwaP/492RMSshC2QFgFJWEJggQLFKwMlqqorW20Yq1olS6v7a8o0rq8Lbbva23xbW25GmiF2tpKqJSWWlwGRcWNPSxhCYZtICRkX8g6yf37455ACAmMZJmEnM91ceWZmWdm7icTnjPPvZyTba80InYtJLcsH8b9ya5EdoedvdKQsCKCgoTqaggOgdiwKMILp5EbsgljIDLScHrIUuqzbiD68Hyip71Mfs/36VX6edwHHfSd9TIjYkZyaOMkyuLXcu+o+ex7ywbtf/0LFi6E8dNtoGwcYHdmuXhmbRompIhd6UJdPQiGwHqb1LImMgNzOpGgEZuoDT2GxBwhoHwYph5qg3Jg11xbQnbE69AjG4Iq7SDzoVvsDLLENbYsbf/tnvGZehuASwZ6Ftd5xqfyrrGDx7GZdlruoA/s2piqaDj0JTuld0Cj/z/uYLtSWursosee2Z70G8YGCYNdgV060D6vzs+unTH+hPpHs/imBTx6Y8uj0w3ZbgEeeoizf8s7d8Jzz9nfp15pdAwNGsprTXMMebsvnKsZsmBB88998YN1/HDNc5zZdD/1IUUEDcygOvpj288eWAHU2/7wALedeXRiMuRMwD82i7Cy8QzJf5iInlDQL42jZ/ZC3wxGFzzFTVP68uL+JVAwgqis+Tz3hJ0ueja76ysuNh5z8tQ9NnXJBSugL5GxtSUul+1aalqPouH3MmmSreNQGWcrELr2xvPFr7lYlfs0eWHv2frhfrV20kN90LkrnRhPRtfw0/abfGCFDZrGz44bnBoPffbanEYNAbYu2AaAyki7qrngKjszK/wEBLrtlOFd99nxifCTdtV1XZDtnpNa2P0NO3Ddz1OzPKQMTo2DqzbYge2ACqgLhezxUN2LgKM3Ya56k7rifhBciv+QD6irCcD/3efox3iufzCNHfuKOXoUhgw13H17NA9PSWkxzXrD1VZ6mZPeZQ4yM+FYjzQqKSaqbynVgTmMLFjA+/+JI3pqGnfdBQu/1CZh7H8AACAASURBVPLrqdbRgXB1VuOCUQ05rXZm2XxPt30ukfeOZHAmOpG/7svg8HMXn4XSuKYxnCtx2VKJyv99/zlKIz6BmbugMpbqkCLPbDGxJ7sAN+SPsrdDyuDYDUQxjDd+/Nh5yRtdrkcvuBr6OavPHltDwGgIaIsXxON0zsPhGSNtnKvLVeLi6Y1P8+H+LAIzbMbWhgHqlgoNNXA6bcAYPvz8Y27YfuMNeOu4k/rgVYQVQHj5PD4+7aQyNMuWoQ07bbvSSgdA5i22a+fYNLuQss8+OzEgtMCOp+SOQwoSMWFNrjT83PhXxMGpCfhXDKY2ZySBY9bSr+gOKiWfwJ4F5Az5DfUFgwneO5+gvGT8olycGbWM4MHpVLkrCeztYkjcIEaXPsrBvVAzdB2ZPZ4jpCwJ/93jmT4qEXP1v3FmvUFt7EHk/Z/in/EAg0seYOBACLlmHVsjdlJTFk7ZUCd5FZkc+XcKf/vf+PPSy7TE5YJvP+7iTRbg7vMxlP8e6ifD0FUQVEKh1IMJ4GTZEZjVl/yBn/D73f707Ak/v/kyKpmpNqNBoxtoOJlGRUF6OqxZAxlBTk5ErWLnawOoDT2JHwMwxSfZvx+GxbZcJaxxTWOH49KVCH/0+YV8a/1cTFC57UI6nWinrwa44fAXbE2MA7fBNf+00zqjD1Lcby3PrC3ktVHnXrxpOvnm7k9MtOVEExNb3h9siu+soiySBg1nUi/H2eNp2B9afm7joNk4oDQEpcREqPiNg8pekDjAwaBbYeREB2t3Q1xAIis+XIeJOURA+gI4ac+uIaFwbe84CiKdUOAgPgbyQ5wkjXZw6/R4Nm2C4quhtHQOOTnnp01ftgw27nCRWZWJqyITKqMxVy+DwALon0/1F75L9Zl+tiuqLAq3yYPASupO9+YAazkQvhYmVdtuqaBqKvtuhroQ/lPZC05UQcxp8DeYL36b6iFvkBlWSKY7FKo+hYiDEAGM20lN6UC2fxhDauq8C4JGc19afrnMxev1C6gf9I6datsz245p+VXbKysjdhZg9BGIOQp+NdTVB7Fm+7tER0NKol5x+IoGjW6g4USXftjF6TwnhQcchIY4iBkI5CVSFZHBmH6JRPfPIGmko8WrhoZB7cKtDt561f6HdXzl4tXeHpg6hyfeHkpu/W6boC+sGGqi4NMptnRtz3V2tlLUccDAsHfsN/IWjqFp2xrf73TagdSGE1Nz+8P5dSMa2tzc/s09t6UCRg2Sk+G1v8Vzfl3zeObMsLd//p2WvoY3fc657Za+uaelwa/+4KJ68tMwbAMkFNiurwD3uSnM/XcAYuuM7PgWhOXawXw4t7YGzu0f6IaAcjsRAeyMsbp6m34kYY1nPz+bDLIm1NPNFgynkogtsXW5XZ7c1ctecbExL43M40XUlkUT8osUTh+KJ3DIZjuxof9xT6bbGDuuUh4HB++EEetswsjjn4PYQ3aad10oiCFnWCYrdx4hJjSmxfriqn1p0OgGGk50T7zqJLhqFaMnwOx+85g2bR6bNsG5efAX71doqOw2azjcdZenS+cS1d4Afn7jM/xwzXOUbrzfzmACZH8Kph6bNiPqiJ2BVBcEFX0ZQDJP3XF+ra2WTtaN7296FdDi1VJk/AVtbbp/V1lZ7B7stOMh9f52SjTmXFoUd6Cd3SSeEsB9d56bPWb8gDo7jlTW3waD4DI7tlEVYRMkhhTD4dlwbKqdERZYacdJgsvt6x6bQVBNX2pDcpGtC+gh9qpo+HDbbfm7152Ujl6Jic8H409JMZD5KDVjUu0kiPyr8M9MoS5vJFy9lpjD8yk/mEzN0HchuByJPE30/kVUUkjVgLcgaxaDIm7gG7PPBX7V8TRodCPRhQ5ic2D25x08+pC9z5v+5wbnfUO/0XNfz/OrvTXngalzmDV4Dst62Ln3AKFT4PBhqDiymBk3JzJwIJTunkZOVAYLbve+GFVjl7oK6Cgtrr9pdH929rlptY0/g89SpzslBfa6HPxrD1TkxVL3ueeoCzsK6fchUdn4Z96Bf99M6ushMhLyBy+3s56qojyLO4uhPpDg3T+g96FHCZ+2gk97L8FdXwtVseDnb9Odn06wxZ8isu36ntBCxB8CwiswMW8SFhBIbE0GUUeSz471pKVBdJGD2JJCckM3cib0EAFBIEEQeGA+Ff7Q5/h8FqQk8/LLcPKDOdx9N0TOgY0nF5Ib8hyDZSHff2QOWw64+DgvhtBaB0/d792YiWo/OnuqG+lMGT8vpy0tnVAbv1bjqZp9+56/3uHpp+H1j1wM/LyTMT0cnMmOv6AuSNP3oTT+M09HLiyEt96yC+2K611sPO6kfJeDzyfZtRhFRVAV7CK3h5Po2kRG3b6OHccPMaJiLkdC1lIeuRmkBnn7eYaGjOdMXycF+Qb32JXI0ekweg2m3o2cnIqp7Amxe+0kgqjDtqpi/mhbHW7vXKbMzOeVnznIzoZn1qZRaYoJIYqP34nGPT6V3lXX8+MZjyGl8bz4qouPqpbB1a/aAlEBZ+wVS62n8qK47TRed7Cti1EdSfiZJL5/5yRuHZpy3vqd8wKkJ9VN0zT9rflbUG1Pp9yqdnWx/+if5dvypaxbZ+fxjx4NeQNXsLVyFWW7ZuFXHUOQy8HM6+KpCHCxT9KYeB0cXJPC7j3Q78Y03G7Ify+FAQNsEsYT7zuouzoNxq/Ef8/9BBxMYeLXnNx/cyIr/5PBbTNjeTd3La59cfgP2krdrrvY/7d5+Ee7qBviJC4gker+mygqAjk+jVvnZXDv5xwsXw7ptWmcLD9uS3TWY6exVsRA7H5PfRA3FA2Gin524LdXpk3FUh9ou4oCq226FAI8if2wCxfdQZ5pt3V21X5t0Lk0L43/6zbOu2XEdh9lX0tIYBhLv32XV/3/t9wC/8leAV/4kU353qDGU7MkzFMcyh1i07Ifu4Gr61I4sFnP9FcCDRqqRY1ns2zaZL8NNyTCu9RJvmF9w7svOsjcFs9DD9lukmWvuEh3pxEaBu9uhLK4dQyM6kfK1EnMn3p5M11cLrjhBtuN5e8PkYNcFEU7qQ8qtOnb995FwJ55xNy4guoJS+jTG+JPLOCjj8Bv6hIqKrD1QQZssVXXqnvYfvkep6HwKpvIz6/OU8c8HDD2pFgTBp/OtmMtvTJt3iXxg+KBEFpuT+YB1fYb9+lrPXUs0m3uq6AztvEXS7bYXMbcptlym0u5Xh9oc0RFZNvbfub8/Q32GGt6QNkggj55iru/lc8z93kXvDdvhh//wsV2llEasxGJyCXY3ZegkzfQ6+jDFNRkUzNhCSZ/JHGnHqaHO56f/UwX310pdJ2GOqtptb7UVNi3zybGO3kSCgY5CT1tB7MvtnoXIG2bk+edq6jJg6Byu6/TCSvec1Jw9Uq7grn0doipJ6tqGyvTMxged3kzXZxOCA+3tUeCgmDCqHhCQ+fx9jYXNQExBBU5mHmHndJqEguJjoZpNznYNBKORRSyYwek94CKuK3Q4xRENJpV1GfvuXKggiePlCdBYvAZT/qRivNP3jGHoSbadtOEFdqTuP9Wm/E1Jwli/aBPpn2dhhO9waYgCaiwiQIDKi+s3XEm2l5JSJ29InEH2asRxH6rL7zK5rU6MdlO0x211o4zTEy1ucRKRkLEMQJcDkIz5hNzTQZzEh08tj6e7GxY8C3oO8JFz8+ltThd1eWCf/8bQmviuX34w+woHkbZRge33pVN2Y2pzJ+YTfLAZGD1Z/4c1ZVFg0Y34DzsZPmWP1F+fBNbtizm2L54CgrsCTk2FspOOCAUGOmwdRQvJstB+BEY2dvBjB9ytjzm4XwH6e5Ce6VxBMpC/BgYMpGUpEmXPdOlYTZU4/n9tivswimtcG5NR/Io7O274cYvu3gnewsMrLOroIMqbZpwqbdXGX4GAkvtiT+ozHYRSb3tx68Jsyf5wDO2u0iAwuEE7p6PXJeKyU0koHIAfrXRBOxPIbBXNvnXfQ8ijtnnBVXb2UliIG+sXVmd9Odz6cWrwqHwGoI+foqavXOIGGC7wSKqEhn+hU2MuAoqtqSw4FvnD/66XHM8aVkeOy99y+b9NlVJTnEx64+n8dF/TeNIeQaFWx34nUrD1DxPEOFsfD2Gh6+bx/LlkFtpa3GYLAe5mfFUV4NJcsKYVZiIQlYV/ZPaA7aYpg0aqrvT7qluwFXiYsFrT5N+PIuvjvgm2f+Zx759kJBgB4k3eRK8Xix/1NnX8mLQsi3HNFrriSdg6V9cVA1wElCYSL+xGYSVJ5InGcQFJnKoZhNx/aHs6EhORa6lT8Ed1EZmUtxjCxKRw/TIb3JsWyKnr1lMcLDw7OyneOCLzZ88XS5Y8FMX/yp7mpp+79qV3f7V9iqiohf+2dOp67fJ3i+G2Jy53B22kkGDvPvdX8r9L6zgb0eXUONXBNXhtnIgfpD+TdsFN2E55CQRuHEJowbEs38/1I5eAaNXwd676JE5j8BAKDa2KBMhhYRN+jvjxwSy5KYlbRI0LjWzrOELwtmuU0+qd+MpAxIV1Ta/K3UhHdNQ57ncGUFdXXOrkb0JjGmvu2C4d+M8TZ/7XKqL111pVFFMQI9SYkccph9J3JFwKx9kZnDHlETy/TLaPKg2vtI4Xp5JVcgxAksSqFy/mEGD4Ki/k+oMB37l8cyYAaGhsPe4i9xwJ3FVDu7+UjyTJtmSxDk5UFRvKz/+zwMO4sJtudfiYnvinjYN/v2eHccaN44Lxq02b4Ylf7CFpobh4IVn4gmKtVc1ObsToW8GvUJiKexrA3WxfyZFlcWYOmPLv8buhaxZMPI1OBOLvPc/mDqQSan0cc1n+dPJOpbSxjRoKNWNNXxJaMi0m5gIf/0r/OMfkB+ymagbU4kqm0pu0AdUZU6ldvCbUN6PujM9CTJR9K+ZRsg1mzh0COTYNPz6ZVB7wp7sqYiFq9far/8J/0AI4obARfzpIVuje/N+F19Z6OTkmcOQuNoW58pJspMKwnPsmo+YLOh53N4u72eTNwZ5VqEHF9lSuDVhdiGh8QfXZLtifNjbcGAOUwpWasr0NqYD4Up1Y41XvSePgiefc/GHbU6qyhxw/RJye/2b3J7rbNdZ5EabA6w2GGrDqamK5tjpLRD1AYwPgeH/pA4/iO9ng0Z1GPQ6bFeOB1Ri3LB1KziH2sWVqU4nub1XQUiUDQRxW206k5pwOD4Fts1HklMxUUcAAb86/E8nYfJHUl9nCOuXTUXEXoJds6ge/JqdghxQCwUj8Tc9iD05n4VP+/b3251p0FDdQtMup+wD8SxZYivlzZ/vm266jlzUllnnpGrEKqjCfvMfFgwmGHKH2aqMI9bD6dFw4nqCTBQB4cVU16dTVxVpEwjmJdhv+n0z7CyuvNFwbCp+o96kt4zgaxNTzk5cmO9wkJcH+99P5NjpSfiFFdvZzP5RRLpSGH5NPF//ehxrMxPJLSmmujKK738lBSm9sLiXy/Vzlr1iqwXOvcdB/uHmC4CpjqPdUx3E14PDjQshNTeQ2BlX5Ta0KTYWXl7nomBIKtuPH+Ja/7lIeD4n33dwJC+b+slLGBc/gto9cxg5NYMjGxx8mgXupGXU90nHXRyLO9yFCc0jOMiPutMjqTs+CdmXQmxgPP/7v75JP/JZ6phcjsZ/c9nZ8MOlTvb800FdPcz4bhq9e8Om1BSqqyFinJNfPORgzgz74W/e7yLV6WTqyET+9nYG+Z84eOT7cBgnu/7hIGlYPA8/7N3fSmf821IX0u6pTsabxH7t+v5OWLkSamvtgPDixUDPcycVpzO+xXTgjW3ebFNz3HGHrUm98dBmTg5aginvC2E55AZu5Vr3dxERCgpgVlwKjz3sfZGjxtLSYOlSOHUKqhOcELMc+hayMe8g5EfC8H/A5L0QnsP2uhDocYjdB4qBw/CFdyH2gE2v0cfPptwWP6pLBkKvdIjOwFTEMLH/vBaz+ra3i2Xiba2GmiFZRVmA/Zv78Hfz4HcNe3imKD/UEFxg/LBzz08eFU/yKPuHULQ3meW58OZqWLJkHvF3fra2dJacYKptaNDoII2T/fnk/R02J9KWLbaIkNMJjHfyp22r2LQJ5ifP4667Ln0CS02Fv21fx59CnrR92rG5EHrYrhT0rwO/Oj6Wn0BtOET04NODGfR8ZTE/W3h5XzGLiqC6Glsj/bpfQ58SpCoWUzTIdqmElHpWQUfabpfiYTD8DduNUjwITsbb+t39P4HwfPwO34pf2WAC/OGW8Q6e/4nvvv22x8n0bP6rwbZmyPDo4SSGOFixgmbzQsXHn/tCU1gIMcfmNXtFUF5ua7E4nRoAujsNGh2kuXTcHfr+8bZg0nk1vns62LQJst5ykBHu3clg/nz4y4vP2RXVYNNvC4A/nBoN0UdtDQYBKnpBVBaZdU7OX4znnZQUO8VzzRr49NN4Ajf+gbg5qaRMms9rr8QR5D+AoNhMyj6cS05pPjFnHAS545k+IZF/n+rBdaHzSb4q+dz00DInSdMuXpmwq2soJjXrTgffvM5+SXGuOXcV6XCcK1cL9jNv+CJTuNXBqlfP3d8gpVGWel9dlanOQ8c0OgFf9vlezns/8ed1/GLbk9SVR0DhSPwjTuOfPx62PUxsbwgcl0ZAIIzvNQ3pc/mpztVn19zn2fg+pxP+9Cdb82Lx4pZTt1+pQVW1TNdpdCHtPSDaXs67akFPOF2BBgbVEg0aXYj+R1ZK+Zq3QcPvUjuo9tcwIKoBQ7UFl8tevTbU6laqLfkkaIhIjIg4ReSQ52d0M/skicjHIpIhIrtFJKW511Kqq2s4yW/efO5k35oTf8NguNPZ9m1VylezpxYBbxtjnhWRRZ7bjzfZpwL4hjHmkIjEAdtF5E1jTHFHN7Y70C4y32k4yQ8YYOubNPBm3Uxz2nP9h1K+Chq3ATM82y8B79IkaBhjMhttZ4vIaaA3oEGjHTScuKBrDcZfCZqrG9L0sc9CF9Op9uSroNHXGHPKs50D9L3YziIyCQgCstq7Yd2Vfjv1ncYn+cbFlvTErzqjdgsaIrIB6NfMQ080vmGMMSLS4hQuEekP/AW4zxhT38I+DwEPAQwaNOiy29yd6bdT1ZZayrXm6xxsqvXaLWgYY2a29JiI5IpIf2PMKU9QON3Cfj2B/wBPGGM+uch7LQeWg51y27qWK9U1dNZxKJcLnl7rJCv0wlxrvs7BplrPV1Nu1wH3ebbvA/7VdAcRCQLWAn82xrzagW1TqkvorLOknE6bmmZ45V3n5VpzuWyqkllxd/ksB5tqPZ8s7hORXsBqYBBwDPiaMaZQRCYC840xD4rIXGAlkNHoqd80xqRf7LW74uI+pS5HZ7vSWPeui2f+5iSiKpHMogyS4hIpCszg2EYHpSVQP8xJkDuW0iEvExPQlzGDB/M/X0/RFDOdhK4IV6ob8mUgGTv/V+wJWWrLwFZHgamH6ONQHQ5ZM2Ho+xBUAkFnoC4Eygfw1QELWP2j9ummcpW4SMtIAwMpo1N0DOUStJ6GUl2Iq8RF2t409mYVs2OHYXpyNAu/ZNezpm1zUrzdQZRfPNFDXKRuSmPyZFj4JXsidLnguVQXG4qXUdkzndJD4/jchvn8/hcdl83X5YKaGiCqAAIq7J3BZyC4xP4b+i6EFENgFZT1g4O34lcxmJGDWt9NtXkz/HjpZvKGpPL9z81HspOJjYUn1zj5tO9KQoJhy/sxLJk7r1NckXV1GjSU8hGXC5a94mJnmZPi6kJ2y0oqKKI+AI7uimb8NTEUFsLzzlXUpUPk4XkUDHJSeM1Kdm2BTzbG8LsH5pGRAX/5yEnJtSsgIB+G7OCTXcNxOud12Iw4pxMiXSkEmY3U9NoGmbdCdU8Y/C6ElBOU/gg1oUegdyaDTywgtDCZmTPh4bsv7/0273fxzNo0jh+Hg2tSqJmxBOpe4+E3PsGvaBi1J8bDiUkwJpFKdxivRb7ByNcS+dm3ky/94uqiNGgo5SNOJ/zhHScF/Vfhf3QWbvf9+IUVE+BnuG5iNI5hDtK2QvgRGBnjIKwnfLDHgX9NIeIPe/c7SK21Kc7vzXKwofgwlUHplB4dx+Tejg5dc2PfKx7TM5WV7zlZ+LiDvqHxpKbC/G9CXFzbdpst+aeT9XnLMRGF8OW/QF0gUEddjyzqIrKg9zYY+iaElkBVJNW9D5PZswegQaO1dExDKR9xueDbj7v4JM/J1YEOov3iGToUBg8+V8e9afr5tDRbmKqkBHJzYcGC8xcEdrbB8fbyyJMuln66gPqhb0BAJdSG2sJf9QFQPBBKB0GffRBQBa4phAzdyWLHj3hs5gO+bnqnpQPhSnUB3eUk39Z+9St49vcuCoakQp9d9KgbSmVRT+rPRENGig0gCWl254Fb8Bv+NtdHz+GDhSt92u7OTAfCleoCdCX+5bElaOM5fvzn5OTA3Ll2QPyvf4WcaggKgrjCR8nPhyF9NiM9erDwxvm+bvYVQa80lFJXjM2bYckSGDEC5sw5lwBSr+IuTa80lFLdTnIyrF59/m3VtrRyn1JKKa/plYZSnVTDIHnTOhsN923adP5MqhEjbJ9+4udc9BjjZMHtDk3RodqcBg2lfKBhYd9HZWlkFxRz4iT0i4yiansKxcfiqQ524U5aBrGZBCxeQEBVHEF/T6Pc/xj1PY/ABgN9d0HeSAgvgNLPw+8WQmk8x+LS8Ou1koq1hbw26lFfH6q6wmjQUMoHlr3i4pfbnqZmwNsQlQfhIWTVB8HYjRB5AwQXwXW/hcAq3PXgzvoiVcNWQvhJCKoEasHfQE9PfdioY3B6POy0U7Hqm608o1TradBQygcy65y4I7Og3s+OLNb2AP9aGLANYo5CbiLUBdmdC0bid9RBeN9CqkKOYSKPYIzBHbMLCkZCWAHi+jzmsIPYWOhZlcKQ0BieukPTj6u2p0FDKR9YcLuDir/B8QOxFPZZy/VRd/DO7kwmzyhiWFw0+16fRkn1JIqLoW9ICjd8J56HH360xamj5y8SjAd08YdqH7pOQymllNfrNHTKrVJKKa9p0FBKKeU1r4KGiEwRkR6e7bki8ryIDG7fpimllOpsvL3S+D1QISLjgAVAFvDndmuVUkqpTsnboOE2dsT8NuB3xpilQET7NUsppVRn5O2U2zIR+REwF/i8iPgBge3XLKWUUp2Rt1caKUA18IAxJgcYCPyy3VqllFKqU/LqSsMTKJ5vdPs4OqahlFLdzkWDhoiUAc2t/hPAGGN6tkurlFJKdUoXDRrGGB3sVkopddZnyj0lIn2AkIbbnm4qpZRS3YS3i/vmiMgh4AjwHnAUeL0d26WUUqoT8nb21DPAZCDTGDMUuBH4pN1apZRSqlPyNmjUGmMKAD8R8TPGbAQumQ1RKaXUlcXbMY1iEQkH3gf+KiKngTPt1yyllFKdkbdB4zagCvh/wD1AJLD4ct9URGKANGAIdnzka8aYohb27QnsA/5pjPne5b6nUpfSuJARQNrrLhjuZFqcg03/iae43kXUBCcpEx1QGk9amt1v2s0uNmU7IcvBtHHxbNoExcX2sagoSEmhxeJJSnU13i7ua3xV8VIbvO8i4G1jzLMisshz+/EW9n0Ge4WjVLtyOmHVKjic7+Jdl5NjpwupO/wWWyIhfeU8Tg9wEnNmFRkZMKDKwYr3nITnOsgIcrKlYhXshS0b5vHBB1BXB/7+EB0NMTEwTwvpqSuEV0GjySK/IGzeqTOtWNx3GzDDs/0S8C7NBA0RmQD0Bd5Ax1BUO2u4wngj10mGrCLSfxahR+5i5AQHJMHWTAe9TkHWdgdnpjgJn7yKpACY73CQmA3EOCg+DunpMHIkJCXZKw2HlupWV5DPXO5VRAR70p9sjFl0WW8qUmyMiWr0ekUNtxvt4we8g02SOBOY2FL3lIg8BDwEMGjQoAnHjh27nGYpBcDm/S5SnU7uGOsg/3D82ZO+0wmJiZCRAYnXu8iocuIY5iA+8lzf0/m1un10AEpdBm/LvV52jXAR2WmMGX+RxzcA/Zp56AngpcZBQkSKjDHRTZ7/PSDMGPOciHyTiwSNxrRGuFJKfXbeBg1vu6e+3OimH7arqOpizzHGzLzI6+WKSH9jzCkR6Q+cbma364FpIvIdIBwIEpHyy726UUop1Xrezp66tdG2Gzvj6bZWvO864D7gWc/PfzXdwRhzT8N2oysNDRhKKeVD3s6eur+N3/dZYLWIPAAcA74GICITgfnGmAfb+P2UUkq1gYuOaYjIb2k+NToAxphH2qNRraFjGkop9dm11ZhGw9l3CpCAXZAH8FXsgjulrhguF6Sl2YV5UVEwcqKLtbudzHc4SB51birU5s2QmgpTp8KKNS4O42RClIOiYjgWnkZ5OYScGUlI0lpCjt5BjjsTgotJvCqK5+9POe+1lOpqvJo9JSKfAFONMW7P7UBgkzFmcju37zPTKw11uR55djNL9z1DfXAuBJXhX9OLuojDYPzhsANK4wiWaKoPToO+GQRUx+Ke+iT0zIbt88EYSF4KlTFwphf02QenEyC0BMJOQ0Ufvhq3iNU/0pV+qvNp09lTQDTQEyj03A733KfUFePdilTqB70F/rUA1NX7AX7gVweJq6A6muqqaIjIgMiTuP0roM8BkHrolWk7cv1rbMA4MRkiT9qfEbkQtw3/giRGxutKP9W1eRs0ngV2ishGbKnXzwM/aa9GKeULM8Lmk3kwj+rIvRB+CmpD4PRoCC6DUxOgNA6qo+HYNHoMz2BY31hK3cspLoaSjxaAH4QG9iBo93xCauPIyx9PSLaD2hoY5nBy53gHD9+tXVOqa/N6cZ+I9AOSPTc3G2Ny2q1VraDdU+pyNazmjh3m4jdvp3HsGAwuT+H734wnPx9iY2HtWpg/H5KTL/16SnUlbbIiXESuMcYcEJFrm3vcGLOjFW1sFxo0lFLqs2urMY0fYnM6LWnmMQN84TLappRSqou6aNAwxjzk+XlDxzRHKaVU+z0NOgAAExVJREFUZ+ZVuVcR+aqIRHi2nxSRf4hIi8kKlVJKXZm8rRH+lDGmTESmYtOUvwiktl+zlFJKdUbeBo06z8+bgeXGmP9gizEppZTqRrwNGidFZBmQAqwXkeDP8FyllFJXCG9P/F8D3gRuMsYUAzHAY+3WKqWUUp2SV0HDGFOBLZQ01XOXGzjUXo1SSinVOXk7e+q/gceBH3nuCgRebq9GKaWU6py87Z66A5gDnAEwxmQDEe3VKKWUUp2Tt0Gjxth8IwZARHq0X5OUUkp1Vt4GjdWe2VNRIvItYAPwx/ZrllJKqc7I2xrhvxIRB1AKXA08bYxxtmvLlFJKdTre1tPAEyScACLiJyL3GGP+2m4tU0op1elctHtKRHqKyI9E5HciMkus7wGHsWs3lFJKdSOXutL4C1AEfAw8CPwYW7nvdmNMeju3TSmlVCdzqaAxzBgzBkBE/gicAgYZY6ravWVKKaU6nUvNnqpt2DDG1AEnNGAopVT3dakrjXEiUurZFiDUc1sAY4zp2a6tU0op1alcqnKff0c1RCmlVOen6c2VUkp5TYOGUkopr2nQUEop5TWfBA0RiRERp4gc8vyMbmG/QSLylojsF5F9IjKkY1uqlFKqMV9daSwC3jbGjADe9txuzp+BXxpjRgGTsIWglFJK+YivgsZtwEue7ZeA25vuICIJQEBDYkRjTLmngqBSSikf8VXQ6GuMOeXZzgH6NrPPSKBYRP4hIjtF5Jci0uwUYBF5SES2ici2vLy89mqzUkp1e15nuf2sRGQD0K+Zh55ofMMYY0TENLNfADANGA8cB9KAbwIvNt3RGLMcWA4wceLE5l5LKaVUG2i3oGGMmdnSYyKSKyL9jTGnRKQ/zY9VnADSjTGHPc/5JzCZZoKGUkqpjuGr7ql1wH2e7fuAfzWzz1ZspcDenttfAPZ1QNuUUkq1wFdB41nAISKHgJme24jIRE823YYEiY8Cb4vIHmy+qz/4qL1KKaVox+6pizHGFAA3NnP/NmzdjobbTmBsBzZNKaXUReiKcKWUUl7ToKGUUsprGjSUUkp5TYOGUkopr2nQUEop5TUNGkoppbymQUMppZTXNGgopZTymgYNpZRSXtOgoZRSymsaNJRSSnlNg4ZSSimvadBQSinlNQ0aSimlvKZBQymllNc0aCillPKaBg2llFJe06ChlFLKaxo0lFJKeU2DhlJKKa9p0FBKKeU1DRpKKaW8pkFDKaWU1zRoKKWU8poGDaWUUl7ToKGUUsprGjSUUkp5TYOGUkopr2nQUEop5TWfBA0RiRERp4gc8vyMbmG/50QkQ0T2i8gLIiId3VallFLn+OpKYxHwtjFmBPC25/Z5RORzwBRgLDAauA6Y3pGNVEopdT5fBY3bgJc82y8BtzezjwFCgCAgGAgEcjukdUoppZoV4KP37WuMOeXZzgH6Nt3BGPOxiGwETgEC/M4Ys7+5FxORh4CHAAYNGnTB47W1tZw4cYKqqqo2ar7yVkhICAMHDiQwMNDXTVFKtYF2CxoisgHo18xDTzS+YYwxImKaef5VwChgoOcup4hMM8ZsarqvMWY5sBxg4sSJF7zWiRMniIiIYMiQIeiwSMcxxlBQUMCJEycYOnSor5ujlGoD7RY0jDEzW3pMRHJFpL8x5pSI9AdON7PbHcAnxphyz3NeB64HLggal1JVVaUBwwdEhF69epGXl+frpiil2oivxjTWAfd5tu8D/tXMPseB6SISICKB2EHwZrunvKEBwzf0967UlcVXQeNZwCEih4CZntuIyEQR+aNnn1eBLGAPsAvYZYz5ty8aq5RSyvLJQLgxpgC4sZn7twEPerbrgIc7uGntoqCggBtvtIebk5ODv78/vXv3BmDLli0EBQVd8jXuv/9+Fi1axNVXX93iPkuXLiUqKop77rmnbRrupXfeeYewsDAmT57coe+rlOp4vpo91a306tWL9PR0AH7yk58QHh7Oo48+et4+xhiMMfj5NX/xt3Llyku+z3e/+93WN/YyvPPOO8TGxmrQUKob0DQiPvTpp5+SkJDAPffcQ2JiIqdOneKhhx5i4sSJJCYmsnjx4rP7Tp06lfT0dNxuN1FRUSxatIhx48Zx/fXXc/q0nUfw5JNP8utf//rs/osWLWLSpElcffXVfPTRRwCcOXOGr3zlKyQkJHDnnXcyceLEswGtsccee4yEhATGjh3L448/DkBubi5f/vKXmThxIpMmTeKTTz4hKyuLP/7xj/zyl78kKSnp7Psopa5MeqXRApcLnE5wOCA+vv3e58CBA/z5z39m4sSJADz77LPExMTgdru54YYbuPPOO0lISDjvOSUlJUyfPp1nn32WH/7wh6xYsYJFiy5YVI8xhi1btrBu3ToWL17MG2+8wW9/+1v69evHmjVr2LVrF9dee+0Fz8vNzWX9+vVkZGQgIhQXFwPwyCOPsHDhQiZPnszRo0e55ZZb2Lt3Lw8++CCxsbH84Ac/aIffkFKqM9Gg0QKnE1atstvz5rXf+wwfPvxswAB45ZVXePHFF3G73WRnZ7Nv374LgkZoaCizZ88GYMKECWza1Pws5C9/+ctn9zl69CgAH3zwwdkrh3HjxpGYmHjB82JiYvDz8+Nb3/oWN998M7fccgsAGzZs4ODBg2f3KyoqorKy8jKPXCnVFWnQaIHDcf7P9tKjR4+z24cOHeI3v/kNW7ZsISoqirlz5za7ir3xwLm/vz9ut7vZ1w4ODr7kPs0JDAxk27ZtOJ1O/v73v/P73/+et9566+yVizcD90qpK5OOabQgPt5eYbRn11RTpaWlRERE0LNnT06dOsWbb77Z5u8xZcoUVq9eDcCePXvYt2/fBfuUlZVRWlrKLbfcwv/93/+xc+dOAGbOnMnSpUvP7tcwFhIREUFZWVmbt1Up1flo0OhErr32WhISErjmmmv4xje+wZQpU9r8Pf7rv/6LkydPkpCQwE9/+lMSEhKIjIw8b5+SkhJuvvlmxo0bx/Tp03n++ecBO6X3ww8/ZOzYsSQkJPCHP/wBgNtuu43Vq1czfvx4HQhX6gonxlyQqqlLmzhxotm2bdt59+3fv59Ro0b5qEWdi9vtxu12ExISwqFDh5g1axaHDh0iIKD9eir1969U5yci240xEy+1n45pdDPl5eXceOONuN1ujDEsW7asXQOGUurKomeLbiYqKort27f7uhlKqS5KxzSUUkp5TYOGUkopr2nQUEop5TUNGkoppbymQaMDFBQUkJSURFJSEv369WPAgAFnb9fU1Hj9OitWrCAnJ+fs7fvvv/+8tB4dpWk7lFLdh86e6gDepEb3xooVK7j22mvp18+WXvcmXXp7aNoOpVT3oVcaPvbSSy8xadIkkpKS+M53vkN9fT1ut5t7772XMWPGMHr0aF544QXS0tJIT08nJSXl7BWKN+nSDx06RHJyMmPGjOGJJ54gKirqgjaUlZUxe/Zsxo0bx+jRo3n11VcB2Lp1K9OnT2fChAnMnj2b3NzcZtuhlOo+NGi0wFXiYsXOFbhKXO32Hnv37mXt2rV89NFHZ0/+q1atYvv27eTn57Nnzx727t3LN77xjbMn6YaTdtOkgQ3p0nft2sX111/PihUrAJs25NFHH2XPnj3079+/2XasX7+eIUOGsGvXLvbu3YvD4aC6uprvf//7rFmzhu3btzN37lyeeuqpS7ZDdW6b97u4+akV3PJ1Fy++6uL+F1aweb8LlwtWrLAlAZS6GO2eaoHzsJNVe21u9Hnj2yc3+oYNG9i6devZ1OiVlZXEx8dz0003cfDgQR555BFuvvlmZs2adcnXaild+ubNm1m/fj0AX//613nyyScveO7YsWNZtGgRixYt4tZbb2XKlCmkp6eTkZHBzJkzAairq2PgwIFtctyq46x718UPlzrJPxZL6LVrKT0ZR0XvTXBmE6+/FEH91Wn87YUX6eEfjTmVxHfzH+ZnCzswS6fqcjRotMAxzHHez/ZgjGHevHk888wzFzy2e/duXn/9dZYuXcqaNWtYvnz5RV/L23TpzRk1ahTbtm1j/fr1LFq0iNmzZzN79mzGjh3bYq0O1fm5Slw8vOppcmJ2wuBsSgIrwAwDvxq4eg/17mAILaImZAc11EHwDj7OGwa0YwEZ1eVp91QL4iPjmTd+HvGR7feta+bMmaxevZr8/HzAzrI6fvw4eXl5GGP46le/yuLFi9mxYwdweSnIJ02axNq1awFY1VBVqomTJ08SHh7Ovffey4IFC9ixYwcJCQmcPHmSLVu2AFBTU0NGRsZlt0N1vGUfppEX+iGEFENgJbiDITIbok5ASCkEl0LeKDhwC+SPgn13QlY7F5BRXZ5eafjQmDFj+O///m9mzpxJfX09gYGBpKam4u/vzwMPPIAxBhHhF7/4BWCn2D744IOEhoaePZlfygsvvMC9997LT3/6U2666aYL0qAD7Nq1i0WLFuHn50dQUBCpqakEBwfz6quv8sgjj1BaWkpdXR0LFiwgMTHxgnbouEbnlJkJAX6B+B3+IrV+ZVAVAVe9QXD1QKr9CiC4DP9991JXHgNhJQQWJnG9Q7um1MVpavQr3JkzZwgLC0NEePnll1m7di1r1qzp0DZ059+/L23e7yLV6eSOsQ4yt8VTXO8iaoKTaXEOUpfBxmNOvvtFByUlsLPMyfgIBw/fHd+hhcdU56Gp0RVgp83+4Ac/oL6+nujoaJ+t7VAdL3lUPMmjPOMTMwDiaRivSP41nD92oeMYyjsaNK5wM2bMOLuwUCmlWqvbDIRfad1wXYX+3pW6snSLoBESEkJBQYGewDqYMYaCggJCQkJ83RSlVBvpFt1TAwcO5MSJE+Tl5fm6Kd1OSEiILgpU6grSLYJGYGAgQ4cO9XUzlFKqy+sW3VNKKaXahgYNpZRSXtOgoZRSymtX3IpwEckDjjXzUCyQ38HNaS9X0rGAHk9ndiUdC+jxXMxgY0zvS+10xQWNlojINm+WyHcFV9KxgB5PZ3YlHQvo8bQF7Z5SSinlNQ0aSimlvNadgsbFqxh1LVfSsYAeT2d2JR0L6PG0WrcZ01BKKdV63elKQymlVCtp0FBKKeW1Lh80ROSLInJQRD4VkUXNPB4sImmexzeLyBDP/ZNEJN3zb5eI3NHRbW/O5R5Po8cHiUi5iDzaUW2+mFZ8PkNEpLLRZ5Ta0W1vqjWfjYiMFZGPRSRDRPaIiM9T/7bis7mn0eeSLiL1IpLU0e1vqhXHEygiL3k+l/0i8qOObntTrTiWIBFZ6TmWXSIyo80bZ4zpsv8AfyALGAYEAbuAhCb7fAdI9WzfBaR5tsOAAM92f+B0w+2ueDyNHn8V+DvwaBf/fIYAe319DG10LAHAbmCc53YvwL+rHk+TfcYAWV388/k6sMqzHQYcBYZ00WP5LrDSs90H2A74tWX7uvqVxiTgU2PMYWNMDbAKuK3JPrcBL3m2XwVuFBExxlQYY9ye+0OAzjAj4LKPB0BEbgeOABkd1N5LadXxdDKtOZZZwG5jzC4AY0yBMaaug9rdkrb6bO72PNfXWnM8BughIgFAKFADlHZMs5vVmmNJAN4BMMacBoqBNl3819WDxgDA1ej2Cc99ze7jCRIl2G96iEiyiGQAe4D5jYKIr1z28YhIOPA48NMOaKe3WvX5AENFZKeIvCci09q7sZfQmmMZCRgReVNEdojIwg5o76W09rNpkAK80k5t/CxaczyvAmeAU8Bx4FfGmML2bvBFtOZYdgFzRCRARIYCE7DF4dtMt6in0RJjzGYgUURGAS+JyOvGmCpft+sy/QT4P2NMeef8ov6ZnQIGGWMKRGQC8E8RSTTG+PIb4OUKAKYC1wEVwNsist0Y87Zvm9U6IpIMVBhj9vq6La00CagD4oBoYJOIbDDGHPZtsy7LCmAUsA2bg+8j7LG1ma5+pXGS86PoQM99ze7jufyMBAoa72CM2Q+UA6PbraXeac3xJAPPichR4AfAj0Xke+3d4Eu47OMxxlQbYwoAjDHbsX28I9u9xS1rzWdzAnjfGJNvjKkA1gPXtnuLL64t/u/cRee4yoDWHc/XgTeMMbWeLp0PaeMunc+oNf9v3MaY/2eMSTLG3AZEAZlt2biuHjS2AiNEZKiIBGH/iNc12WcdcJ9n+07gHWOM8TwnAEBEBgPXYAfAfOmyj8cYM80YM8QYMwT4NfA/xpjfdVTDW9Caz6e3iPgDiMgwYATgy29+l30swJvAGBEJ8/zNTQf2dVC7W9Ka40FE/ICv0TnGM6B1x3Mc+AKAiPQAJgMHOqTVzWvN/5swzzEgIg7AbYxp2781X80QaKt/wJewkTQLeMJz32Jgjmc7BDub6FNgCzDMc/+92AHjdGAHcLuvj6U1x9PkNX5CJ5g91crP5ytNPp9bu+qxeB6b6zmevcBzvj6WNjieGcAnvj6GNvpbC/fcn4EN5o914WMZAhwE9gMbsOnO27RtmkZEKaWU17p695RSSqkOpEFDKaWU1zRoKKWU8poGDaWUUl7ToKGUUsprGjSUUkp5TYOGUkopr/1/qshWjDTJ+WgAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -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 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"-----------------------------------------------------\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", + "RMSE Training = 0.0757375853624068\n", + "RMSE Testing = 0.07580806645083134\n", + "Out of Bag error = 0.5348983654969031\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07586964482710655\n", + "RMSE Testing = 0.07588828912269077\n", + "Out of Bag error = 0.536980725478543\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 111\n", "Number of features = 1\n", - "RMSE Training = 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0.07607366884690017\n", + "RMSE Testing = 0.07608311567515999\n", + "Out of Bag error = 0.5404068798209425\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 114\n", "Number of features = 1\n", - "RMSE Training = 0.0754482445066051\n", - "RMSE Testing = 0.07551892942489594\n", - "Out of Bag error = 0.5299009420491501\n", + "RMSE Training = 0.07512905935339827\n", + "RMSE Testing = 0.07517794933352481\n", + "Out of Bag error = 0.5257644553813962\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 115\n", "Number of features = 1\n", - "RMSE Training = 0.07554038784803362\n", - "RMSE Testing = 0.0755593654294237\n", - "Out of Bag error = 0.5309736270596809\n", + "RMSE Training = 0.07537234318975647\n", + "RMSE Testing = 0.07536332357317743\n", + "Out of Bag error = 0.529501089125716\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07526474514930911\n", + "RMSE Testing = 0.07524271049667965\n", + "Out of Bag error = 0.5278110915568349\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07538493501360466\n", + "RMSE Testing = 0.07539771076843224\n", + "Out of Bag error = 0.5291991182730158\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees 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"-----------------------------------------------------\n", "Number of trees = 127\n", "Number of features = 1\n", - "RMSE Training = 0.07557269734826384\n", - "RMSE Testing = 0.0757184736665953\n", - "Out of Bag error = 0.5317686558970021\n", + "RMSE Training = 0.0759361834449723\n", + "RMSE Testing = 0.07597975219703389\n", + "Out of Bag error = 0.5379259244867596\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07548559853515478\n", + "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", - "RMSE Training = 0.07550948962180926\n", - "RMSE Testing = 0.07559273464939514\n", - "Out of Bag error = 0.5316955245913748\n", + "RMSE Training = 0.07552737500984824\n", + "RMSE Testing = 0.0755984913246935\n", + "Out of Bag error = 0.5309799842514641\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 130\n", "Number of features = 1\n", - "RMSE Training = 0.07553364369768654\n", - "RMSE Testing = 0.07559320200937929\n", - "Out of Bag error = 0.5301141746652079\n", + "RMSE Training = 0.07584948798506856\n", + "RMSE Testing = 0.07590110138221565\n", + "Out of Bag error = 0.5359730329270009\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 131\n", "Number of features = 1\n", - "RMSE Training = 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"-----------------------------------------------------\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 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"-----------------------------------------------------\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 = 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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": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. 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", + "RMSE Testing = 0.06456473457442968\n", + "Out of Bag error = 0.39183296399022904\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 50\n", + "Number of features = 2\n", + "RMSE Training = 0.06441503531691949\n", + "RMSE Testing = 0.06436636827788714\n", + "Out of Bag error = 0.3893996216123007\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 51\n", + "Number of features = 2\n", + "RMSE Training = 0.0643273190304706\n", + "RMSE Testing = 0.0644045442754941\n", + "Out of Bag error = 0.3893973581512269\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 52\n", + "Number of features = 2\n", + "RMSE Training = 0.06418164934883493\n", + "RMSE Testing = 0.06413615779350715\n", + "Out of Bag error = 0.38746794327433104\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 53\n", + "Number of features = 2\n", + "RMSE Training = 0.06367778569268107\n", + "RMSE Testing = 0.06364495191866217\n", + "Out of Bag error = 0.3818174870522696\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 54\n", + "Number of features = 2\n", + "RMSE Training = 0.06533298937455637\n", + "RMSE Testing = 0.06533066105527283\n", + "Out of Bag error = 0.39983808068434007\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 55\n", + "Number of features = 2\n", + "RMSE Training = 0.06438247917570199\n", + "RMSE Testing = 0.064355355964522\n", + "Out of Bag error = 0.3887033418194874\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 56\n", + "Number of features = 2\n", + "RMSE Training = 0.06410645271181761\n", + "RMSE Testing = 0.06426318450615075\n", + "Out of Bag error = 0.3855921056330131\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 57\n", + "Number of features = 2\n", + "RMSE Training = 0.06453617522858829\n", + "RMSE Testing = 0.0644724551242025\n", + "Out of Bag error = 0.38930618036209363\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 58\n", + "Number of features = 2\n", + "RMSE Training = 0.0634625524962257\n", + "RMSE Testing = 0.06342363435360593\n", + "Out of Bag error = 0.3773736569472715\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 59\n", + "Number of features = 2\n", + "RMSE Training = 0.06426463100483955\n", + "RMSE Testing = 0.0642356992476131\n", + "Out of Bag error = 0.3876862654206306\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 60\n", + "Number of features = 2\n", + "RMSE Training = 0.06455727198267165\n", + "RMSE Testing = 0.06444169490595966\n", + "Out of Bag error = 0.3911470165165679\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 61\n", + "Number of features = 2\n", + "RMSE Training = 0.06459132335999826\n", + "RMSE Testing = 0.06475664563415937\n", + "Out of Bag error = 0.39036393022959026\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 62\n", + "Number of features = 2\n", + "RMSE Training = 0.06491177799817827\n", + "RMSE Testing = 0.0648871669769185\n", + "Out of Bag error = 0.3966749894209358\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 63\n", + "Number of features = 2\n", + "RMSE Training = 0.06367126765243955\n", + "RMSE Testing = 0.06368275859166747\n", + "Out of Bag error = 0.37924951560287234\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number 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"-----------------------------------------------------\n", + "Number of trees = 112\n", + "Number of features = 2\n", + "RMSE Training = 0.06486363168520962\n", + "RMSE Testing = 0.06486562429626827\n", + "Out of Bag error = 0.3926679273127708\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 113\n", + "Number of features = 2\n", + "RMSE Training = 0.06399900624884422\n", + "RMSE Testing = 0.06411268002115804\n", + "Out of Bag error = 0.38285580578149486\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 114\n", + "Number of features = 2\n", + "RMSE Training = 0.06430292117044759\n", + "RMSE Testing = 0.06427985512657255\n", + "Out of Bag error = 0.38486498881188036\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + 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"-----------------------------------------------------\n", + "Number of trees = 134\n", + "Number of features = 2\n", + "RMSE Training = 0.06384921938632128\n", + "RMSE Testing = 0.0638912407817604\n", + "Out of Bag error = 0.3805196522103797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 135\n", + "Number of features = 2\n", + "RMSE Training = 0.06409429443624184\n", + "RMSE Testing = 0.06406410105553005\n", + "Out of Bag error = 0.3822737005789877\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 136\n", + "Number of features = 2\n", + "RMSE Training = 0.06439371171077762\n", + "RMSE Testing = 0.06434349958693271\n", + "Out of Bag error = 0.3869298511291811\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number 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+ "Out of Bag error = 0.3852895759898314\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 144\n", + "Number of features = 2\n", + "RMSE Training = 0.06458353000793413\n", + "RMSE Testing = 0.06459322712478753\n", + "Out of Bag error = 0.38823183067052797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 145\n", + "Number of features = 2\n", + "RMSE Training = 0.06392471544380866\n", + "RMSE Testing = 0.06386559603217362\n", + "Out of Bag error = 0.38111828308537643\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 146\n", + "Number of features = 2\n", + "RMSE Training = 0.06402480079528483\n", + "RMSE Testing = 0.06397877847943659\n", + "Out of Bag error = 0.38246040605297854\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 147\n", + "Number of features = 2\n", + "RMSE Training = 0.06428734811447165\n", + "RMSE Testing = 0.06417502097258877\n", + "Out of Bag error = 0.3846169238410732\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 148\n", + "Number of features = 2\n", + "RMSE Training = 0.0640831613142483\n", + "RMSE Testing = 0.06408902433145172\n", + "Out of Bag error = 0.38307806424588725\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 149\n", + "Number of features = 2\n", + "RMSE Training = 0.06438675047679246\n", + "RMSE Testing = 0.06437996891736887\n", + "Out of Bag error = 0.38625022001618287\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 150\n", + "Number of features = 2\n", + "RMSE Training = 0.06466495185083813\n", + "RMSE Testing = 0.06458186105771743\n", + "Out of Bag error = 0.3900315671675289\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 151\n", + "Number of features = 2\n", + "RMSE Training = 0.06408909769345869\n", + "RMSE Testing = 0.06415880492321423\n", + "Out of Bag error = 0.38258032095408784\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 152\n", + "Number of features = 2\n", + "RMSE Training = 0.06428062103867507\n", + "RMSE Testing = 0.0642519397432695\n", + "Out of Bag error = 0.3846244385770786\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 153\n", + "Number of features = 2\n", + "RMSE Training = 0.06476460814871772\n", + "RMSE Testing = 0.06476823149548443\n", + "Out of Bag error = 0.3904214035964054\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 154\n", + "Number of features = 2\n", + "RMSE Training = 0.06462555582635872\n", + "RMSE Testing = 0.06464370831445848\n", + "Out of Bag error = 0.38920285353084927\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 155\n", + "Number of features = 2\n", + "RMSE Training = 0.06403334127496044\n", + "RMSE Testing = 0.06401501624526283\n", + "Out of Bag error = 0.381919243045597\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 156\n", + "Number of features = 2\n", + "RMSE Training = 0.0644963523414425\n", + "RMSE Testing = 0.06448132681378893\n", + "Out of Bag error = 0.38730887156438093\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 157\n", + "Number of features = 2\n", + "RMSE Training = 0.0644553044640783\n", + "RMSE Testing = 0.06436391269395689\n", + "Out of Bag error = 0.38649244380973335\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 158\n", + "Number of features = 2\n", + "RMSE Training = 0.06433498473838903\n", + "RMSE Testing = 0.06428802847530271\n", + "Out of Bag error = 0.3855213128929499\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 159\n", + "Number of features = 2\n", + "RMSE Training = 0.06414228391991286\n", + "RMSE Testing = 0.0641017223703412\n", + "Out of Bag error = 0.38301474885229564\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 160\n", + "Number of features = 2\n", + "RMSE Training = 0.06433327388643913\n", + "RMSE Testing = 0.06420783368028563\n", + "Out of Bag error = 0.3845123150831411\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 161\n", + "Number of features = 2\n", + "RMSE Training = 0.06438854587490626\n", + "RMSE Testing = 0.06437996912964492\n", + "Out of Bag error = 0.3853847840142298\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 162\n", + "Number of features = 2\n", + "RMSE Training = 0.06433923583066072\n", + "RMSE Testing = 0.06432979357156862\n", + "Out of Bag error = 0.3858736948618581\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 163\n", + "Number of features = 2\n", + "RMSE Training = 0.0641547569897708\n", + "RMSE Testing = 0.06425775184980534\n", + "Out of Bag error = 0.38306619667020525\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 164\n", + "Number of features = 2\n", + "RMSE Training = 0.06415581508017151\n", + "RMSE Testing = 0.06405474133556661\n", + "Out of Bag error = 0.3838765295211535\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 165\n", + "Number of features = 2\n", + "RMSE Training = 0.06421243923952676\n", + "RMSE Testing = 0.06422612533228282\n", + "Out of Bag error = 0.38392451736147254\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 166\n", + "Number of features = 2\n", + "RMSE Training = 0.06419289969734537\n", + "RMSE Testing = 0.06407954450182596\n", + "Out of Bag error = 0.38535035378939664\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 167\n", + "Number of features = 2\n", + "RMSE Training = 0.06429778572399039\n", + "RMSE Testing = 0.06426887923695337\n", + "Out of Bag error = 0.38533838254670155\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 168\n", + "Number of features = 2\n", + "RMSE Training = 0.06414709769559186\n", + "RMSE Testing = 0.0641249403960428\n", + "Out of Bag error = 0.3830802164220441\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 169\n", + "Number of features = 2\n", + "RMSE Training = 0.06418370564813783\n", + "RMSE Testing = 0.064183459936363\n", + "Out of Bag error = 0.38402174180504967\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 170\n", + "Number of features = 2\n", + "RMSE Training = 0.06456403753480912\n", + "RMSE Testing = 0.06453864658173886\n", + "Out of Bag error = 0.38873946698366507\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 171\n", + "Number of features = 2\n", + "RMSE Training = 0.0638569608530293\n", + "RMSE Testing = 0.06386744407655576\n", + "Out of Bag error = 0.3799667530746048\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 172\n", + "Number of features = 2\n", + "RMSE Training = 0.06444063338828118\n", + "RMSE Testing = 0.06445979403687273\n", + "Out of Bag error = 0.38694167671759067\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 173\n", + "Number of features = 2\n", + "RMSE Training = 0.06455939647967776\n", + "RMSE Testing = 0.0646154957416996\n", + "Out of Bag error = 0.387787560571093\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 174\n", + "Number of features = 2\n", + "RMSE Training = 0.06431206491065486\n", + "RMSE Testing = 0.06430621567966564\n", + "Out of Bag error = 0.385256513057972\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 175\n", + "Number of features = 2\n", + "RMSE Training = 0.06439260157074725\n", + "RMSE Testing = 0.06436040828035487\n", + "Out of Bag error = 0.3857688083959487\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 176\n", + "Number of features = 2\n", + "RMSE Training = 0.06434027439141007\n", + "RMSE Testing = 0.06436240901785296\n", + "Out of Bag error = 0.3857402734566432\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 177\n", + "Number of features = 2\n", + "RMSE Training = 0.06407955959699554\n", + "RMSE Testing = 0.06406473384493479\n", + "Out of Bag error = 0.38257948035347134\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 178\n", + "Number of features = 2\n", + "RMSE Training = 0.06417060165607787\n", + "RMSE Testing = 0.0641238317381633\n", + "Out of Bag error = 0.38347087507535665\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 179\n", + "Number of features = 2\n", + "RMSE Training = 0.06432444549101898\n", + "RMSE Testing = 0.06429893193568312\n", + "Out of Bag error = 0.38608611770620804\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 180\n", + "Number of features = 2\n", + "RMSE Training = 0.06449843240563236\n", + "RMSE Testing = 0.06449035733124815\n", + "Out of Bag error = 0.38718811426117067\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 181\n", + "Number of features = 2\n", + "RMSE Training = 0.06475094710129217\n", + "RMSE Testing = 0.06472139121333498\n", + "Out of Bag error = 0.39083721868046634\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 182\n", + "Number of features = 2\n", + "RMSE Training = 0.06413498886254927\n", + "RMSE Testing = 0.06413250514189639\n", + "Out of Bag error = 0.3833016080284549\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 183\n", + "Number of features = 2\n", + "RMSE Training = 0.06449101121109886\n", + "RMSE Testing = 0.06451035617068136\n", + "Out of Bag error = 0.3876715295067261\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 184\n", + "Number of features = 2\n", + "RMSE Training = 0.0645064853386338\n", + "RMSE Testing = 0.0645275292581733\n", + "Out of Bag error = 0.3873668093025086\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 185\n", + "Number of features = 2\n", + "RMSE Training = 0.06451112549316351\n", + "RMSE Testing = 0.06448627189281433\n", + "Out of Bag error = 0.38745560891970676\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 186\n", + "Number of features = 2\n", + "RMSE Training = 0.06430457768309558\n", + "RMSE Testing = 0.06425333407312138\n", + "Out of Bag error = 0.3845810054621894\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 187\n", + "Number of features = 2\n", + "RMSE Training = 0.06422785708045838\n", + "RMSE Testing = 0.06415592716187893\n", + "Out of Bag error = 0.38383672677246006\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 188\n", + "Number of features = 2\n", + "RMSE Training = 0.06438333248690543\n", + "RMSE Testing = 0.06444287212391037\n", + "Out of Bag error = 0.3862274773217883\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 189\n", + "Number of features = 2\n", + "RMSE Training = 0.06410823174249927\n", + "RMSE Testing = 0.06411238011628334\n", + "Out of Bag error = 0.38251274330219254\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 190\n", + "Number of features = 2\n", + "RMSE Training = 0.06407162609173674\n", + "RMSE Testing = 0.06405871089223998\n", + "Out of Bag error = 0.38242627694703346\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 191\n", + "Number of features = 2\n", + "RMSE Training = 0.06408869196722566\n", + "RMSE Testing = 0.0640522358516534\n", + "Out of Bag error = 0.38360005837649225\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 192\n", + "Number of features = 2\n", + "RMSE Training = 0.06411891558953393\n", + "RMSE Testing = 0.06402483199664191\n", + "Out of Bag error = 0.3822256454932185\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 193\n", + "Number of features = 2\n", + "RMSE Training = 0.06469464939567138\n", + "RMSE Testing = 0.06473771004491886\n", + "Out of Bag error = 0.3898817627822015\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 194\n", + "Number of features = 2\n", + "RMSE Training = 0.06416633930941953\n", + "RMSE Testing = 0.06410160859379366\n", + "Out of Bag error = 0.38308963585866024\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 195\n", + "Number of features = 2\n", + "RMSE Training = 0.06428508325483002\n", + "RMSE Testing = 0.064249939591444\n", + "Out of Bag error = 0.38505729409800665\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 196\n", + "Number of features = 2\n", + "RMSE Training = 0.06448599982191938\n", + "RMSE Testing = 0.06444532409910894\n", + "Out of Bag error = 0.386948110529531\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 197\n", + "Number of features = 2\n", + "RMSE Training = 0.06385818923259906\n", + "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", + "Number of features = 3\n", + "RMSE Training = 0.06091698669768365\n", + "RMSE Testing = 0.060834482910836665\n", + "Out of Bag error = 0.3496806008329285\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 32\n", + "Number of features = 3\n", + "RMSE Training = 0.0603522501165095\n", + "RMSE Testing = 0.06033635148432213\n", + "Out of Bag error = 0.3440668368960028\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 33\n", + "Number of features = 3\n", + "RMSE Training = 0.06064061420491705\n", + "RMSE Testing = 0.060534614571547254\n", + "Out of Bag error = 0.34699970407591424\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 34\n", + "Number of features = 3\n", + "RMSE Training = 0.061039843213025434\n", + "RMSE Testing = 0.06115134181978303\n", + "Out of Bag error = 0.35147634800712313\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 35\n", + "Number of features = 3\n", + "RMSE Training = 0.060085855612217506\n", + "RMSE Testing = 0.0600231721974408\n", + "Out of Bag error = 0.3402501825623455\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 36\n", + "Number of features = 3\n", + "RMSE Training = 0.060214374081219914\n", + "RMSE Testing = 0.06004530237721678\n", + "Out of Bag error = 0.34053154130066837\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 37\n", + "Number of features = 3\n", + "RMSE Training = 0.060113340372585554\n", + "RMSE Testing = 0.05988746455956688\n", + "Out of Bag error = 0.33963777764104514\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 38\n", + "Number of features = 3\n", + "RMSE Training = 0.06014757290870888\n", + "RMSE Testing = 0.060074129430803404\n", + "Out of Bag error = 0.3396756727158878\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 39\n", + "Number of features = 3\n", + "RMSE Training = 0.06055463606628823\n", + "RMSE Testing = 0.06039341750048517\n", + "Out of Bag error = 0.3432077019701885\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 40\n", + "Number of features = 3\n", + "RMSE Training = 0.06052663884007028\n", + "RMSE Testing = 0.06055367126228994\n", + "Out of Bag error = 0.34384649228563546\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 41\n", + "Number of features = 3\n", + "RMSE Training = 0.05980729983645246\n", + "RMSE Testing = 0.05971833671206622\n", + "Out of Bag error = 0.33567305966921324\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 42\n", + "Number of features = 3\n", + "RMSE Training = 0.061291079578224515\n", + "RMSE Testing = 0.06120499930789811\n", + "Out of Bag error = 0.3519052962253236\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 43\n", + "Number of features = 3\n", + "RMSE Training = 0.06032252483449361\n", + "RMSE Testing = 0.06026232974080949\n", + "Out of Bag error = 0.3414620925584081\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 44\n", + "Number of features = 3\n", + "RMSE Training = 0.060418413035242736\n", + "RMSE Testing = 0.06041592337499846\n", + "Out of Bag error = 0.34211305461933056\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 45\n", + "Number of features = 3\n", + "RMSE Training = 0.06093827163750744\n", + "RMSE Testing = 0.060888046526366035\n", + "Out of Bag error = 0.3482566321813663\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 46\n", + "Number of features = 3\n", + "RMSE Training = 0.06028671478621832\n", + "RMSE Testing = 0.0602374423391963\n", + "Out of Bag error = 0.34119321831109206\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 47\n", + "Number of features = 3\n", + "RMSE Training = 0.06034326427251022\n", + "RMSE Testing = 0.060336952187909666\n", + "Out of Bag error = 0.3425451499536285\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 48\n", + "Number of features = 3\n", + "RMSE Training = 0.05992624057708684\n", + "RMSE Testing = 0.05984620038880524\n", + "Out of Bag error = 0.33765781442892717\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 49\n", + "Number of features = 3\n", + "RMSE Training = 0.06061881062626917\n", + "RMSE Testing = 0.06056528307841373\n", + "Out of Bag error = 0.34395973624060894\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 50\n", + "Number of features = 3\n", + "RMSE Training = 0.060187955362830524\n", + "RMSE Testing = 0.060263641884764615\n", + "Out of Bag error = 0.34000180730385055\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 51\n", + "Number of features = 3\n", + "RMSE Training = 0.06061943129065158\n", + "RMSE Testing = 0.06049003382164022\n", + "Out of Bag error = 0.3428767219289209\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 52\n", + "Number of features = 3\n", + "RMSE Training = 0.06062399400272026\n", + "RMSE Testing = 0.060647617675123014\n", + "Out of Bag error = 0.3438568851502549\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 53\n", + "Number of features = 3\n", + "RMSE Training = 0.05996638091020336\n", + "RMSE Testing = 0.05983479385953089\n", + "Out of Bag error = 0.3363059323273305\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 54\n", + "Number of features = 3\n", + "RMSE Training = 0.06060052782468596\n", + "RMSE Testing = 0.0605563261964027\n", + "Out of Bag error = 0.3427181408577277\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 55\n", + "Number of features = 3\n", + "RMSE Training = 0.060343924565608276\n", + "RMSE Testing = 0.060316476892592306\n", + "Out of Bag error = 0.34135905301675773\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 56\n", + "Number of features = 3\n", + "RMSE Training = 0.060815148542910605\n", + "RMSE Testing = 0.06081940116943817\n", + "Out of Bag error = 0.34487673186983797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 57\n", + "Number of features = 3\n", + "RMSE Training = 0.05984780352309484\n", + "RMSE Testing = 0.059707800668086494\n", + "Out of Bag error = 0.3355227096489359\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 58\n", + "Number of features = 3\n", + "RMSE Training = 0.0605732392788342\n", + "RMSE Testing = 0.06059587285923875\n", + "Out of Bag error = 0.3430678555907575\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 59\n", + "Number of features = 3\n", + "RMSE Training = 0.06029601205522613\n", + "RMSE Testing = 0.06029240820831383\n", + "Out of Bag error = 0.33973041384496655\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 60\n", + "Number of features = 3\n", + "RMSE Training = 0.060841580205563774\n", + "RMSE Testing = 0.06070652267588863\n", + "Out of Bag error = 0.34662537276549255\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 61\n", + "Number of features = 3\n", + "RMSE Training = 0.06074137905772017\n", + "RMSE Testing = 0.06065663768732514\n", + "Out of Bag error = 0.3445208313455531\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 62\n", + "Number of features = 3\n", + "RMSE Training = 0.06092769785944051\n", + "RMSE Testing = 0.060927185621512184\n", + "Out of Bag error = 0.346863696111961\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 63\n", + "Number of features = 3\n", + "RMSE Training = 0.06010154183752377\n", + "RMSE Testing = 0.06003120250181858\n", + "Out of Bag error = 0.33745097446130284\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 64\n", + "Number of features = 3\n", + "RMSE Training = 0.06024877593573227\n", + "RMSE Testing = 0.060173615539473216\n", + "Out of Bag error = 0.3393674882757437\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 65\n", + "Number of features = 3\n", + "RMSE Training = 0.06035198683173373\n", + "RMSE Testing = 0.060269386947751705\n", + "Out of Bag error = 0.3398913628569875\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 66\n", + "Number of features = 3\n", + "RMSE Training = 0.060191832205992155\n", + "RMSE Testing = 0.060177994380073783\n", + "Out of Bag error = 0.3390324655480253\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 67\n", + "Number of features = 3\n", + "RMSE Training = 0.060457260225578034\n", + "RMSE Testing = 0.060447751620351986\n", + "Out of Bag error = 0.3413603284776936\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 68\n", + "Number of features = 3\n", + "RMSE Training = 0.060769094284179245\n", + "RMSE Testing = 0.060808956822635464\n", + "Out of Bag error = 0.34505206697300644\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 69\n", + "Number of features = 3\n", + "RMSE Training = 0.06061624272504538\n", + "RMSE Testing = 0.06050864560172926\n", + "Out of Bag error = 0.34244402396441614\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 70\n", + "Number of features = 3\n", + "RMSE Training = 0.06099330059216068\n", + "RMSE Testing = 0.060984566041962875\n", + "Out of Bag error = 0.3470927955176909\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 71\n", + "Number of features = 3\n", + "RMSE Training = 0.06033786346252786\n", + "RMSE Testing = 0.06031197211689017\n", + "Out of Bag error = 0.34043250651108536\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 72\n", + "Number of features = 3\n", + "RMSE Training = 0.06070702143904837\n", + "RMSE Testing = 0.06072660557652474\n", + "Out of Bag error = 0.3437801605680195\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 73\n", + "Number of features = 3\n", + "RMSE Training = 0.06072795777070367\n", + "RMSE Testing = 0.06070025142432709\n", + "Out of Bag error = 0.34456988887820766\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 74\n", + "Number of features = 3\n", + "RMSE Training = 0.06030313253879811\n", + "RMSE Testing = 0.060314386030869284\n", + "Out of Bag error = 0.3390874309386811\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 75\n", + "Number of features = 3\n", + "RMSE Training = 0.05977089691135261\n", + "RMSE Testing = 0.059658703551353845\n", + "Out of Bag error = 0.3331347176534764\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 76\n", + "Number of features = 3\n", + "RMSE Training = 0.060465227569251254\n", + "RMSE Testing = 0.06040909799544517\n", + "Out of Bag error = 0.3409142168647058\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 77\n", + "Number of features = 3\n", + "RMSE Training = 0.06066356240390812\n", + "RMSE Testing = 0.060661471696154066\n", + "Out of Bag error = 0.3429050934816857\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 78\n", + "Number of features = 3\n", + "RMSE Training = 0.060273913206822585\n", + "RMSE Testing = 0.060180945396984285\n", + "Out of Bag error = 0.3385153157897655\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 79\n", + "Number of features = 3\n", + "RMSE Training = 0.06069014146090823\n", + "RMSE Testing = 0.0607303076758182\n", + "Out of Bag error = 0.3427954827156764\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 80\n", + "Number of features = 3\n", + "RMSE Training = 0.06024235416629699\n", + "RMSE Testing = 0.05999482654962378\n", + "Out of Bag error = 0.33867882109679315\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 81\n", + "Number of features = 3\n", + "RMSE Training = 0.06030022698400821\n", + "RMSE Testing = 0.06028220587388313\n", + "Out of Bag error = 0.3388553129201502\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 82\n", + "Number of features = 3\n", + "RMSE Training = 0.06025571321060909\n", + "RMSE Testing = 0.060267166475006836\n", + "Out of Bag error = 0.33877192431226966\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + 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0.33795226730172684\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 162\n", + "Number of features = 3\n", + "RMSE Training = 0.060243661754051284\n", + "RMSE Testing = 0.060240228402086385\n", + "Out of Bag error = 0.33706230082006466\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 163\n", + "Number of features = 3\n", + "RMSE Training = 0.06047797652894567\n", + "RMSE Testing = 0.060444738922313626\n", + "Out of Bag error = 0.3399820683172788\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 164\n", + "Number of features = 3\n", + "RMSE Training = 0.06064285465291289\n", + "RMSE Testing = 0.060567648298826174\n", + "Out of Bag error = 0.34282506168227844\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 165\n", + "Number of features = 3\n", + "RMSE Training = 0.060301493484967904\n", + "RMSE Testing = 0.060226683716943154\n", + "Out of Bag error = 0.3377858936595657\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 166\n", + "Number of features = 3\n", + "RMSE Training = 0.06008884362322421\n", + "RMSE Testing = 0.05997586612873792\n", + "Out of Bag error = 0.33600468637619896\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 167\n", + "Number of features = 3\n", + "RMSE Training = 0.060522004734584724\n", + "RMSE Testing = 0.06044944314158755\n", + "Out of Bag error = 0.3404019625988007\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 168\n", + "Number of features = 3\n", + "RMSE Training = 0.0601282736029474\n", + "RMSE Testing = 0.060116885718335865\n", + "Out of Bag error = 0.3357785177972712\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 169\n", + "Number of features = 3\n", + "RMSE Training = 0.059928466762988776\n", + "RMSE Testing = 0.059827769446961346\n", + "Out of Bag error = 0.33432910884248684\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 170\n", + "Number of features = 3\n", + "RMSE Training = 0.060212808831823596\n", + "RMSE Testing = 0.060156253646714494\n", + "Out of Bag error = 0.33725844436959374\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 171\n", + "Number of features = 3\n", + "RMSE Training = 0.060370989071659845\n", + "RMSE Testing = 0.060351890372468464\n", + "Out of Bag error = 0.3387147920684247\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 172\n", + "Number of features = 3\n", + "RMSE Training = 0.059776376044830805\n", + "RMSE Testing = 0.059815852380838176\n", + "Out of Bag error = 0.3322584525600456\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 173\n", + "Number of features = 3\n", + "RMSE Training = 0.06038116365616941\n", + "RMSE Testing = 0.06042246290218768\n", + "Out of Bag error = 0.33888553481197936\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 174\n", + "Number of features = 3\n", + "RMSE Training = 0.06013942162886463\n", + "RMSE Testing = 0.06006615607433241\n", + "Out of Bag error = 0.3365037616278507\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 175\n", + "Number of features = 3\n", + "RMSE Training = 0.06022697983552385\n", + "RMSE Testing = 0.060119789178867365\n", + "Out of Bag error = 0.33705444808107676\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 176\n", + "Number of features = 3\n", + "RMSE Training = 0.06012088756015258\n", + "RMSE Testing = 0.060075432835225574\n", + "Out of Bag error = 0.33598821225059483\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 177\n", + "Number of features = 3\n", + "RMSE Training = 0.06074990388081959\n", + "RMSE Testing = 0.06071862920412371\n", + "Out of Bag error = 0.3427863251850691\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 178\n", + "Number of features = 3\n", + "RMSE Training = 0.06021900298482273\n", + "RMSE Testing = 0.06011230098451089\n", + "Out of Bag error = 0.33690053804598014\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 179\n", + "Number of features = 3\n", + "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", + "Number of features = 4\n", + "RMSE Training = 0.06088152014341284\n", + "RMSE Testing = 0.06081940877209269\n", + "Out of Bag error = 0.34393115735504887\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 81\n", + "Number of features = 4\n", + "RMSE Training = 0.06084642220462405\n", + "RMSE Testing = 0.06081923411798048\n", + "Out of Bag error = 0.34326972099874753\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 82\n", + "Number of features = 4\n", + "RMSE Training = 0.060899413531212\n", + "RMSE Testing = 0.060786451289099576\n", + "Out of Bag error = 0.34366678126566386\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 83\n", + "Number of features = 4\n", + "RMSE Training = 0.06094331067390481\n", + "RMSE Testing = 0.06085018521097019\n", + "Out of Bag error = 0.34387933771381807\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 84\n", + "Number of features = 4\n", + "RMSE Training = 0.06086852712782605\n", + "RMSE Testing = 0.06075448343817134\n", + "Out of Bag error = 0.3433365362908357\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 85\n", + "Number of features = 4\n", + "RMSE Training = 0.060916107189766554\n", + "RMSE Testing = 0.06082255654306161\n", + "Out of Bag error = 0.34420471615775866\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 86\n", + "Number of features = 4\n", + "RMSE Training = 0.06071420039474397\n", + "RMSE Testing = 0.06060075453238593\n", + "Out of Bag error = 0.342505689298383\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 87\n", + "Number of features = 4\n", + "RMSE Training = 0.060972263262722835\n", + "RMSE Testing = 0.06088322750253504\n", + "Out of Bag error = 0.3448589398838598\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 88\n", + "Number of features = 4\n", + "RMSE Training = 0.06089209579626492\n", + "RMSE Testing = 0.060797082894489686\n", + "Out of Bag error = 0.3438218796566104\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 89\n", + "Number of features = 4\n", + "RMSE 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"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", + "Number of trees = 62\n", + "Number of features = 5\n", + "RMSE Training = 0.060443430304165414\n", + "RMSE Testing = 0.060382968914671965\n", + "Out of Bag error = 0.3385804302453199\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 63\n", + "Number of features = 5\n", + "RMSE Training = 0.06052207619610943\n", + "RMSE Testing = 0.06042414964536449\n", + "Out of Bag error = 0.3391607676623953\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 64\n", + "Number of features = 5\n", + "RMSE Training = 0.06042964411239852\n", + "RMSE Testing = 0.0603327493337512\n", + "Out of Bag error = 0.33811490271868483\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 65\n", + "Number of features = 5\n", + "RMSE Training = 0.060606930036977036\n", + "RMSE Testing = 0.060513728108503174\n", + "Out of Bag error = 0.3399485796551505\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 66\n", + "Number of features = 5\n", + "RMSE Training = 0.06060742857694643\n", + "RMSE Testing = 0.060481656205060054\n", + "Out of Bag error = 0.3398340440334576\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 67\n", + "Number of features = 5\n", + "RMSE Training = 0.060596262503731235\n", + "RMSE Testing = 0.060494201706877936\n", + "Out of Bag error = 0.340116048067565\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 68\n", + "Number of features = 5\n", + "RMSE Training = 0.0606265683865012\n", + "RMSE Testing = 0.06052602574817998\n", + "Out of Bag error = 0.34035863415998885\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 69\n", + "Number of features = 5\n", + "RMSE Training = 0.060567523475451426\n", + "RMSE Testing = 0.060459880634075334\n", + "Out of Bag error = 0.3396080855531896\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 70\n", + "Number of features = 5\n", + "RMSE Training = 0.06035997102224093\n", + "RMSE Testing = 0.0602703990074684\n", + "Out of Bag error = 0.33763550643034973\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 71\n", + "Number of features = 5\n", + "RMSE Training = 0.060552818852778675\n", + "RMSE Testing = 0.060428114405788935\n", + "Out of Bag error = 0.3393773879739771\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 72\n", + "Number of features = 5\n", + "RMSE Training = 0.060611498764467434\n", + "RMSE Testing = 0.0605161713564253\n", + "Out of Bag error = 0.34015500720763264\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 73\n", + "Number of features = 5\n", + "RMSE Training = 0.06035386859446602\n", + "RMSE Testing = 0.06027102234472498\n", + "Out of Bag error = 0.33731736758901637\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 74\n", + "Number of features = 5\n", + "RMSE Training = 0.06053095859607668\n", + "RMSE Testing = 0.06043725726435365\n", + "Out of Bag error = 0.3392903874602296\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 75\n", + "Number of features = 5\n", + "RMSE Training = 0.06049890937911736\n", + "RMSE Testing = 0.060388025965544045\n", + "Out of Bag error = 0.3389776805392891\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 76\n", + "Number of features = 5\n", + "RMSE Training = 0.06061131519850242\n", + "RMSE Testing = 0.06050497864231922\n", + "Out of Bag error = 0.3402196972511747\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 77\n", + "Number of features = 5\n", + "RMSE Training = 0.06054418377096158\n", + "RMSE Testing = 0.06044350326841148\n", + "Out of Bag error = 0.3394923362825727\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 78\n", + "Number of features = 5\n", + "RMSE Training = 0.060569521128277716\n", + "RMSE Testing = 0.060476653936981914\n", + "Out of Bag error = 0.339641392828758\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 79\n", + "Number of features = 5\n", + "RMSE Training = 0.060464920513289944\n", + "RMSE Testing = 0.06037971088105024\n", + "Out of Bag error = 0.33846535937050837\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 80\n", + "Number of features = 5\n", + "RMSE Training = 0.06046144810404832\n", + "RMSE Testing = 0.0603612948671318\n", + "Out of Bag error = 0.3384948967380896\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 81\n", + "Number of features = 5\n", + "RMSE Training = 0.060519739996118906\n", + "RMSE Testing = 0.060389843512680966\n", + "Out of Bag error = 0.33905991692293874\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 82\n", + "Number of features = 5\n", + "RMSE Training = 0.06048914838813698\n", + "RMSE Testing = 0.0603789837430538\n", + "Out of Bag error = 0.3389027893499034\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 83\n", + "Number of features = 5\n", + "RMSE Training = 0.06051545622034926\n", + "RMSE Testing = 0.060424221173177785\n", + "Out of Bag error = 0.3391890031339676\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 84\n", + "Number of features = 5\n", + "RMSE Training = 0.06059296811415088\n", + "RMSE Testing = 0.06050789337335798\n", + "Out of Bag error = 0.33992405297693107\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 85\n", + "Number of features = 5\n", + "RMSE Training = 0.060527212142341344\n", + "RMSE Testing = 0.06043576656231141\n", + "Out of Bag error = 0.33947401357565127\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 86\n", + "Number of features = 5\n", + "RMSE Training = 0.06051589309324491\n", + "RMSE Testing = 0.06042034883837283\n", + "Out of Bag error = 0.3391456892138915\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 87\n", + "Number of features = 5\n", + "RMSE Training = 0.060601555243438396\n", + "RMSE Testing = 0.060466573740589266\n", + "Out of Bag error = 0.34001965951946944\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 88\n", + "Number of features = 5\n", + "RMSE Training = 0.06050909701073416\n", + "RMSE Testing = 0.06041177587695864\n", + "Out of Bag error = 0.33902547758647117\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 89\n", + "Number of features = 5\n", + "RMSE Training = 0.06038118524416149\n", + "RMSE Testing = 0.060323636058243965\n", + "Out of Bag error = 0.3375193716165724\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 90\n", + "Number of features = 5\n", + "RMSE Training = 0.060493495759439596\n", + "RMSE Testing = 0.06039605136476937\n", + "Out of Bag error = 0.33884190087193716\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 91\n", + "Number of features = 5\n", + "RMSE Training = 0.06057107503741486\n", + "RMSE Testing = 0.06048749018922674\n", + "Out of Bag error = 0.3396239909490232\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 92\n", + "Number of features = 5\n", + "RMSE Training = 0.06054973534044887\n", + "RMSE Testing = 0.06046629092799266\n", + "Out of Bag error = 0.33946811874279736\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 93\n", + "Number of features = 5\n", + "RMSE Training = 0.06061092808561862\n", + "RMSE Testing = 0.06048907902200158\n", + "Out of Bag error = 0.34015017492387656\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 94\n", + "Number of features = 5\n", + "RMSE Training = 0.060519790612524715\n", + "RMSE Testing = 0.06043462960913791\n", + "Out of Bag error = 0.33910892068505094\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 95\n", + "Number of features = 5\n", + "RMSE Training = 0.06053214405746236\n", + "RMSE Testing = 0.06043549367606608\n", + "Out of Bag error = 0.33929514609714156\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 96\n", + "Number of features = 5\n", + "RMSE Training = 0.060499499722673156\n", + "RMSE Testing = 0.060412889338944474\n", + "Out of Bag error = 0.338790207821698\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 97\n", + "Number of features = 5\n", + "RMSE Training = 0.060462946104966865\n", + "RMSE Testing = 0.06034864588692225\n", + "Out of Bag error = 0.3384303169520767\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 98\n", + "Number of features = 5\n", + "RMSE Training = 0.06046367243358684\n", + "RMSE Testing = 0.06039332891760866\n", + "Out of Bag error = 0.3385637670389797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 99\n", + "Number of features = 5\n", + "RMSE Training = 0.06045374840865314\n", + "RMSE Testing = 0.06036224262372107\n", + "Out of Bag error = 0.3384335912510298\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 100\n", + "Number of features = 5\n", + "RMSE Training = 0.06054973706751643\n", + "RMSE Testing = 0.06044500851719583\n", + "Out of Bag error = 0.33955928123769547\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 101\n", + "Number of features = 5\n", + "RMSE Training = 0.060438954048713245\n", + "RMSE Testing = 0.06035023450551252\n", + "Out of Bag error = 0.3380949934815079\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 102\n", + "Number of features = 5\n", + "RMSE Training = 0.06053949610812983\n", + "RMSE Testing = 0.06044452510037544\n", + "Out of Bag error = 0.33920295441845577\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 103\n", + "Number of features = 5\n", + "RMSE Training = 0.060508238807270485\n", + "RMSE Testing = 0.06040486227735055\n", + "Out of Bag error = 0.33895666113281175\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 104\n", + "Number of features = 5\n", + "RMSE Training = 0.06044223760812717\n", + "RMSE Testing = 0.06035300695770942\n", + "Out of Bag error = 0.338442337771912\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + 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[ + "-----------------------------------------------------\n", + "Number of trees = 190\n", + "Number of features = 5\n", + "RMSE Training = 0.0604940057664524\n", + "RMSE Testing = 0.06039991926315389\n", + "Out of Bag error = 0.33882302305563455\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 191\n", + "Number of features = 5\n", + "RMSE Training = 0.06053145379315141\n", + "RMSE Testing = 0.06041056038917146\n", + "Out of Bag error = 0.33914918790677673\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 192\n", + "Number of features = 5\n", + "RMSE Training = 0.06044086176357003\n", + "RMSE Testing = 0.0603486126844728\n", + "Out of Bag error = 0.3380447134124191\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 193\n", + "Number of features = 5\n", + "RMSE Training = 0.06039501008271615\n", + "RMSE Testing = 0.06031146045318168\n", + "Out of Bag error = 0.3378094701947227\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 194\n", + "Number of features = 5\n", + "RMSE Training = 0.06050101124967529\n", + "RMSE Testing = 0.060392194466090766\n", + "Out of Bag error = 0.33870780242654336\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 195\n", + "Number of features = 5\n", + "RMSE Training = 0.060537022906515546\n", + "RMSE Testing = 0.06042821632403265\n", + "Out of Bag error = 0.339032667581182\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 196\n", + "Number of features = 5\n", + "RMSE Training = 0.06051249758519792\n", + "RMSE Testing = 0.06040891686195142\n", + "Out of Bag error = 0.33884943202986517\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 197\n", + "Number of features = 5\n", + "RMSE Training = 0.060482333122738395\n", + "RMSE Testing = 0.06038450267735157\n", + "Out of Bag error = 0.3387200434121965\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 198\n", + "Number of features = 5\n", + "RMSE Training = 0.060459132880708887\n", + "RMSE Testing = 0.060360767224025356\n", + "Out of Bag error = 0.3383033102168782\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 199\n", + "Number of features = 5\n", + "RMSE Training = 0.060516140293304745\n", + "RMSE Testing = 0.06039259220413109\n", + "Out of Bag error = 0.33899469343187266\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 200\n", + "Number of features = 5\n", + "RMSE Training = 0.060470622944550655\n", + "RMSE Testing = 0.06035642665436379\n", + "Out of Bag error = 0.33852502883154906\n", + "-----------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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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", 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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", "text/plain": [ "
" ] @@ -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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jTFcGVHIozBlETlqS1TsYY0wXBlRyEBGmFWbblYMxxnRhQCUH8OodSqvq2X+o2e9QjDEmZg3I5KAKKyvq/A7FGGNiVlSTg4jMEZH1IlIqIreHmD5aRF4RkZUi8pqIFEQzHoAT7U5pY4zpUtSSg4jEAw8CFwPFwDUiUhw028+Bx1T1BOBu4KfRiqdd1qBExuWmscwqpY0xplPRvHKYBZSqapmqNgFPAZcHzVMMvOreLwwxPSraK6Wth1ZjjAktmslhJFAeMFzhxgVaAVzp3n8MyBCRIcELEpGbRKREREqqqqqOOrDphdlU1zdSuffgUS/LGGP6I78rpG8DzhaRZcDZQCXQGjyTqj6kqjNVdWZubu5Rr3Sa3QxnjDFhRTM5VAKFAcMFbtxhqrpdVa9U1enA99y4qB+xjxueQXJCnN0MZ4wxnYhmclgCTBSRsSKSBFwNzAucQUSGikh7DHcAj0QxnsMS4+M4fmQWy+zKwRhjQopaclDVFuBWYD6wFnhGVVeLyN0iMtfNNhtYLyIbgHzgx9GKJ9i0wmw+qKyj2XpoNcaYDhKiuXBVfRF4MWjcnQHvnwOei2YMnZlWmM0f39zMuh37mVqQ5UcIxhgTs/yukPbNh48N3eNzJMYYE3sGbHIoGDyIoelJVu9gjDEhDNjkYD20GmNM5wZscgCYPmowZVUHqGuwHlqNMSbQgE4O7fUOKyrs6sEYYwIN6ORwQkEWInantDHGBBvQySEjJZEJuemWHIwxJsiATg5gPbQaY0wolhxGZVN7oInyWuuh1Rhj2llycJXSy+xmOGOMOWzAJ4dJ+RkMSoy3egdjjAkw4JNDQnwcU0dmWXIwxpgAAz45gFfvsHr7PpparIdWY4wBSw6AV+/Q1NLG2h37/A7FGGNigiUHAntotaIlY4wBSw4ADM9KIS8j2ZKDMcY4lhzwemidPiqbkq21fodijDExwZKDc8aEoZTXHmRz9QG/QzHGGN9FNTmIyBwRWS8ipSJye4jpo0RkoYgsE5GVInJJNOMJZ/akPAAWrtvtVwjGGBMzopYcRCQeeBC4GCgGrhGR4qDZvg88o6rTgauB30Yrnq4U5qQyLjeN1zZU+RWCMcbEjGheOcwCSlW1TFWbgKeAy4PmUSDTvc8Ctkcxni6dMymPd8tqONjU6mcYxhjju2gmh5FAecBwhRsX6C7gMyJSAbwIfDnUgkTkJhEpEZGSqqrondnPnpRLU0sb75bVRG0dxhjTF/hdIX0N8GdVLQAuAf4iIh1iUtWHVHWmqs7Mzc2NWjCzxuYwKDGeheut3sEYM7BFMzlUAoUBwwVuXKAbgGcAVPUdIAUYGsWYwkpOiOe08UN4bX2VPd/BGDOgRTM5LAEmishYEUnCq3CeFzTPNuA8ABGZjJccfK0Rnn1cHttqG6xJqzFmQItaclDVFuBWYD6wFq9V0moRuVtE5rrZvgncKCIrgCeB69XnU/bZRV6x1WvrrdWSMWbgSojmwlX1RbyK5sBxdwa8XwOcHs0YuqswJ5XxuWksXL+bz58x1u9wjDHGF35XSMek2ZPyWLy51pq0GmMGLEsOIZwzKY+mljbeKav2OxRjjPGFJYcQTh47mNSkeBaus3oHY8zAZMkhhMNNWjfstiatxpgByZJDJ86elEd57UHKrEmrMWYAsuTQCWvSaowZyCw5dKIwJ5UJeem8Zl1pGGMGIEsOYcwuymVxWS0NTS1+h2KMMb3KkkMYsyfl0dTaxjubrJdWY8zAYskhjPYmrVbvYIwZaCw5hOE1aR3KwvXWpNUYM7BYcujC7Em5VOw5yKYqa9JqjBk4LDl0Yfak9iat1mrJGDNwhE0OIhIvIgt7K5hYVDA4lYl56by+weodjDEDR9jkoKqtQJuIZPVSPDFp9iSvSeuBRmvSaowZGCIpVqoHVonIH0Xkf9pf0Q4slliTVmPMQBPJw36ed68Ba+aYwaQlxfP6hirOL873OxxjjIm6LpODqj7qngFd5EatV9Xm6IYVW5IT4plakMUH2+v8DsUYY3pFl8VKIjIb2Ag8CPwW2CAiZ0WycBGZIyLrRaRURG4PMf0XIrLcvTaIyN5uxt9rivIz2Lir3u53MMYMCJEUK90PXKiq6wFEpAh4Ejgp3IdEJB4voVwAVABLRGSee240AKr69YD5vwxM7/Y36CVF+RnUN7awve4QI7MH+R2OMcZEVSQV0ontiQFAVTcAiRF8bhZQqqplqtoEPAVcHmb+a/CSTkyaNCwDgA079/sciTHGRF8kyaFERP4gIrPd62GgJILPjQTKA4Yr3LgORGQ0MBZ4NYLl+qIozyWHXZYcjDH9XyTFSjcDtwBfccNv4NU9HEtXA8+5+yo6EJGbgJsARo0adYxXHZms1ETyM5NZb8nBGDMAhE0Ort7gEVW9Fnigm8uuBAoDhgvcuFCuxktAIanqQ8BDADNnzvStRrgoP8OuHIwxA0Ikd0iPdk1Zu2sJMFFExrrPXw3MC55JRI4DBgPv9GAdvaooP4PS3fW0tlmLJWNM/xZJsVIZ8JaIzAMOd02qqmGvJFS1RURuBeYD7Vcgq0XkbqBEVdsTxdXAU9oH2ohOys/gUHMb5bUNjBma5nc4xhgTNZEkh03uFQdkdGfhqvoi8GLQuDuDhu/qzjL9VORaLK3ftd+SgzGmX4ukziFDVW/rpXhi2sS8dAA27trPRVOG+RyNMcZETyR1Dqf3UiwxLy05gYLBg1i/q97vUIwxJqoiKVZa7uobnuXIOocB2RnfpPwMuxHOGNPvRZIcUoAa4NyAccoA7al1Yn4GizZW0dzaRmK8PUjPGNM/RdIr63/1RiB9xaRh6TS3KluqDzAxv1v188YY02dE0itrkYi8IiIfuOETROT70Q8tNhXlf9hiyRhj+qtIykUeBu4AmgFUdSXevQkD0vjcdOIENliltDGmH4skOaSq6ntB4wbsw5RTEuMZMyTNKqWNMf1aJMmhWkTG41VCIyIfB3ZENaoYZ30sGWP6u0haK92C1+ndcSJSCWwGro1qVDGuKD+dBWt2cqi5lZTEeL/DMcaYYy6S1kplwPkikgbEqeqAP2UuGpZBm8KmqnqmjMjyOxxjjDnmIm6or6oHLDF4JrkWSxutUtoY00/ZXVw9MGZoGonxYs1ZjTH9liWHHkiMj2Pc0HRrsWSM6be6rHMQkStDjK4DVqnq7mMfUt9QNCyD5eV7/A7DGGOiIpLWSjcAHwEWuuHZwFJgrIjcrap/iVJsMa0oL51/rNjOgcYW0pIj2YzGGNN3RFKslABMVtWrVPUqoBjvnodTgO9EM7hY1v7gn427rVLaGNP/RJIcClV1V8DwbjeuFtelxkDU3mLJboYzxvRHkZSHvCYi/8R7ngPAVW5cGrA3apHFuMKcVJIT4qxS2hjTL0Vy5XAL8Gdgmns9Btzi7ns4J9wHRWSOiKwXkVIRub2TeT4pImtEZLWIPNHN+H0THydMzE+35qzGmH4pkjukFXjOvSLmnj/9IHABUAEsEZF5qromYJ6JeD2+nq6qe0Qkrzvr8FtRfgZvl9b4HYYxxhxzkTzP4VQRWSIi9SLSJCKtIrIvgmXPAkpVtUxVm4CngMuD5rkReFBV9wD0taaxRfkZ7Nx3iLqGAVv1YozppyIpVvoNcA2wERgEfAHviqArI4HygOEKNy5QEVAkIm+JyLsiMifUgkTkJhEpEZGSqqqqCFbdOw5XSu+2oiVjTP8S0R3SqloKxKtqq6r+CQh5EO+BBGAi3r0T1wAPi0h2iPU/pKozVXVmbm7uMVr10Wtvzmotlowx/U0krZUaRCQJWC4i9+I9yyGSpFIJFAYMF7hxgSqAxaraDGwWkQ14yWJJBMv33YisFNKTE6zFkjGm34nkIP9ZN9+twAG8A/5VEXxuCTBRRMa65HI1MC9onr/jXTUgIkPxipnKIoo8BohYiyVjTP8USWulre7tIeCHkS5YVVtE5FZgPhAPPKKqq0XkbqBEVee5aReKyBqgFfiWqvap5j9FeRm8vHZX1zMaY0wf0umVg4hMFJE/i8gDIlIgIv92LZZWiMjJkSxcVV9U1SJVHa+qP3bj7nSJAfV8Q1WLVXWqqj51bL5W7ykalkHNgSaq6xv9DsUYY46ZcMVKfwLeBrYDi4FHgKHAbXgtmAwBLZas3sEY04+ESw7prpXQz4GDqvqsqh5S1ZeA5F6KL+YV5acD1mLJGNO/hEsObQHvg296a8MAkJuRTHZqIuvtkaHGmH4kXIX0cSKyEhBgvHuPGx4X9cj6CBGhKD+DjXblYIzpR8Ilh8m9FkUfV5SfzgvLt6OqiIjf4RhjzFHrNDkENGE1XZiUn8H+Qy3s3HeI4VmD/A7HGGOOWkTdZ5jwig4/+MfqHYwx/YMlh2OgPTms2xFJZ7XGGBP7wt0E94r7+7PeC6dvGpyWRMHgQaysqPM7FGOMOSbCVUgPF5HTgLki8hReK6XDVPX9qEbWx0wrzGbZtgH71FRjTD8TLjncCfwArzfVB4KmKXButILqi6YVZvPPlTuo2t9IbobdI2iM6dvCtVZ6DnhORH6gqj/qxZj6pBMLvcdQrCjfy/nF+T5HY4wxR6fLCmlV/ZGIzBWRn7vXR3sjsL7m+BFZxMcJKyqsaMkY0/dF8gzpnwJfBda411dF5CfRDqyvGZQUz6T8DJaXW3IwxvR9kTwJ7lJgmqq2AYjIo8Ay4LvRDKwvOrEwm3+t3E5bmxIXZ3dKG2P6rkjvcwh8rnNWNALpD6YXZrPvUAtbag74HYoxxhyVSK4cfgosE5GFeM1ZzwJuj2pUfVR7pfTy8r2My033ORpjjOm5SCqknwROBZ4H/gZ8RFWfjnZgfdGEvHTSkuJZYfUOxpg+LqJiJVXdoarz3GtnpAsXkTkisl5ESkWkw9WGiFwvIlUisty9vtCd4GNNfJwwtSCL5XantDGmj4ta30oiEg88CFwMFAPXiEhxiFmfVtVp7vWHaMXTW04szGbt9n00trT6HYoxxvRYNDvemwWUqmqZqjYBTwGXR3F9MWFaQTZNrW2s3WEP/zHG9F2R3Ofwl0jGhTASKA8YrnDjgl0lIitF5DkRKewkhptEpERESqqqqiJYtX+mjfrwTmljjOmrIrlymBI44IqLTjpG6/8HMEZVTwBeAh4NNZOqPqSqM1V1Zm5u7jFadXQMy0whLyPZboYzxvRp4brsvkNE9gMniMg+99oP7AZeiGDZlUDglUCBG3eYqtaoaqMb/APHLun4RkQ4sTDbrhyMMX1ap8lBVX+qqhnAfaqa6V4ZqjpEVe+IYNlLgIkiMlZEkoCrgXmBM4jI8IDBucDaHnyHmDOtMJuy6gPUNTT7HYoxxvRIJDfB/VtEzgoeqaqLwn1IVVtE5FZgPhAPPKKqq0XkbqBEVecBXxGRuUALUAtc390vEIumtffQWrGXs4piuxjMGGNCiSQ5fCvgfQpeK6SlRPA8B1V9EXgxaNydAe/vACK5CulTphZkIeJVSltyMMb0RV0mB1W9LHDYtSj6ZdQi6gcyUxIZn5tu3XcbY/qsntznUAFMPtaB9DcnFmSzvHwvqup3KMYY021dXjmIyK/xHgsKXjKZBtjzo7swbVQ2f3u/gsq9BykYnOp3OMYY0y2R1DmUBLxvAZ5U1beiFE+/Ma2g/Wa4OksOxpg+J5Lk8DQwwb0vVdVDUYyn35g0LIOkhDiWl+/h0hOGd/0BY4yJIeFugksQkXvx6hgeBR4DykXkXhFJ7K0A+6qkhDiOH5HJinLrodUY0/eEq5C+D8gBxqrqSao6AxiP91S4n/dGcH3diYXZrKqso6W1ze9QjDGmW8Ilh48CN6rq4e5FVXUfcDNwSbQD6w+mFWZzsLmVDbvq/Q7FGGO6JVxyUA3RDlNVW/mw9ZIJI/BOaWOM6UvCJYc1IvK54JEi8hlgXfRC6j9G5aSSnZponfAZY/qccK2VbgGeF5HP43WXATATGAR8LNqB9QcicvhmOGOM6UvC9cpaqaqnAHcDW9zrblWdpaqVnX3OHGlaYTYbdu3nQGOL36EYY0zEIulb6VXg1V6IpV+aVphNm8IHlXWcMm6I3+EYY0xEovkMaQOcUJAFYEVLxpg+xZJDlA1JT2ZUTqq1WDLG9CmWHHqB99hQu1PaGNOW13K7AAAY30lEQVR3WHLoBScWZFG59yC791u3VMaYvsGSQy+YMXowAO+W1fociTHGRCaqyUFE5ojIehEpFZHbw8x3lYioiMyMZjx+ObEgmyFpSby0ZpffoRhjTESilhxEJB54ELgYKAauEZHiEPNlAF8FFkcrFr/FxwkXFOezcN1uGlta/Q7HGGO6FM0rh1l4z38oU9Um4Cng8hDz/Qj4GdCvC+QvnJJPfWML72yq8TsUY4zpUjSTw0igPGC4wo07TERmAIWq+q9wCxKRm0SkRERKqqqqjn2kveC08UNJS4pn/morWjLGxD7fKqRFJA54APhmV/Oq6kOqOlNVZ+bm5kY/uChISYxn9qQ8Xlqzi7Y269TWGBPbopkcKoHCgOECN65dBnA88JqIbAFOBeb110pp8IqWqusbWVa+x+9QjDEmrGgmhyXARBEZKyJJwNXAvPaJqlqnqkNVdYyqjgHeBeaqakkUY/LVOcflkRgvLLCiJWNMjItaclDVFuBWYD6wFnhGVVeLyN0iMjda641lmSmJnDpuCPNX7yTEc5SMMSZmdNkr69FQ1ReBF4PG3dnJvLOjGUusuGjKML7/9w/YuLueovwMv8MxxpiQ7A7pXnZBcT4AC1bv9DkSY4zpnCWHXpafmcL0UdnWpNUYE9MsOfjgwuJhrKqsY/veg36HYowxIVly8MFFU6xoyRgT2yw5+GBcbjoT8tJZYB3xGWNilCUHn1w0JZ/Fm2vZc6DJ71CMMaYDSw4+ubB4GK1tyqvrdvsdijHGdGDJwSdTR2YxLDOF+VbvYIyJQZYcfBIXJ1w4JZ9FG6s42GTPeDDGxBZLDj66sHgYh5rbWLSxb3ZDbozpvyw5+OiUcTlkpiRYR3zGmJhjycFHifFxnDc5n1fW7aKltc3vcIwx5jBLDj67aEo+exuaeW9Lrd+hGGPMYZYcfHZWUS7JCXFWtGSMiSmWHHyWmpTAmRNzWbB6J632+FBjTIyw5BADrpoxku11h/jJi2v9DsUYYwBLDjHh4qnDuf60Mfzxzc08sXib3+EYY4wlh1jx/Usnc3ZRLne+8AFvl1b7HY4xZoCLanIQkTkisl5ESkXk9hDTvyQiq0RkuYi8KSLF0YwnliXEx/HrT09n7NA0vvT4Usqq6v0OyRgzgEUtOYhIPPAgcDFQDFwT4uD/hKpOVdVpwL3AA9GKpy/ITEnkketPJiE+jhseLWFvg/XYaozxRzSvHGYBpapapqpNwFPA5YEzqOq+gME0YMA31ynMSeV/P3sSlXsOcvPj79NsN8cZY3wQzeQwEigPGK5w444gIreIyCa8K4evhFqQiNwkIiUiUlJV1f/7ITp5TA73XDWVd8pquPOFD1Ad8DnTGNPLfK+QVtUHVXU88B3g+53M85CqzlTVmbm5ub0boE+unFHA/5s9niffK+ePb272OxxjzAATzeRQCRQGDBe4cZ15CrgiivH0ObddOIk5U4bx4xfX8spau4PaGNN7opkclgATRWSsiCQBVwPzAmcQkYkBg5cCG6MYT58TFyc88KkTmTIik689tZzy2ga/QzLGDBBRSw6q2gLcCswH1gLPqOpqEblbROa62W4VkdUishz4BnBdtOLpq1KTEvjdtSehwDeeWW69txpjeoX0tcrOmTNnaklJid9h9Lr/W1bB159ewTcvKOLL503s+gPGGBNARJaq6sxI5/e9QtpE5oppI5l74gh++cpGlm3b43c4xph+zpJDHyEi/OiK4xmWmcLXnl5OfWOL3yEZY/oxSw59SNagRH7xqWmU1zbww3mr/Q7HGNOPWXLoY2aNzeGWcybw7NIK/rVyh9/hGGP6KUsOfdBXzpvIiYXZ3PH8SrbvPeh3OMaYfijB7wBM9yXGx/GrT03j0v95g68/vZwnbjyV+DjxOywTZc2tbVTtb2T3/kZq6hs5oSCb3IzkHi9PVak72My22obDr3L3NyEujutPH8PsolxEjt2+9XZpNU8tKef0CUO4akYBCfF2fhqrrClrH/ZsSTnfem4l35lzHDfPHu93OL5pammjfE8D22oa2FpzgC013gFua80B0pIT+Nr5EzlnUt4xPchFk6ry9+WVvF1aw+79jezad4iq/Y3UNjQR+HMdlBjPjWeO5cazxpGRkhjRsusbW3jqvW38fXklW2sa2H/oyIYNQ9OTKMxJZWfdIXbUHaJ4eCY3zx7PJVOHH9UJyJrt+7jnP+tYtKGKQYnxHGxuZWJeOt+ecxznT47sf7Opqp7H393Khl37mToym5NGD2bGqGyGpPc8QfamvQ1NtCnkpCX5sv7uNmW15NCHqSq3PrGM+at38sSNpzJrbE6vrr+1TXn+/Qp272/kM6eOJmtQZAeoSKkqu/c3srPOOzhW1Td6f9tf9Y3uIHaQwMdvpybFM3pIGqNzUlm3cx9bahr4yLghfPeSyUwtyDqmMR5rFXsauOP5VbyxsZqh6UkMzxpEfmYyuRkp5GUkk5/p/U1PSeCvi7fxjxXbyUlL4svnTuDTp4wiOSE+5HJr6ht59O0tPPrOVuoONjNjVDZTR2ZRmJPKqJxURg1JpXBwKmnJXmFCU0sbLyyv5Pevb2JT1QHGDEnli2eP58oZIztdRyjltQ088NIG/r68ksyURG49ZwKf/choFq7bzX3z11NWfYCZowdz+8XHMXNMx/23pbWNl9fu5vF3t/JmaTWJ8cKEvAxKd++nudX7p48ZksqM0YM5yb1G56QRmGtEQJDD79tUaWuDVlVa25S2NqVVvb8K5KYnE3cMr8S31TTw0BubeLakAlW49tRR3HLOBIb2clKz5DDA1DU0c/GvFrG97hAzRmXziZmFfPSE4RGfSfbU25uq+dE/17J2h9fr+uDURL5xQRHXzBrV46KChqYWVlbUsWzbXpZt28Oy8r1U7W/sMN/g1ERyM5LJzUgmLyOFwpxUxgxJZfSQVEblpDE0PenwmWhzaxtPLN7Gr17ZSO2BJi6fNoLbLpxEYU5qz798Jw41t1KyZQ9vllbz9qZqttU2cOX0Am44cywjsweF/Wxbm/LX97Zxj3uO+O2XTObaWaO6PEitqqjjnv+s5a3SGgpzBvHNCyYx98QRhz9XufcgDy8q46kl2zjU3MaFxfl8afZ4ZowaHNF3amtTFqzZyW9f28TKijryM5P5whnjmDttBDlpSSR28r/ec6CJBxeW8tg7WxGB/zp9LDfPHn/ECURzaxvPlJTzy5c3UrW/kQuK8/nOnElMyMugan8jTy/ZxhOLt7G97hDDs1K49pRRfOrkUeRmJHOouZUPKutYunUPS7fu4f1te6iuPzbPPxmcmsissTmcMnYIp4zLYfKwzB4liw8q6/j965t4cdUOEuLiuHLGSFTh2aXlDEqM54YzxvKFs8aR2cVvta1NWVa+lwVrdnLZCSM4fmTPTnAsOQxANfWNPLe0gmeXVlC6u56UxDguOX44H59ZwKljhxzTs6At1Qf4yYtrWbBmFyOzB3HHJccxZkga//2vNbxbVsvEvHS+d+lkZk/K63JZu/cf4o0N1Szdtodl2/ayfue+w1cAY4emMb0wmxMKsigYnEpeppcMhqQlk5TQ/eSz71Azv39tE398czOqcP3pY7hl9gSyUnueRFvblA8q6w4ngyVb9tDU0kZCnDBj1GCGpCfx0ppdKHDZCcO56azxFI/I7LCcbTUNfPtvK3i3rJYzJw7lp1dOpWBw5MlLVXljYzX3/Hsda3bso3h4Jl88exyvb6hi3vLtAFwxfSRfPGscE/MzevRdVZW3Smv47WulvL2p5vD4jOQEstMSyUlNIjs1iZy0JFIS4/nnyu0caGzh4ycV8PULihie1XlybGhq4ZE3N/P718toaGph1tgclm7dQ3OrcsaEoXzm1NGcPzkv7EmHqrKttoGlW/ewa18j6h4NE3h4U1VUvT7L4kSIj4M4ERLihPg4IS5OaG1TVpTXsXhzDRV7vMYemSkJh5PFjNGDGZaVQk5qEoOSOl5BqSrvbKrhd69v4o2N1aQnJ3DtqaP4/Oljyc9MAbzisQcWbOBfq3aQnZrI/5s9ns99ZAwpiR8u71BzK+9sqmHBmp28tGY31fWNJMR59zpdM2tUZP+0IJYcBjBVZXn5Xp4pqeCfK7azv7GFwpxBXDWjgKtPHsWwrJQeL3vfoWZ+82opf3prM4nxcdxyzgRuOGPs4R1aVVmwZhc/eXEtW2samD0pl+9fOpkJeR8ejJpb23h/6x5e21DF6+urWOOuOjKSE5g2KpvphdlMHzWYaYXZDI5SueyOuoPcv2ADf3u/gsyURD5z6ihmT8pjWmF2p2fBgRqaWli0oZoFq3fyyrrd1B1sBuC4YRmcPmEoZ0wYyqyxOYeLZyr3HuSRNzfz5HvbaGhq5cyJQ/nS2eM5bfwQVOHRd7Zw73/WkxAnfP+jk/nkzMIe1420tSn/WLmd++avp2LPQQYlxnP1rEK+cOa4Lq9cumNlxV5WVNSx50ATexqa3N9m731DE3sbmjll7BC+PWcSRd1IRrUHmvjNq6W8tHYn5x2Xz2dOHc2EvPRjFnd3Ve49yOKyGt7bXMvizbVsrj5wxPSUxDhyUpMYnOYlxezUJLbVHGBFRR1D05P5/BljuPaUzotbV1XUcd+C9SzaUMWwzBS+fN4E0pMTWLB6F6+t382BplbSkuKZfVweFxbnM3tS3lEV3VpyMAAcbGpl/uqdPLu0nLdKa0iMFy47cQQ3njmOycM7nr12prGlleeWVvDAgg3UNjTxiZMKuO3CSeRlhk40jS2tPPb2Vv7n1Y00NLVy7SmjOG5YJq9v2M1bpTXUN7YQHyecNHowZxflcnZRLsXDe3bZfjTWbN/HffPX8fqGKtrUS1CnTRjCWUW5nDUx94hip70NTby8djfzV+/kjY1VHGpuI2tQIudNzuPsolxOGz+0y1ZDdQ3NPL54K396awvV9Y0cPzKT5IR4lm7dwzmTcvnJlVPDnl13R2OLd9Z5QkG2b5Wf/dGufYdYWVFHTX3j4WRYe6DpwyTZ0ExyQhyf/chorppRcMSVQDjvltVw73/W8f62vQDkZiRz/uR8LpySz2njh3SrjiccSw6mg201DTzy1maeKSk/fPb6hTPHcdbEoSHPUmvqG1m4voqX1+zijY1VHGhqZdaYHO68rDji8s6a+kZ+8fIGnli8jTaFEVkpnD0pl7OL8jhtwpAuy1l7S93BZt4urWbRxioWbaim0t03MnZoGqeNH8Lm6gMs3lxLa5syPCuFC4vzuWjKME4emxPRlUawQ82t/N+ySh5eVEZtQxM/uLSYK2eM7DMtqUx0qCqLN9eSGB/H9MLsqJwsWXIwndrb0MRfF2/j0be3sHt/I8cNy+CGM8Yyd9oIttU08PLa3by8dhfvb9uDKuRnJnPe5HzmTBnGmZ0kkq6U1zZwqLmVCXnpMX8AVFU2VR3gjY1VLNpQxbtltYwcPIiLpngJYerIrGP2Hdp/d7G+TUz/YcnBdKmxpZV/rNjBw4vKWL9rP8kJcTS2eM+JOH5kJucdl8/5k/M5fmTmgD54qeqA/v6mf+lucrA7pAeg5IR4Pn5SAVfNGMmijdX854Odh5PC0VRa9zeWGMxAZslhABORw5XCxhgTyDo2McYY00FUk4OIzBGR9SJSKiK3h5j+DRFZIyIrReQVERkdzXiMMcZEJmrJQUTigQeBi4Fi4BoRKQ6abRkwU1VPAJ4D7o1WPMYYYyIXzSuHWUCpqpapahPwFHB54AyqulBVG9zgu0BBFOMxxhgToWgmh5FAecBwhRvXmRuAf4eaICI3iUiJiJRUVVUdwxCNMcaEEhMV0iLyGWAmcF+o6ar6kKrOVNWZubnWssYYY6Itmk1ZK4HCgOECN+4IInI+8D3gbFXt2D+zMcaYXhfNK4clwEQRGSsiScDVwLzAGURkOvC/wFxV3R3FWIwxxnRDVLvPEJFLgF8C8cAjqvpjEbkbKFHVeSLyMjAV2OE+sk1V53axzCpgayeThwLVxyb6qIjl+Cy2nrHYesZi65mjiW20qkZcLt/n+lYKR0RKutN3SG+L5fgstp6x2HrGYuuZ3owtJiqkjTHGxBZLDsYYYzrob8nhIb8D6EIsx2ex9YzF1jMWW8/0Wmz9qs7BGGPMsdHfrhyMMcYcA5YcjDHGdNBvkkNX3YP7SUS2iMgqEVkuIr4+41REHhGR3SLyQcC4HBF5SUQ2ur+DYyi2u0Sk0m275e7eGT9iKxSRha6L+dUi8lU33vdtFyY237ediKSIyHsissLF9kM3fqyILHa/16fdjbKxEtufRWRzwHab1tuxBcQYLyLLROSfbrj3tpuq9vkX3k12m4BxQBKwAij2O66A+LYAQ/2Ow8VyFjAD+CBg3L3A7e797cDPYii2u4DbYmC7DQdmuPcZwAa8ruh933ZhYvN92wECpLv3icBi4FTgGeBqN/73wM0xFNufgY/7vc+5uL4BPAH80w332nbrL1cOXXYPbjyqugioDRp9OfCoe/8ocEWvBuV0EltMUNUdqvq+e78fWIvXy7Dv2y5MbL5TT70bTHQvBc7Fe4YL+LfdOostJohIAXAp8Ac3LPTidusvyaG73YP3NgUWiMhSEbnJ72BCyFfV9i5MdgL5fgYTwq3uaYGP+FXkFUhExgDT8c40Y2rbBcUGMbDtXNHIcmA38BLeVf5eVW1xs/j2ew2OTVXbt9uP3Xb7hYgk+xEbXtdD3wba3PAQenG79ZfkEOvOUNUZeE/Fu0VEzvI7oM6od70aM2dPwO+A8cA0vD647vczGBFJB/4GfE1V9wVO83vbhYgtJradqraq6jS8nplnAcf5EUcowbGJyPHAHXgxngzkAN/p7bhE5KPAblVd2tvrbtdfkkNE3YP7RVUr3d/dwP/h/UBiyS4RGQ7g/sZMD7mqusv9gNuAh/Fx24lIIt7B96+q+rwbHRPbLlRssbTtXDx7gYXAR4BsEWl/ZIDvv9eA2Oa4YjpV7xECf8Kf7XY6MFdEtuAVk58L/Ipe3G79JTl02T24X0QkTUQy2t8DFwIfhP9Ur5sHXOfeXwe84GMsR2g/8Dofw6dt58p7/wisVdUHAib5vu06iy0Wtp2I5IpItns/CLgAr05kIfBxN5tf2y1UbOsCkr3glen3+nZT1TtUtUBVx+Adz15V1Wvpze3md238sXoBl+C10tgEfM/veALiGofXemoFsNrv2IAn8YoYmvHKLG/AK8t8BdgIvAzkxFBsfwFWASvxDsTDfYrtDLwio5XAcve6JBa2XZjYfN92wAnAMhfDB8Cdbvw44D2gFHgWSI6h2F512+0D4HFciya/XsBsPmyt1GvbzbrPMMYY00F/KVYyxhhzDFlyMMYY04ElB2OMMR1YcjDGGNOBJQdjjDEdWHIwfYaIqIjcHzB8m4jcdYyW/WcR+XjXcx71ej4hImtFZGHAuKkBPYDWBvQI+nK04zGmM5YcTF/SCFwpIkP9DiRQwB2rkbgBuFFVz2kfoaqrVHWaet04zAO+5YbPP4r1GHNULDmYvqQF7xm6Xw+eEHzmLyL17u9sEXldRF4QkTIRuUdErnX9+K8SkfEBizlfREpEZIPr26a9Y7b7RGSJ64jtiwHLfUNE5gFrQsRzjVv+ByLyMzfuTrwb1v4oIvdF8oVF5HwRec3157/KjbvOxb9cRH4rInFu/MUi8o6IvO/6+k9z4+8T71kPK9tjMaYrdiZi+poHgZUicm83PnMiMBmvO/Ay4A+qOku8h+J8Gfiam28MXj8644GFIjIB+BxQp6onu9453xKRBW7+GcDxqro5cGUiMgL4GXASsAevR94rVPVuETkX7xkL3Xno00y855Nscx3DfQw4TVVbROQh4GpXBHU7cJ6qNojI94Cvisgf8e6WnqKq2t5dhDFdseRg+hRV3ScijwFfAQ5G+LEl6rrVFpFNQPvBfRVwTsB8z6jXSd1GESnD65nzQuCEgKuSLGAi0AS8F5wYnJOB11S1yq3zr3gPMvp7hPEGe0dVt7n357vll3hd/zAIr7v6BrwH/LztxicBb+IlxDbgYRH5F/DPHsZgBhhLDqYv+iXwPl6Pme1acMWkrpgl8PGJjQHv2wKG2zjyNxDcl4ziPS3sy6o6P3CCiMwGDvQs/G4LXI8Aj6jqD4Li+RjwH1X9bPCHRWQmXqdynwBuxkt4xoRldQ6mz1HVWrzHJd4QMHoLXjEOwFy8p3p11ydEJM7VQ4wD1gPzgZtdl9iISFF7WX4Y7wFni8hQEYkHrgFe70E8obwMfLK9Ul5EhojIKOBtt85xbnyaiEx0PQJnquo/8epqph+jOEw/Z1cOpq+6H7g1YPhh4AURWQH8h56d1W/DO7BnAl9S1UMi8ge8uoj3XRfOVXTxaEZV3SEit+N1ryzAv1T1mHStrKqrROSHwMvuCqnZxbpERG4AAh86/128orfnXX1JHN4ziY3pkvXKaowxpgMrVjLGGNOBJQdjjDEdWHIwxhjTgSUHY4wxHVhyMMYY04ElB2OMMR1YcjDGGNPB/wePkgAESvMoRQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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" ] @@ -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": [ - "" + "" ] }, - "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", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -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 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"-----------------------------------------------------\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", + "RMSE Training = 0.0757375853624068\n", + "RMSE Testing = 0.07580806645083134\n", + "Out of Bag error = 0.5348983654969031\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07586964482710655\n", + "RMSE Testing = 0.07588828912269077\n", + "Out of Bag error = 0.536980725478543\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 111\n", "Number of features = 1\n", - "RMSE Training = 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0.07607366884690017\n", + "RMSE Testing = 0.07608311567515999\n", + "Out of Bag error = 0.5404068798209425\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 114\n", "Number of features = 1\n", - "RMSE Training = 0.0754482445066051\n", - "RMSE Testing = 0.07551892942489594\n", - "Out of Bag error = 0.5299009420491501\n", + "RMSE Training = 0.07512905935339827\n", + "RMSE Testing = 0.07517794933352481\n", + "Out of Bag error = 0.5257644553813962\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 115\n", "Number of features = 1\n", - "RMSE Training = 0.07554038784803362\n", - "RMSE Testing = 0.0755593654294237\n", - "Out of Bag error = 0.5309736270596809\n", + "RMSE Training = 0.07537234318975647\n", + "RMSE Testing = 0.07536332357317743\n", + "Out of Bag error = 0.529501089125716\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07526474514930911\n", + "RMSE Testing = 0.07524271049667965\n", + "Out of Bag error = 0.5278110915568349\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07538493501360466\n", + "RMSE Testing = 0.07539771076843224\n", + "Out of Bag error = 0.5291991182730158\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees 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"-----------------------------------------------------\n", "Number of trees = 127\n", "Number of features = 1\n", - "RMSE Training = 0.07557269734826384\n", - "RMSE Testing = 0.0757184736665953\n", - "Out of Bag error = 0.5317686558970021\n", + "RMSE Training = 0.0759361834449723\n", + "RMSE Testing = 0.07597975219703389\n", + "Out of Bag error = 0.5379259244867596\n", "-----------------------------------------------------\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", + "RMSE Training = 0.07548559853515478\n", + "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", - "RMSE Training = 0.07550948962180926\n", - "RMSE Testing = 0.07559273464939514\n", - "Out of Bag error = 0.5316955245913748\n", + "RMSE Training = 0.07552737500984824\n", + "RMSE Testing = 0.0755984913246935\n", + "Out of Bag error = 0.5309799842514641\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 130\n", "Number of features = 1\n", - "RMSE Training = 0.07553364369768654\n", - "RMSE Testing = 0.07559320200937929\n", - "Out of Bag error = 0.5301141746652079\n", + "RMSE Training = 0.07584948798506856\n", + "RMSE Testing = 0.07590110138221565\n", + "Out of Bag error = 0.5359730329270009\n", "-----------------------------------------------------\n", "-----------------------------------------------------\n", "Number of trees = 131\n", "Number of features = 1\n", - "RMSE Training = 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"-----------------------------------------------------\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 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"-----------------------------------------------------\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 = 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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": { - "image/png": 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\n", 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yl6wThHgL4d4Xt/OfT212j0XUQlAUJYvJOkGItxBe3NLEs5sa3WMRTTtVFCWLyTpBiF/+eiASpS9u5dNInMvIGDMh7VMURZkoMhIEEblURLaISJ2I3J7ieFBEHrGPrxSRGrv8ehFZF/cvJiIn2seuFZENIrJeRJ4UkfIjeWPpCPg8RGOGSDRGfzhG72C8IFhCETND1oKiKEq2MKogiIgXuBu4DFgEXCsii5Kq3Qi0GWPmAncBdwIYY35ljDnRGHMi8DFgpzFmnYj4gB8A5xtjjgfWA7ccqZsaCXcbzWiM/kiUvnhBiBMBjSMoipJtZGIhLAPqjDE7jDGDwMPA8qQ6y4EH7NePAReKiCTVudY+F0DsfyG7XiFw4BDaP2YCtiAMhGMMhGP0DEZc91C8IKSbvKYoivJuJRNBqAb2xr3fZ5elrGOMiQAdQFlSnY8Av7brhIG/AzZgCcEi4P4xtv2QCPq8gGUhWLGCIWsgHDc/QVNPFUXJNsYlqCwipwK9xpi37Pd+LEE4CZiG5TL6YppzbxKR1SKyuqmp6bDbEm8h9NvZRE4cIRrvMtJMI0VRsoxMBGE/MCPu/XS7LGUdOz5QBLTEHb8G2zqwORHAGLPdWP6aR4EzUn24MeY+Y8xSY8zSioqKDJo7Mk4MYSASpd/OMOoZiABDE9Os4yoIiqJkF5kIwipgnojUikgAq3NfkVRnBXCD/foq4Dm7o0dEPMDVDMUPwBKQRSLi9PAXAZsO7RbGhmMh9A5G3ZiBk3rqLF0B6jJSFCX78I1WwRgTEZFbgKcAL/BTY8xGEfkasNoYswLL//+giNQBrVii4XAOsNcYsyPumgdE5KvASyISBnYDnzhSNzUSjoXQ1R9xyxyXUUQtBEVRsphRBQHAGPME8ERS2R1xr/uBD6c59wXgtBTl9wL3jqGtRwQnqNzRF3bLegdtl5HGEBRFyWKybqay4zLq7I8ThIEj4zL6ycs7eGJD/WG2UFEUZWLIOkFwXEad8RaCHUMIRw0ee/bEobiMfvTSDh5dvXf0ioqiKJOQ7BWEOAuhz3YZRaIxQkHLizZWC6E/HKWpa4C23vDolRVFUSYhWSgIw2MIPbbLKBIzhAK2ICTFEKKjrG20r60PgPbewSPWVkVRlPEk6wTBjSH0DWUZOWmnkaghLzg0k9khFjOcdedz/Nvv30q7Cuq+tl4A2npUEBRFeWeSdYKQymXkZBlFYjHyg8MthO7BCPUd/Tzw2m6+9/TWlNd1LITO/oi7aqqiKMo7iawThECqoPJgCpdRXAyh256zUJLn57+fr3MFJJ69toUA0N6ncQRFUd55ZJ0gOBZCwjyEgSGXUY7fg0cSs4ycSWwnzSzBGGjsHBh2XcdCAI0jKIryziTrBMHntTr8TruTD/g8btppJGbweT0Efd4EQegesMRjdnkIgMau1ILg91o5q5pppCjKO5GsEwSwMo0cl1FpXiAh7dTvFYJ+DwNxW2s6FsKcynwAGrv6h11zX2svC6cUAlZg+bE1+1jx5rhs8aAoinJEyEpBCPg8rgVQEgokpJ16PR6CccdhSBBcCyHJZdQ7GKGlZ5DF04sAaO8Nc88LdTzw6q6jfSsuo6XFKoqijEZWCoITRwAoDfnjXEYx/B5J4TKyBGFGaR5+rwxzGe234weLqy1BaO4ZYF9rHy3dw11LR4PtTd0s/H9/oq6xa1w+T1GUdyfZKQh+67Z9HqEg6I9zGRl8XrEthOFZRgU5Piryg8NcRk6G0fyqfAJeD5vruxiMxmgZpzkJ2xq6CEcNu1t6R6+sKIqShqwUhIDXuu0cv5e8gNd1GYWjtsvI70mYh9A1EEEEQgEfFYU5NCVZCM1dVsdfWZBDcZ6fN/e1W+f1R8ZlXwVHeJz0WUVRlEMhKwXBWb4i6POQF/QmbJDj9w53GXX1h8kP+PB4hMqC4LAYguNSKsjxUZIXSBipt/Uc/Yyjlm5HEIbPj5is/H7dftbtbZ/oZiiKEkdWCoIzOc2yEHxDM5WjBp/HQ8DrYTA+htAfIT/HmrBWWWC5jN7a38FnH1pLJBpzt+DMC/goCfkTPqt5hDhC32B0xOOZ4sQq3kkWwlf/8Db//VzdRDdDUZQ4slIQnKBy0O8h1++lPxwjGjOEYzErhuBPiiEMRChwBSGHtt4wD762m8fX19PUPUDPYJSA10PA56EkL5DwWa1JcYQ9Lb1s2NcBwF3PbOXqe1877Ptpfoe5jGIxQ3vvIJvqOye6KYqixJGVguBaCD4rhgDWAnfRmMHnkZRpp84aR5WFQQCe3HgQsKyHnoEIIXtRvGJbEKqLcwFo6Um0AO58cjOff/gNAHa39LC7tZfYYaaMDlkI7wyXUfdghJiB/e19CTPGFUWZWLJSENwYgt9Dnt3R9w5GCEcdQUiKIQxEyM+xXEGVBZYgOB1Z14AlCHn2GkgleVa9k2YWA0P+fYcDHX1u2mpbT5hozCQstJcpsZjhrztaEj7DCY5PdjriZnJvVitBUSYNWSoIcRaC3xIHJ7XUWroicaZyd384wWUUT3d/hJ7BIQvCcRkdV12EzyPDUk+bugboHrCyj1rtNY8OJT31mU0NXHPfX1m/r911S/W9Q1xG8VaBuo0UZfKQ3YLg97guo05XEIT8HF9Cp9XVH6EgyWXk0D0QoWcg6rqMSkKWINSU5VEaCtAaZyEYY1zroLVn0N07ITnOkAlbDlqT0DbXd7nC0hseH0FYuaOFF7Y0HvL57b3xgqCT6RRlspCVghCfZZTrCIItAD6PMLUoh57BKF22Kyc+qFwWCiACC6cUWMf6I3QPRNytN+dX5RPweTh2WhFl+cGEGEJnX8TNXmruGqTNsRC6xy4I25u6AVizuw1nz57egfGJIXzv6a1864lNh3x+e58zbyPIpoNqISjKZCErBcHNMvJ53I7c8eP7PB6mFFkB4YMd/URjht7BKPlBKzbg83q45fy5fOE98wErhtA7GHH3UTh+ejFvf/USZpTmURYKJLiDmrqHZjjvbOnBiSUfioWwo7kHgFW7W92y8coyauwaoL5j+AJ/meJYCKfPKWPLwa5JtaFQW88g+9v7Rq+oKO9CslIQEiwEv2MhWKNrv1eYUmjFCeo7+t3YgjMPAeC2ixdw0aIqwMkyirrCApZoAJTlBxJG//ET2uoau93XrT1jm4tgjGG7ff6OJksYgj5P2iyj/nCUHz67jaXfeJpnNzWM6bNS0djZT5dtGR0KjjvulJpSBiKxhL0kJpqvP/42N/z09YluhqJMCFkpCE6WUY7f63bkTifl9XiYWmQJwsHOfrrsvRAK4gTBqifk+r10D4Rtl5F32OeUhgIJo/+muElo2+MEYaxB5cYua+5DfpwIzSjNS2sh3PX0Vr779Faauwd58zBnB3cPROixP+dgCishHI3xqV+sZqWdAZWK9t5BcvweNzW3bYI3FHr7QCdv7GkDYFdzDzuausdlyZHx4A9vHuCHz26b6GYo7xAyEgQRuVREtohInYjcnuJ4UEQesY+vFJEau/x6EVkX9y8mIifaxwIicp+IbBWRzSJy5ZG8sZEIJLiMrI7cEQSfV9zA8cGO/qFlKYK+YdfJz/HR7biMUhwvzw/SPRCh3w72prMQxhpDcOIH5y6ocMtmlOSmFYT1+zo4cUYxZaEATYcQr4insXNIBFIJwob9HTz9dgN3Prk57TXae8MU5wYoslN0j9SWo69ub+azD60d87yOr/xhI1/87QbAuqeY4V2zUOCjq/dy74vbMUaXR1dGZ1RBEBEvcDdwGbAIuFZEFiVVuxFoM8bMBe4C7gQwxvzKGHOiMeZE4GPATmPMOvucLwONxpj59nVfPBI3lAlDM5W9ru/fEQRnLaOyUID6jn53L4T8nOEdfkHQR0v3IOGoSRitO5TaGUeOldDUPUDQ56E8P8BOOwZQURAccwxhu+0muugYy23lEZhWnJvWZbSrpYfZ5SHK84OHvVRGQ5yo1XcMd/Ws3mXFNNbuaXdfJ9PeF6Y4z09xriUInYcgCFsOdnHJXS/x4tYmt+x/XtjO4+vrU+5oNxI7mnrY09pLNGZosM91XHFjIRyN8er25jGfNxqNnf3uoOLh1/fw+s7UzzUVe1p76RmMalxEyYhMLIRlQJ0xZocxZhB4GFieVGc58ID9+jHgQhGRpDrX2uc6fBL4dwBjTMwYc+T/ktIQn3aa6/ciAh125ovXYx2rKsyhobM/bulr/7Dr5Of4aLBHzE76ajxltiA4FkBjZz+VhUFKQwEG7UDqvMr8jF1GK3e0sOybz/CHdQfIC3g5fU4ZYAlPftDnunLi6RuMUt/RT015iPKCQMIeDQ+t3MMnfvb6mEbU8Ut/p7IQVu1qo7o4l+I8Pz96aUfKa3T0hinK9VNkC0L7GLcc3XKwi2vue40tDV28tr3FbdcrddZPaE9r5qP7zv4wzd0D9A5G2XKwy91oaEdz9yhnDueJDfVc9+OV7G4Zu5ikIxYzXP5fL/PD57ZhjOFrf3ybH7+c+rkmE4nG3L06tjYc3fTeWMzQ2NnvruuVTfx+3f4jshdJfUcfa3ZnLvZHg0wEoRrYG/d+n12Wso4xJgJ0AGVJdT4C/BpARIrtsq+LyFoR+Y2IVI2x7YdMwIkh+Lx4PEKe3ztkIXgsHZtalGNZCPYPPJUFkB/0uSPmVC6jsnxbEOygcWPXABX5QddyCPo8TC/JzTio/NqOFhq7Bnh9VyuzK0JUFgTJD/ooCwXJC/gYjMSG7Zy2u9XqnGrLQ5SFgjTb4vTzV3bypd9t4IUtTXSPYckLx+2V4/dQ35koCMYY1uxu4/Q5ZVxzykye3dSQchZ2e98gxXljEwRjjOv2+MnLO4hEDRUFQfbanf8f3qx3s7b2jkEQdjUPdd6r4iyaQ7EQDrT3J/zvsK+tl/d878Uxtcthf3sfzd2DbDzQSVOXJVzx8aeRqO/oJ2I/lC0Hxy5wsZhhc4ZpwV/9w0aWfetZjvvKUzx/GHNUxsJP/7KTT/1i9bh8Vjr2tvbyhUfW8YNnD3+hxn/7/UY+8dNVE+reG5egsoicCvQaY96yi3zAdOBVY8wS4DXgO2nOvUlEVovI6qamplRVxkz84nZgdeZDMQSrbEpRDgc7+ty5CMlBZbAEwQkUpxKMspAVi3AshKauASoLctzy0lCA0pDlMsrkR7CtoZuqwiDzq/I5paYUEWF+VT5Ti3NcCyXZbeR0eLVxLqP+cJRvPL7JjYukG9Wt39fuxiscGjr7yfV7qS3PpyHJQtjR3ENrzyBLZ5VwzvxyYgZWpXBvdPRZMQSf10NB0OfOSxiJf3psPTf8bBVgLf8xtyqfY6YWutbA79ftZ0FVASJjsxB2xgnC67YgFOf5E8ozxXHHJbvlXqlrpq6xm9WHMPpzYk07m3vcNu1u7U1YjTcd8QK07RAshCc3HuSyH7yckbvp7fpOZleEEOCN3W1j/qyx0tUf5q5ntvL02w3D9icZT36zei/G/p0fTkfePRDhha1NdA1EJtS9l4kg7AdmxL2fbpelrCMiPqAIiE8zuQbbOrBpAXqB39rvfwMsSfXhxpj7jDFLjTFLKyoqUlUZM/GL20GSINgWwpRCa1VTpzNPKQg5PndEnsplVJqfGENo7BqgomDIQijJC1AWChCOGtp6wzy+vp6vrNiY1ke8rbGLxdXFPPn5c7jjfVYY5wfXnMS3PriYvKAjCIluI2e+guMyclwjkZjh1NmWEee4xeKpa+ziw/e+xhU//IvrinHuobIwyDTbgorHiRksrSllycwSAj6Pu95SPO29VgwBoDDXn3aBu+e3NPKTl3fw5Fv1PLZmH2t2WX909R39TC3KYWZpLntae2nrGWT9vg6uOHEaUwpz3B3sMiHeEnDE6/TZZexoGvuI2hGC5A7q7QPWKDtdoPrFrU08+NfdKY85rp69rb1stcUhGjPsst1SXf1hfvDMtpTLljjCOLcyny2HIAg7m3swBtctGk/PQIRbH13nTlDc29rHSTNKmFGa5/7mjiYPrdzjxvcmys0SjRkeXb2PgNfDwc7+w0qffn5zoyvy2xrG/ts7UmQiCKuAeSJSKyIBrM59RVKdFcAN9uurgOeMLZci4gGuJi5+YB/7A3CeXXQh8PYh3sOYCcbNQwAIBb2j4l3qAAAgAElEQVTugms+ry0IdurppvpOPII7XyGe+MyjVBZCQdBHwOuhuWeAgUiUjr4wlXGCYFkI1utbH13HZx9aywOv7eKjP1nJU/Zqqg7haIydzT3Mr8rH4xGcEM2M0jymFefGWQiJHcOu5h7K8y3XUnm+ZZk4KZaLptqzreMshD5bML7wyDryAl6ml+TxsftX8r4fvswTG+pp6OynqiDHsqCSOop1ezsoyvUzpyJEjt/LSTOKeS1JEPrDUQYiMTfDqDjPn7DYXTz3vbiDbzy+iU//ci0APYNR2nvDHOzoZ0phLjNL8+joC7Nyp/UZx08vYkZp3phcMzube5heYsU8GrusoP+SmSW09YbdpUUcjDF8989beGt/R8prOUKQbCG8ba/XtCeFILywpZEbf76Kr6zYmNJS22p3DjEDL24ZspAdy+HZTY3c9cxWHlm1Z9i5e1p78XuFc+ZVUNfYPcydmMwf1x9IEEInRpT8/XT0hrn6R6/x27X7+e3a/QxEojR09TOjNJfa8lBG1tXmg53c+ui6ES2d1p5Bbn5w9TCBHYzEuP8vO1lWW0rQ52HVrqNvkaTipa1NHOzs5zPnzwFg5RiC/Q794ShNXQM8+dZB14V6tOM9IzGqINgxgVuAp4BNwKPGmI0i8jURucKudj9QJiJ1wK1AfGrqOcBeY0xyJOxfgK+IyHqsDKTbDu9WMicQF1QGa2MbJ1bg8wy5jMAym0+tLWN4jDwx8yhVDEFE3PWMnB91ZWHQjS2UhAKuFfHCliY+eFI1q7/8Ho6ZVsjnH37DzSwBq2MPRw3zqwpS3pOz2qrTqRhjiERj7GrupbY8D4AKRxDsuQiLphUCiYJw5f+8yiXff4m39nfy7SuP59GbT+eW8+fSMxDljt+/RX1HPxWFQaYW5dDaM5jQxv3tfcwszXOf1elzyth4oDPBAnDiBcW51n0X5/nTpp129IWpLQ9x8qwSPnOe9Ue36WAnvYNR20Kw7uupjdZku2OmFjKjJI+9ralHag2d/bQnzXnY1dJDbXmIGSXWtaYV5zK7IgQwbKTb2R/hh8/V8cf19W7Zql2tXPfjvzIQiaZ0GcVixl2vKd6V1TMQ4fvPbOXmB9dQmOsnGjO8sWf4HJFtjV3uCrqv1DUzrSgHkSFBcCyFn7+6i72tvXxlxUb3Hne39lJdnMvCqQUMRGI8/fZB1u9LPQ9lT0svn/v1G/zoxaE/U0fwk116P3h2G5vqOzl/QQXN3QNsPdiNMTC9JM8VhNHcJ3/acJDfrt3vbjebihe3NvLUxoZhmVtbG7po7Brgo6fN4oQZxaze1craPW18+0+bOTCO7pZfv76H8vwAf3feHIpy/a6FaYxJO8hJ5o7fv8Up33yGJ96q5/0nTKWyIOgOAiaCjGIIxpgnjDHzjTFzjDHftMvuMMassF/3G2M+bIyZa4xZFt/5G2NeMMacluKau40x5xhjjjfGXGiMGT7EOUo47h9nVJ8f9LnrATkWgjM5LeD18K0PLU55HWc5C8BNX02mLN9avsIJPlcW5AxZCHl+NxMJ4FNnz6YsP8gnz6yhPxxL9AHbHcDcyvyUnxO/rwPA1/74Nhd890W2NHRRU2Z1cI6FsHZPG3kBLzNLrXJHRGIxw7bGLi5fPIUn/v5sLjl2CkV5fm69eAH/cukCmrsH2dPaa1sI1qSyeHfCgfY+phUPrQZ72uwyjCHBBeZ0Lo7LqDg3MKyTdujoC3PSjGL+9+/O4PLFUwFYY48GpxTlMMMWhGc2NVBVGKQ8P8jM0jwauvoThMrh+p+s5F//7y33vTGGnU1WSu70Eut+phTmMK/SEt3nNifO6nbSbDviOshHV+3l1e0t7GzucQP2zXFzPfa29dI9ECHo87C7tZe+wSiffnANJ339ab7/zDbec0wVv/vMGXhkKIbhEIsZ6hq7udBOL+4LR1k4tZDq4twhQbBFa1dLL+/9r5f5+au7eHS1lQOyt7WXGaV5LLAHEZ/+5Vquuve1lBbUL1fuxhioi7MQnO82Pui/o6mbX7y2i4+cMpNrls0E4KVtluUyvSSX2eUhegejCenJqXCELD6QPxCJJixjsnF/asvKsUDmVeZzSk0Jbx3o5O9+uYZ7X9zOef/5Al9ZsTFhvszh8qMXt/Ohe15JKGvs6ufZzY1cefJ0gj4vp9SUuPfy/JZGln7z6YziNn/Z1szCKQVcvKiKG06vYX5VQYKF0NI9QHgcl3bJypnKS2aW8OOPL+WUmlIg0f/vxBCqi/Mozw/ypcuPobY8lPI6+XGzk1PNVAbLLdTSM+iOXKYV5w7FEOJcRqfWlroj9ll2B74r7g9ha0MXHhlJEBIthDf3trOntZeOvjA1dvsdy2RvqzWSdwSx295Hoa3XmlOxrGaoLQ7nLah061fZFgIMZdQYY6hv72OqLRRg7QlRmOPje09vda2QIQshPoaQOqjd2Rd2XUvOrOZVdsByapwgdPVHWDTVau+M0lyMvfkOWHGNva29HOzop66x2/Xng9Vxdw1ELAvBvtbUohxmluXxwZOquffFHazdM+SOqG9P7CCNMfzFjq/saOoZmm8S5+Jwlvc+d36F5RrYWM+TGw9y5ZLp/PYzZ3D39UuYVRbimKmFw+Zt7G/vo3cwysmzSlwxrykLMbcyP85C6GVZbSnTinKIxgwzSnNdC2ZPay8zS/M4dlohnzyzli9ffgwegW8nTRrsG4zyyCpLROoau93RveMyiheEb/9pM0Gfh1svms+cCuu36MwFmV6SS225VTZa2q4jZKt2ttLcPcAtD63lhK/+mc8+tNats9GJvSQJmHNuTVmIpTWlRGOG5u5B7v3oyXxoSTUP/nU3l//Xy25cxRjD3c/X8Zdth5bZ/uzmRtbuaU9Is/7fNfuJxgwfWWqFV0+pKXWTKtbv6yAcNfxmzT63/vee3so9LyRmIh1o7+NARz8fOWUGP/rYUuZVFTCvyvpuYzHD+n3tnPHt57jiv18Zt2Xis1IQRISLFlXhsTv/eP+/384yyg14ef1LF3LDGTVprzOaywisUXlL94A7upxWnJhlNKUwh4sXVfGPlyxwz6kpszqn+Hz2bQ3dzCzNc+MeybgWgv1HsKe1j9Nnl7GstpRz5lnBeEcQwIo9OO3vtjOpnFFdVWHing9gxVsuPtYaqVYWBplbmY/XIzz9tjWK7uy3lrRwOm6wlgj5r2tPYmtDF194eB3GGLdzSYgh9A3PsopEY3QNRFy/anGen7yA181gmVKUQ2GO37U0HAFz3Eh77WDz9T9ZyZd+t8GNM8Rn6Dj+8tqK/CELwRa6ry4/limFOVx972uc/R/P8dcdLW4Q3VlqY3tTt1vmxGVEEl1Gbx/oxOsR3mOvffXoqn3k+r18bfmxLJlZ4tY7paaUN/a0J4wGt9m57fOr8plti3pteR5zK/LZ0Wx1GrtbephTkc8v//ZU/vj3Z/Ox02axfl8Hb+3voL03zMzSPHxeD3e8fxGfOmc2N50zh8fX1/O3D6zmP57cbAdG99LRF+aSY6vo6AvT0jNIOBpzM+gcl99r21v489sNfOb8uVQUWNaY1yOs3d2G12OtAVZru9tGiiMYY9zjq3e38d0/b+WpjQc5fnoxT21s4K87WjDGsPGAFatJZSFMK8ohN+Dl5Fkl5Ad93HL+XC49bgrfvvJ47rl+Cc3dg+6I/ccv7+A/n9rCT1/ZmbZNDm09g3zy56v43zX7MMYQixl3EOEMDoyxntmy2lJm26J4jD0g2drQ5d7bb9fuJxKNsaOpm/9+bhv3PL89wXJdY/+Wl84qdcsWVBXQF47yxt42bn5wDaWhAE1dAyy/+5VxcYdlpSAkkxfn7vF6hmIFHs/wuEE8jsvIa2+7mQpnPaMD7f0UBH0U5PiZXRHimlNmcN78SnxeD/fFWStgbcNZmONLyErZ2tDF3MrU8QMYcln1DkbpHYzQ3D3AWfPKefTm01k8vQiwOuhCWwRmlua5Vo0zoa3BnnRWmUIQAK5aMh2A2vJ8qgpz+OBJ1fxq5W4aO/vdH+vU4sRzz1tQyecumMszmxqo7+h33S3OVqPFuX7CUTMsGO7sT+EIgohQXZxL10AEkaGNihwBWDTVuscZcYLw6Oq9DERivLq9hT9tsIL0UbsTdZ4pWB2uIwiO5VOY4+fBG5fxqXNm09AxwPObG11Rd0TtZXvE6fcKa23/f21ZiJbuIYF7Y287cypCbuzntR0tLK0pcQceDqfUlNIXjroB6z9tqOebj2/C6xHmVha4VmpNuWUh9IdjbNjfQVtvmNryPGZX5FNbHnJdazfZ+fkLpiT+Zj597mwuWlTFrpYe7nlhO7c9uo5//9MmzphTxnWnzgKsdbaaugZcN2p77yCxmOEbj7/NtKIcbjyrFrBicTNL84jEDFOLcvB5PUwtzCHH72FHUw/Pb25MGShv6w3T2R9hcXURXf0RHl61h6tOns4vPrmMqUU5/PufNrOvrY/O/ggBr8edS+Owo7nHFZ7CHD8rv3QhX3jPPPf42fPK8XuFV+qaWbmjhW//aTNej7h7iIzEo6v38tzmRm77zZv882Pr2dPa61q3a+0OfFdLLzube7jihGnuefOqLGHY1tjNzuYeQgEvzd0DvLi1ibuf307MWLG6+Jn1a3a3kev3snBqQdx1rNfX3reStt5BfnLDUv78D+fwjQ8cx7S4wdbRQgWBRNeP3zuyCCSeZ3WuoYA3ZdAZrFF572CU7U3d7hfq93r49pXHM9O2BFJRUx5y/az94Sg7mns4Zmp6QciNm4fgBFWdzjGecnsL0JmleQR9XgJej5u+5/hdq5I2AXI4Y245L//z+Zw4w5pX+LkL5hKJGe55YXucBTT8R3vufMtCWb+vnS0Hu8nxe9wAt9PhO6PQHU3d7Gvrdd87xwG30y7PD7qJAc49OhZCRX6QUMDLb9/Yzy9e282M0lyiMcOTGw+69+XMrdh8sIvCHB9TCnM4dloRxXl+TphR7H7e7Ip8/uXShdSWhxKsAadtf9nWzKyyPBZOKWTDPqsjP2ZqIYPRGJ19EZ7f3MjL25q59LipzIr7Lk6bnTxnE06ptayFl7Y2s7e1l888tBaPCHdft4SiXL/rKqwtD7npwr+0U1UdF6P1jPJYOquEpu4B/vW9x7jP3iEv4OPHH1/KM7eey0dPm8n/rTtAKODj+x850f2MuqbuhAyy9r4wr25vYeOBTv7xkgUJVqpjuTjfjccj1JSFePj1PfzNz1fxvae3MhCJsvzuV/i/N6xsdWcEffVSa4AhwM3nzCHH7+XWi+bz5t52vvG4lXR49rxyGjqtuTN9g1GMMexo6k5w44aCvoS/v7yAjyUzS/hLXTP//XwdFQVB/u7cOexvH5pXtKOpm0u//1LC8ivGGB5ZtZeTZ5Vw/akzeWztPncjqOI8v2shOAs3xn+PUwpzyA/62NbQxc6mHq44sZry/AC3PPQGv3tjHx8/fRYleX7+uL6eR1ft5Ucvbuf1na2cOKM4YXCwYEoBBTk+jq0u5HefOZNjpxVRGgpw9dL4zP+jR2o/R5aRF4y3EDLXyOTgdCqcoPFb+zsSOpvRmFUWclcmfbu+k2jMcFx1Udr6obh5CE42y8xUgpAfZEdTj3ssFPS6ozjHZVRRkFoQIFFkZpWFeO/iqfzfuv3MsUds04qGC8IxUwvxe4U393Wwerf1R+B06I7Lp613kKc2HuSbj2/ijLnl3HqRtd9EvCBUJ43iAY6vLmLNrja3w/V4hG99aDH/9Jv1DEZj3HP9Eu56eivbGru5eukMfvhcnet/33KwiwVTChARqgpzWHfHxSnveU5liE31XUwrtqwYx0J4fVcr7zt+Kp19ETbYI/uFUwp4fEM925u7uf2361lQVcBnz59DwJ6E1zUQSSkIlQU5nDGnjEdX7yUSs9xGP//kMtcF95FlM5hRmsd0OxtqVlkev3/zAICbNOBwz/VL6AtHE4QiFf/2/mMJBX1cvKiKysIcYjFDrt/L9sYeSm0Lrjw/SHtv2B2cnDGnPOnZ5PPs5kY3SwtgdkWIzQe7CAW8/H7dAY6ZWsibe9v52Ss7+cBJ1a4gnDm3nNkVIU6YXuzGua5cMp1fv76HpzY24PUIFx9bxbObG3l+cyOff3gd3/zgcXT2R9xYRTrOmlvOd5/eCsA/XbLADaxvbejm5FklrNndxuaDXTy7qZGPnmZZRq/vbGVHcw/fOX8ui6uL+NXKPdzzwnb8XuEDJ1bz0Mo9DESivL6zlfL8gPubB8uCnVuZz8odrXQNRJhflc/HTjuVR1fvZXtTN7ecP5dw1PDIqj38wf7ewBpUxZMf9LHySxe6qyiMN2ohkOj/943hS3CEIG9EQbA617be8JhMvlmleexv7yMcjbHR7mwWjyAIziS7njhBmFEy/PPK7TiC07E7K7aClWZYGgq4y4NnwlnzymnvDfPytmZ8HkkpJjl+LwunFLqjzHj3WJGdfrrizQN89Q9v4xFJayFUF1ttnhLn0vrbs2fz/D+el/DHs/zEan5902nccv5cLl5Uxftt0/68BZVMK8pxA6dbGrqGuVRSMacinz2tva4Lry8cpaV7gK7+CLPKQgkiudD2Jd//8k4aOgf49ysXE/RZFuTMsjxy/V6On576e7zu1Jnsb+/jRy/t4Ky55QnxmMIcP5ceN8V9f/6CSjcWkiz8lYU5o4oBWJbqFy87hpNtH7bHI8ypDCVYCAunFNDRF6a+oy/l9ztkIQy14bpls/jMeXP47tUn0Nw9wNf/aI3239zXwc7mHnY19+D1CDNK81hxy1nceeXx7rmOoPs8kuBq+8Gz2xiMxtxVdGenSfRwOHOeJVxBn4drl810v2fHbeRkUMXPk3nwr7spyPHx3sVT3bhNY9cA8yoLOG12KYPRGG/t72TlzlaW1ZYO8wrMi5sAWFseYtG0Qr5yxbE8eOOpVBbm8KEl1RjghtNn8Z0Pn8CM0lwuOXYKyeQFfBMiBqAWAmC5fBySfbsj4QRl0wWUYWi2MpDwBz4as8ryiMYM+9v62LC/g9JQIGFknIzH3p+hbzBCZ1+YUMDrZjDF42SrOCZ+KDAkCI2d/VSOYB2kwuncX9jSRFVhTkIMJp7jp1sjLrBmMjs4FsKKdQfI8Xu4csl0Vqw74KaiOsdhyEKIF1avR1x3WTwnzyrh5FmWG+aTZ9VSXZzLkpnFzKnMZ3tTj7uS7YI08zrimVORTzRm2NfWR17Aa832tv/wpxTmuKKV6/e6nfOf3z5IbXmIk+KswuUnTqO1J5z2N3bxoimU5wdo7h4c1UVw3oIKfv7qLqYU5qS8/0NlTkU+q3e1ccyUAgI+DzXleWw80EF9e3/K73eO7WaaHjf4OGteOWfNK2cwEqMkz09bb5i/PauW+1/ZyR/ePMDOFmsyoN/rSfksFk4p5M4rjycv4HWtn80Hu/B6xE3pTZf553B8dRHl+UEuPa6K0lCA4lw/oYCXLfbaTI7776/brQD2hv0d/HF9PZ85b477PC89bgr3vLCd46oLWTKrBBFrb5H97X3cdM7sYZ8ZnwE4O4UFc0pNKWv/9SKK8/yICFedPH3Ee5gI1EIgsUNP16GlYmgeQ/o/yPLQUAc7UoeejGNC72rpYcP+To6rLkobp3AIBb30DEbd/PNU9a9eOoM73rfI9QMX5PjcpSsaOgfcLJuM21mWR3m+tXrrSIJ3wnSrY/QILJk51Ek6nWl9Rz+n1JRSXWIFjp0RXGGChZCYCZQp+UEfV548HRFhTkU+25u63UXbFkwpHOVs3IlqVn3b9WCPNCsLg64IVBQE3RF0OGq4aFFVwndw0zlzuP2yhWk/J+DzcMPpNUwtynF35EvHabPLCPo8zBohDnUozK3IZ3+7NQipKgxSkhegoy/M/va+lM99ycwSvnT5Qi45bvhIN+Dz8IGTqgkFvHzugnmcUlPKo6v3sn5f+zA3VzJXnjydyxZPpTjP77pmv3DhPHweweeRBAFKhc/r4c//cA53vO9YwBowzZ9S4Aq5k0La0jPI1oZuvvHHTe4kMwcnQH/89GIqC3K47aL5bprxstpSknECy36vuIOXZEpCgVH/jicSFQQSJ5WNJagc9HnweyUhSymZeAthrC4jsDJhtjV0sbh69I4rN+Clz3YZpYofABxXXcQn7SwRsMSwZ9ARBGtZirEgIu5IPDnDKB4nfrJwSmHCUuLxFsDpc8rc7CFnPZd4l9H8qnzmVIQ4pWYoXXOszK3Mp3cwyhN21lEmFoKTWghD6YVb7PZVFQ7NmC7Pt0aizqBitE49FbdcMJeX/vn8tOnFDjl+L1+8bCF/c2bNmD9jJC5bPJVcv5dXt7e41k/MWNkzqQY0Xo9w0zlz0sbR/uXShTxz27kU5fn59Lmzae4eYG9rX0JmzUiICLPK8vB5hI+dPosrTpjGsdVF7iKUI1EaCrixKrC+6y0HuzDGcLCzn/l2B/7Zh9by+q5W/uGi+Qm/zeOqi3j05tP5sB38/uz5c7nxrFrmVuan/N04ExpnlYXGNLCcTKjLCNyF4YCMfmgOIkJ+0DdiUDkU8BL0eRiIjDyCTsZZBO+eF7YTiRmOm5Y+fjD0WZb7Z09r77DsknTkB33saeklEo3R3D2QNsNoJE6pKeWpjQ0jCt7cynxKQwHOnJsYUM31W5lOg9EYZ8wpd7NAtjVa2Ujx8YyCHD/P3nbemNsXz5lzywkFvDy2Zp/V4cUJUjryg1Ym0sHO/oR8c7AEIcfnwecRyvODeDxCWShAJGYS5hlkiohkPCj5xJm1o1caI3Mr8/mPq47nc79+gylFuW56cGvP4CGlPeb4ve5kxQsWVrH+3y5ha0NXgtU1GstPqObMOQMU5wX49pXHEzvEVUUXTCng4VV7aeoa4GBHPxcfO4XewSh1jd3cfO5srj1l5rBz4i0BEeH/vW8R/2pMylF+dXEuOX7PqO6syYwKAolZQmMJKgN84oxajp2WfvQuYnUQ9Z39KSd8jXTejz++lFseWks7YXcuwUjkBrxsa+hiIBIbMaU1nvygJSItPYPETPo5CCPhxARG6jC8HuGJvz87wSIA6z4Lc/0MhKMcN63Q3Q2urrE7wTo4UtSWh/jdZ8/k5gfXcNLMzLO+5lSGONjZ7y4IuNXOonF+OxcfW8Uy+zmcM7+CaUXp4ymTnfefMI2YMcypyE+YnTvlEH4byQR8nhGz5VLxqTh/fSDNfJ9McMT8zX0dtPQMMrUoh/+46nj6BqPu8iCZkM7l4/EIX7r8GOZWjJwBNZlRQSD10hWZ8vm4CTHpKMsPEomZMf+YT55VwhN/fzbbGrsTsjjSkR/08caedoI+T8rUxnTndMf57MciWg4nTC/iWx9czHuPnzpivXS+/+qSXKbZE5ucoHb3QGRMFtVYmF9VwHO3ncsoi38mMK+ygFU729zJgV0DkYRMl3uuP9l9/Z0Pn3DE2jpRLD/R2gMrfmbttBFcgu8EHEFwNvCZUpgzLI32cPn46TVH9HrjjQoCiRbC0RjVza4IDRsZZ0pJKJAygJWKz5w3l3PnV/CBk6rdbKLRCAV99NrbbEL6SWkjISJcd+pwcztTfvLxpQnzEhwX0tGwEBxEhDGEi/j0uXO4YGElhTk+/F4hHDVUHsKzeqcR/7udmmKOyTuJolw/00tyeW6TLQhjTE7IBlQQGFq6whe3z8CR5NsfOnS/51g4fU6Zu89ypjgZHM7s3UOxEA6X+Nx2ESvXfX97X0KG0UQzpSjH7UCKcgN2vOXd36E480RgbFlyk5VFUwv5s73+1rvhfo40mmWE5ZcMeD3u0tdHmtyAd8S5ChOJ064397YTCnjHPA/haOCMvI+mhXA4OKPm7BAE6179XsnY6pzMxK/iW6WCMIzJ2UtNAHlBL9HoxG1uPVE47rI39rQzt6pgUuRIO6I0aQXBbtdkEM+jTcDnIRTwUhIKTNjs2SOJs0x6KOBN2PFQsVALwSYU8OE9ShbCZMYRBGuK/uTIjnDmIkxaQcgiCwGslWlTrVH1TuRYO8NpSlHOpBj8TDZUIm1CQS8DI+zv+m4lfk+HySMI1sj7UAPxRxvHr54tgnDO/IpRZwa/U5hWZE2204ByalQQbEJBn7sMdDYRP0vbmXo/0Uz2GEKJayG8+11GAP+eZgvZdyIiwucvnJc1Yj5WVBBsQgHfO3Yi0eFQkGAhZLacwNHGmRw3WQVhmj0jVTuVdybxS7coiagg2ISC3jGtdPpuwckyyvV7j9pEsLFyam0pnzq7NuP5F+PNdafO5IKFlaOuN6Qo7zRUEGyuXjpjXPYsnWw4G+vMrcyfNFkkeQEfX37vooluRlpy/F53NVpFeTehgmAzlrVM3k0422hOlviBoigThwqCwu2XLXSXsFYUJXtRQVA0yKYoCqAT0xRFURSbjARBRC4VkS0iUicit6c4HhSRR+zjK0Wkxi6/XkTWxf2LiciJSeeuEJG3jsTNKIqiKIfOqIIgIl7gbuAyYBFwrYgkp4DcCLQZY+YCdwF3AhhjfmWMOdEYcyLwMWCnMWZd3LU/BHQfkTtRFEVRDotMLIRlQJ0xZocxZhB4GFieVGc58ID9+jHgQhm+UMi19rkAiEg+cCvwjUNpuKIoinJkyUQQqoG9ce/32WUp6xhjIkAHkLww/0eAX8e9/zrwXaB3pA8XkZtEZLWIrG5qasqguYqiKMqhMC5BZRE5Feg1xrxlvz8RmGOM+d1o5xpj7jPGLDXGLK2oyGzjeEVRFGXsZCII+4EZce+n22Up64iIDygCWuKOX0OidXA6sFREdgF/AeaLyAtjabiiKIpyZMlEEFYB80SkVkQCWJ37iqQ6K4Ab7NdXAc8ZY+0ZKSIe4Gri4gfGmP8xxkwzxtQAZwFbjTHnHc6NKIqiKIfHqBPTjDEREbkFeArwAj81xmwUka8Bq40xK4D7gQdFpA5oxRINh3OAvcaYHYfb2DVr1jSLyO4xnlYONB/uZx8lJmvbtN+NVmYAAASzSURBVF1jY7K2CyZv27RdY+Nw2zUrk0pixmHz94lERFYbY5ZOdDtSMVnbpu0aG5O1XTB526btGhvj1S6dqawoiqIAKgiKoiiKTTYIwn0T3YARmKxt03aNjcnaLpi8bdN2jY1xade7PoagKIqiZEY2WAiKoihKBryrBWG0VVrHsR0zROR5EXlbRDaKyOft8q+IyP641WAvn4C27RKRDfbnr7bLSkXkaRHZZv8/7rvniMiCpJVyO0XkCxPxzETkpyLSGL8qb7pnJBb/Zf/m1ovIknFu13+KyGb7s38nIsV2eY2I9MU9t3vHuV1pvzcR+aL9vLaIyCVHq10jtO2RuHbtEpF1dvl4PrN0fcT4/s6MMe/Kf1hzJrYDs4EA8CawaILaMhVYYr8uALZirRz7FeAfJ/g57QLKk8r+A7jdfn07cOck+C4PYuVSj/szw5pLswR4a7RnBFwO/AkQ4DRg5Ti362LAZ7++M65dNfH1JuB5pfze7L+DN4EgUGv/zXrHs21Jx78L3DEBzyxdHzGuv7N3s4WQySqt44Ixpt4Ys9Z+3QVsYvgCgZOJ+NVrHwA+MIFtAbgQ2G6MGeukxCOCMeYlrAmX8aR7RsuBXxiLvwLFIjJ1vNpljPmzsRaYBPgr1lIz40qa55WO5cDDxpgBY8xOoA7rb3fc2yYigrWqwq9THT+ajNBHjOvv7N0sCJms0jruiLV50EnASrvoFtvk++lEuGYAA/xZRNaIyE12WZUxpt5+fRComoB2xZO8FtZEPzNI/4wm0+/uk1ijSIdaEXlDRF4UkbMnoD2pvrfJ9LzOBhqMMdviysb9mSX1EeP6O3s3C8KkQ6w9IP4X+IIxphP4H2AOcCJQj2WujjdnGWOWYG2A9FkROSf+oLHs0wlLRRNr/awrgN/YRZPhmSUw0c8oFSLyZSAC/MouqgdmGmNOwtqH5CERKRzHJk267y0F15I48Bj3Z5aij3AZj9/Zu1kQMlmlddwQET/WF/0rY8xvAYwxDcaYqDEmBvyYo2gqp8MYs9/+vxH4nd2GBsf8tP9vHO92xXEZsNYY0wCT45nZpHtGE/67E5FPAO8Drrc7EWyXTIv9eg2Wr37+eLVphO9twp8XuKs0fwh4xCkb72eWqo9gnH9n72ZByGSV1nHB9k3eD2wyxnwvrjze5/dBYFz3lhaRkIgUOK+xApJvkbh67Q3A78ezXUkkjNom+pnFke4ZrQA+bmeBnAZ0xJn8Rx0RuRT4Z+AKY0xvXHmFWNvhIiKzgXnAYS84OYZ2pfveVgDXiLUve63drtfHq11xvAfYbIzZ5xSM5zNL10cw3r+z8YigT9Q/rEj8Vixl//IEtuMsLFNvPbDO/nc58CCwwS5fAUwd53bNxsrweBPY6DwjrN3ungW2Ac8ApRP03EJY+2oUxZWN+zPDEqR6IIzlq70x3TPCyvq42/7NbQCWjnO76rB8y87v7F677pX2d7wOWAu8f5zblfZ7A75sP68twGXj/V3a5T8HPp1UdzyfWbo+Ylx/ZzpTWVEURQHe3S4jRVEUZQyoICiKoiiACoKiKIpio4KgKIqiACoIiqIoio0KgqIoigKoICiKoig2KgiKoigKAP8ftO5VMR2duu4AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", + " warn(\"Some inputs do not have OOB scores. \"\n", + "/usr/local/lib/python3.7/site-packages/sklearn/ensemble/forest.py:732: UserWarning: Some inputs do not have OOB scores. 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", + "RMSE Testing = 0.06456473457442968\n", + "Out of Bag error = 0.39183296399022904\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 50\n", + "Number of features = 2\n", + "RMSE Training = 0.06441503531691949\n", + "RMSE Testing = 0.06436636827788714\n", + "Out of Bag error = 0.3893996216123007\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 51\n", + "Number of features = 2\n", + "RMSE Training = 0.0643273190304706\n", + "RMSE Testing = 0.0644045442754941\n", + "Out of Bag error = 0.3893973581512269\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 52\n", + "Number of features = 2\n", + "RMSE Training = 0.06418164934883493\n", + "RMSE Testing = 0.06413615779350715\n", + "Out of Bag error = 0.38746794327433104\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 53\n", + "Number of features = 2\n", + "RMSE Training = 0.06367778569268107\n", + "RMSE Testing = 0.06364495191866217\n", + "Out of Bag error = 0.3818174870522696\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 54\n", + "Number of features = 2\n", + "RMSE Training = 0.06533298937455637\n", + "RMSE Testing = 0.06533066105527283\n", + "Out of Bag error = 0.39983808068434007\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 55\n", + "Number of features = 2\n", + "RMSE Training = 0.06438247917570199\n", + "RMSE Testing = 0.064355355964522\n", + "Out of Bag error = 0.3887033418194874\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 56\n", + "Number of features = 2\n", + "RMSE Training = 0.06410645271181761\n", + "RMSE Testing = 0.06426318450615075\n", + "Out of Bag error = 0.3855921056330131\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 57\n", + "Number of features = 2\n", + "RMSE Training = 0.06453617522858829\n", + "RMSE Testing = 0.0644724551242025\n", + "Out of Bag error = 0.38930618036209363\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 58\n", + "Number of features = 2\n", + "RMSE Training = 0.0634625524962257\n", + "RMSE Testing = 0.06342363435360593\n", + "Out of Bag error = 0.3773736569472715\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 59\n", + "Number of features = 2\n", + "RMSE Training = 0.06426463100483955\n", + "RMSE Testing = 0.0642356992476131\n", + "Out of Bag error = 0.3876862654206306\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 60\n", + "Number of features = 2\n", + "RMSE Training = 0.06455727198267165\n", + "RMSE Testing = 0.06444169490595966\n", + "Out of Bag error = 0.3911470165165679\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 61\n", + "Number of features = 2\n", + "RMSE Training = 0.06459132335999826\n", + "RMSE Testing = 0.06475664563415937\n", + "Out of Bag error = 0.39036393022959026\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 62\n", + "Number of features = 2\n", + "RMSE Training = 0.06491177799817827\n", + "RMSE Testing = 0.0648871669769185\n", + "Out of Bag error = 0.3966749894209358\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 63\n", + "Number of features = 2\n", + "RMSE Training = 0.06367126765243955\n", + "RMSE Testing = 0.06368275859166747\n", + "Out of Bag error = 0.37924951560287234\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number 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"-----------------------------------------------------\n", + "Number of trees = 112\n", + "Number of features = 2\n", + "RMSE Training = 0.06486363168520962\n", + "RMSE Testing = 0.06486562429626827\n", + "Out of Bag error = 0.3926679273127708\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 113\n", + "Number of features = 2\n", + "RMSE Training = 0.06399900624884422\n", + "RMSE Testing = 0.06411268002115804\n", + "Out of Bag error = 0.38285580578149486\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 114\n", + "Number of features = 2\n", + "RMSE Training = 0.06430292117044759\n", + "RMSE Testing = 0.06427985512657255\n", + "Out of Bag error = 0.38486498881188036\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + 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"-----------------------------------------------------\n", + "Number of trees = 134\n", + "Number of features = 2\n", + "RMSE Training = 0.06384921938632128\n", + "RMSE Testing = 0.0638912407817604\n", + "Out of Bag error = 0.3805196522103797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 135\n", + "Number of features = 2\n", + "RMSE Training = 0.06409429443624184\n", + "RMSE Testing = 0.06406410105553005\n", + "Out of Bag error = 0.3822737005789877\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 136\n", + "Number of features = 2\n", + "RMSE Training = 0.06439371171077762\n", + "RMSE Testing = 0.06434349958693271\n", + "Out of Bag error = 0.3869298511291811\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number 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+ "Out of Bag error = 0.3852895759898314\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 144\n", + "Number of features = 2\n", + "RMSE Training = 0.06458353000793413\n", + "RMSE Testing = 0.06459322712478753\n", + "Out of Bag error = 0.38823183067052797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 145\n", + "Number of features = 2\n", + "RMSE Training = 0.06392471544380866\n", + "RMSE Testing = 0.06386559603217362\n", + "Out of Bag error = 0.38111828308537643\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 146\n", + "Number of features = 2\n", + "RMSE Training = 0.06402480079528483\n", + "RMSE Testing = 0.06397877847943659\n", + "Out of Bag error = 0.38246040605297854\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 147\n", + "Number of features = 2\n", + "RMSE Training = 0.06428734811447165\n", + "RMSE Testing = 0.06417502097258877\n", + "Out of Bag error = 0.3846169238410732\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 148\n", + "Number of features = 2\n", + "RMSE Training = 0.0640831613142483\n", + "RMSE Testing = 0.06408902433145172\n", + "Out of Bag error = 0.38307806424588725\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 149\n", + "Number of features = 2\n", + "RMSE Training = 0.06438675047679246\n", + "RMSE Testing = 0.06437996891736887\n", + "Out of Bag error = 0.38625022001618287\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 150\n", + "Number of features = 2\n", + "RMSE Training = 0.06466495185083813\n", + "RMSE Testing = 0.06458186105771743\n", + "Out of Bag error = 0.3900315671675289\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 151\n", + "Number of features = 2\n", + "RMSE Training = 0.06408909769345869\n", + "RMSE Testing = 0.06415880492321423\n", + "Out of Bag error = 0.38258032095408784\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 152\n", + "Number of features = 2\n", + "RMSE Training = 0.06428062103867507\n", + "RMSE Testing = 0.0642519397432695\n", + "Out of Bag error = 0.3846244385770786\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 153\n", + "Number of features = 2\n", + "RMSE Training = 0.06476460814871772\n", + "RMSE Testing = 0.06476823149548443\n", + "Out of Bag error = 0.3904214035964054\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 154\n", + "Number of features = 2\n", + "RMSE Training = 0.06462555582635872\n", + "RMSE Testing = 0.06464370831445848\n", + "Out of Bag error = 0.38920285353084927\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 155\n", + "Number of features = 2\n", + "RMSE Training = 0.06403334127496044\n", + "RMSE Testing = 0.06401501624526283\n", + "Out of Bag error = 0.381919243045597\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 156\n", + "Number of features = 2\n", + "RMSE Training = 0.0644963523414425\n", + "RMSE Testing = 0.06448132681378893\n", + "Out of Bag error = 0.38730887156438093\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 157\n", + "Number of features = 2\n", + "RMSE Training = 0.0644553044640783\n", + "RMSE Testing = 0.06436391269395689\n", + "Out of Bag error = 0.38649244380973335\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 158\n", + "Number of features = 2\n", + "RMSE Training = 0.06433498473838903\n", + "RMSE Testing = 0.06428802847530271\n", + "Out of Bag error = 0.3855213128929499\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 159\n", + "Number of features = 2\n", + "RMSE Training = 0.06414228391991286\n", + "RMSE Testing = 0.0641017223703412\n", + "Out of Bag error = 0.38301474885229564\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 160\n", + "Number of features = 2\n", + "RMSE Training = 0.06433327388643913\n", + "RMSE Testing = 0.06420783368028563\n", + "Out of Bag error = 0.3845123150831411\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 161\n", + "Number of features = 2\n", + "RMSE Training = 0.06438854587490626\n", + "RMSE Testing = 0.06437996912964492\n", + "Out of Bag error = 0.3853847840142298\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 162\n", + "Number of features = 2\n", + "RMSE Training = 0.06433923583066072\n", + "RMSE Testing = 0.06432979357156862\n", + "Out of Bag error = 0.3858736948618581\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 163\n", + "Number of features = 2\n", + "RMSE Training = 0.0641547569897708\n", + "RMSE Testing = 0.06425775184980534\n", + "Out of Bag error = 0.38306619667020525\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 164\n", + "Number of features = 2\n", + "RMSE Training = 0.06415581508017151\n", + "RMSE Testing = 0.06405474133556661\n", + "Out of Bag error = 0.3838765295211535\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 165\n", + "Number of features = 2\n", + "RMSE Training = 0.06421243923952676\n", + "RMSE Testing = 0.06422612533228282\n", + "Out of Bag error = 0.38392451736147254\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 166\n", + "Number of features = 2\n", + "RMSE Training = 0.06419289969734537\n", + "RMSE Testing = 0.06407954450182596\n", + "Out of Bag error = 0.38535035378939664\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 167\n", + "Number of features = 2\n", + "RMSE Training = 0.06429778572399039\n", + "RMSE Testing = 0.06426887923695337\n", + "Out of Bag error = 0.38533838254670155\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 168\n", + "Number of features = 2\n", + "RMSE Training = 0.06414709769559186\n", + "RMSE Testing = 0.0641249403960428\n", + "Out of Bag error = 0.3830802164220441\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 169\n", + "Number of features = 2\n", + "RMSE Training = 0.06418370564813783\n", + "RMSE Testing = 0.064183459936363\n", + "Out of Bag error = 0.38402174180504967\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 170\n", + "Number of features = 2\n", + "RMSE Training = 0.06456403753480912\n", + "RMSE Testing = 0.06453864658173886\n", + "Out of Bag error = 0.38873946698366507\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 171\n", + "Number of features = 2\n", + "RMSE Training = 0.0638569608530293\n", + "RMSE Testing = 0.06386744407655576\n", + "Out of Bag error = 0.3799667530746048\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 172\n", + "Number of features = 2\n", + "RMSE Training = 0.06444063338828118\n", + "RMSE Testing = 0.06445979403687273\n", + "Out of Bag error = 0.38694167671759067\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 173\n", + "Number of features = 2\n", + "RMSE Training = 0.06455939647967776\n", + "RMSE Testing = 0.0646154957416996\n", + "Out of Bag error = 0.387787560571093\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 174\n", + "Number of features = 2\n", + "RMSE Training = 0.06431206491065486\n", + "RMSE Testing = 0.06430621567966564\n", + "Out of Bag error = 0.385256513057972\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 175\n", + "Number of features = 2\n", + "RMSE Training = 0.06439260157074725\n", + "RMSE Testing = 0.06436040828035487\n", + "Out of Bag error = 0.3857688083959487\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 176\n", + "Number of features = 2\n", + "RMSE Training = 0.06434027439141007\n", + "RMSE Testing = 0.06436240901785296\n", + "Out of Bag error = 0.3857402734566432\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 177\n", + "Number of features = 2\n", + "RMSE Training = 0.06407955959699554\n", + "RMSE Testing = 0.06406473384493479\n", + "Out of Bag error = 0.38257948035347134\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 178\n", + "Number of features = 2\n", + "RMSE Training = 0.06417060165607787\n", + "RMSE Testing = 0.0641238317381633\n", + "Out of Bag error = 0.38347087507535665\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 179\n", + "Number of features = 2\n", + "RMSE Training = 0.06432444549101898\n", + "RMSE Testing = 0.06429893193568312\n", + "Out of Bag error = 0.38608611770620804\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 180\n", + "Number of features = 2\n", + "RMSE Training = 0.06449843240563236\n", + "RMSE Testing = 0.06449035733124815\n", + "Out of Bag error = 0.38718811426117067\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 181\n", + "Number of features = 2\n", + "RMSE Training = 0.06475094710129217\n", + "RMSE Testing = 0.06472139121333498\n", + "Out of Bag error = 0.39083721868046634\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 182\n", + "Number of features = 2\n", + "RMSE Training = 0.06413498886254927\n", + "RMSE Testing = 0.06413250514189639\n", + "Out of Bag error = 0.3833016080284549\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 183\n", + "Number of features = 2\n", + "RMSE Training = 0.06449101121109886\n", + "RMSE Testing = 0.06451035617068136\n", + "Out of Bag error = 0.3876715295067261\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 184\n", + "Number of features = 2\n", + "RMSE Training = 0.0645064853386338\n", + "RMSE Testing = 0.0645275292581733\n", + "Out of Bag error = 0.3873668093025086\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 185\n", + "Number of features = 2\n", + "RMSE Training = 0.06451112549316351\n", + "RMSE Testing = 0.06448627189281433\n", + "Out of Bag error = 0.38745560891970676\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 186\n", + "Number of features = 2\n", + "RMSE Training = 0.06430457768309558\n", + "RMSE Testing = 0.06425333407312138\n", + "Out of Bag error = 0.3845810054621894\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 187\n", + "Number of features = 2\n", + "RMSE Training = 0.06422785708045838\n", + "RMSE Testing = 0.06415592716187893\n", + "Out of Bag error = 0.38383672677246006\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 188\n", + "Number of features = 2\n", + "RMSE Training = 0.06438333248690543\n", + "RMSE Testing = 0.06444287212391037\n", + "Out of Bag error = 0.3862274773217883\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 189\n", + "Number of features = 2\n", + "RMSE Training = 0.06410823174249927\n", + "RMSE Testing = 0.06411238011628334\n", + "Out of Bag error = 0.38251274330219254\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 190\n", + "Number of features = 2\n", + "RMSE Training = 0.06407162609173674\n", + "RMSE Testing = 0.06405871089223998\n", + "Out of Bag error = 0.38242627694703346\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 191\n", + "Number of features = 2\n", + "RMSE Training = 0.06408869196722566\n", + "RMSE Testing = 0.0640522358516534\n", + "Out of Bag error = 0.38360005837649225\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 192\n", + "Number of features = 2\n", + "RMSE Training = 0.06411891558953393\n", + "RMSE Testing = 0.06402483199664191\n", + "Out of Bag error = 0.3822256454932185\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 193\n", + "Number of features = 2\n", + "RMSE Training = 0.06469464939567138\n", + "RMSE Testing = 0.06473771004491886\n", + "Out of Bag error = 0.3898817627822015\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 194\n", + "Number of features = 2\n", + "RMSE Training = 0.06416633930941953\n", + "RMSE Testing = 0.06410160859379366\n", + "Out of Bag error = 0.38308963585866024\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 195\n", + "Number of features = 2\n", + "RMSE Training = 0.06428508325483002\n", + "RMSE Testing = 0.064249939591444\n", + "Out of Bag error = 0.38505729409800665\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 196\n", + "Number of features = 2\n", + "RMSE Training = 0.06448599982191938\n", + "RMSE Testing = 0.06444532409910894\n", + "Out of Bag error = 0.386948110529531\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 197\n", + "Number of features = 2\n", + "RMSE Training = 0.06385818923259906\n", + "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", + "Number of features = 3\n", + "RMSE Training = 0.06091698669768365\n", + "RMSE Testing = 0.060834482910836665\n", + "Out of Bag error = 0.3496806008329285\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 32\n", + "Number of features = 3\n", + "RMSE Training = 0.0603522501165095\n", + "RMSE Testing = 0.06033635148432213\n", + "Out of Bag error = 0.3440668368960028\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 33\n", + "Number of features = 3\n", + "RMSE Training = 0.06064061420491705\n", + "RMSE Testing = 0.060534614571547254\n", + "Out of Bag error = 0.34699970407591424\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 34\n", + "Number of features = 3\n", + "RMSE Training = 0.061039843213025434\n", + "RMSE Testing = 0.06115134181978303\n", + "Out of Bag error = 0.35147634800712313\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 35\n", + "Number of features = 3\n", + "RMSE Training = 0.060085855612217506\n", + "RMSE Testing = 0.0600231721974408\n", + "Out of Bag error = 0.3402501825623455\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 36\n", + "Number of features = 3\n", + "RMSE Training = 0.060214374081219914\n", + "RMSE Testing = 0.06004530237721678\n", + "Out of Bag error = 0.34053154130066837\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 37\n", + "Number of features = 3\n", + "RMSE Training = 0.060113340372585554\n", + "RMSE Testing = 0.05988746455956688\n", + "Out of Bag error = 0.33963777764104514\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 38\n", + "Number of features = 3\n", + "RMSE Training = 0.06014757290870888\n", + "RMSE Testing = 0.060074129430803404\n", + "Out of Bag error = 0.3396756727158878\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 39\n", + "Number of features = 3\n", + "RMSE Training = 0.06055463606628823\n", + "RMSE Testing = 0.06039341750048517\n", + "Out of Bag error = 0.3432077019701885\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 40\n", + "Number of features = 3\n", + "RMSE Training = 0.06052663884007028\n", + "RMSE Testing = 0.06055367126228994\n", + "Out of Bag error = 0.34384649228563546\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 41\n", + "Number of features = 3\n", + "RMSE Training = 0.05980729983645246\n", + "RMSE Testing = 0.05971833671206622\n", + "Out of Bag error = 0.33567305966921324\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 42\n", + "Number of features = 3\n", + "RMSE Training = 0.061291079578224515\n", + "RMSE Testing = 0.06120499930789811\n", + "Out of Bag error = 0.3519052962253236\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 43\n", + "Number of features = 3\n", + "RMSE Training = 0.06032252483449361\n", + "RMSE Testing = 0.06026232974080949\n", + "Out of Bag error = 0.3414620925584081\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 44\n", + "Number of features = 3\n", + "RMSE Training = 0.060418413035242736\n", + "RMSE Testing = 0.06041592337499846\n", + "Out of Bag error = 0.34211305461933056\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 45\n", + "Number of features = 3\n", + "RMSE Training = 0.06093827163750744\n", + "RMSE Testing = 0.060888046526366035\n", + "Out of Bag error = 0.3482566321813663\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 46\n", + "Number of features = 3\n", + "RMSE Training = 0.06028671478621832\n", + "RMSE Testing = 0.0602374423391963\n", + "Out of Bag error = 0.34119321831109206\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 47\n", + "Number of features = 3\n", + "RMSE Training = 0.06034326427251022\n", + "RMSE Testing = 0.060336952187909666\n", + "Out of Bag error = 0.3425451499536285\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 48\n", + "Number of features = 3\n", + "RMSE Training = 0.05992624057708684\n", + "RMSE Testing = 0.05984620038880524\n", + "Out of Bag error = 0.33765781442892717\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 49\n", + "Number of features = 3\n", + "RMSE Training = 0.06061881062626917\n", + "RMSE Testing = 0.06056528307841373\n", + "Out of Bag error = 0.34395973624060894\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 50\n", + "Number of features = 3\n", + "RMSE Training = 0.060187955362830524\n", + "RMSE Testing = 0.060263641884764615\n", + "Out of Bag error = 0.34000180730385055\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 51\n", + "Number of features = 3\n", + "RMSE Training = 0.06061943129065158\n", + "RMSE Testing = 0.06049003382164022\n", + "Out of Bag error = 0.3428767219289209\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 52\n", + "Number of features = 3\n", + "RMSE Training = 0.06062399400272026\n", + "RMSE Testing = 0.060647617675123014\n", + "Out of Bag error = 0.3438568851502549\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 53\n", + "Number of features = 3\n", + "RMSE Training = 0.05996638091020336\n", + "RMSE Testing = 0.05983479385953089\n", + "Out of Bag error = 0.3363059323273305\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 54\n", + "Number of features = 3\n", + "RMSE Training = 0.06060052782468596\n", + "RMSE Testing = 0.0605563261964027\n", + "Out of Bag error = 0.3427181408577277\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 55\n", + "Number of features = 3\n", + "RMSE Training = 0.060343924565608276\n", + "RMSE Testing = 0.060316476892592306\n", + "Out of Bag error = 0.34135905301675773\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 56\n", + "Number of features = 3\n", + "RMSE Training = 0.060815148542910605\n", + "RMSE Testing = 0.06081940116943817\n", + "Out of Bag error = 0.34487673186983797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 57\n", + "Number of features = 3\n", + "RMSE Training = 0.05984780352309484\n", + "RMSE Testing = 0.059707800668086494\n", + "Out of Bag error = 0.3355227096489359\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 58\n", + "Number of features = 3\n", + "RMSE Training = 0.0605732392788342\n", + "RMSE Testing = 0.06059587285923875\n", + "Out of Bag error = 0.3430678555907575\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 59\n", + "Number of features = 3\n", + "RMSE Training = 0.06029601205522613\n", + "RMSE Testing = 0.06029240820831383\n", + "Out of Bag error = 0.33973041384496655\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 60\n", + "Number of features = 3\n", + "RMSE Training = 0.060841580205563774\n", + "RMSE Testing = 0.06070652267588863\n", + "Out of Bag error = 0.34662537276549255\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 61\n", + "Number of features = 3\n", + "RMSE Training = 0.06074137905772017\n", + "RMSE Testing = 0.06065663768732514\n", + "Out of Bag error = 0.3445208313455531\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 62\n", + "Number of features = 3\n", + "RMSE Training = 0.06092769785944051\n", + "RMSE Testing = 0.060927185621512184\n", + "Out of Bag error = 0.346863696111961\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 63\n", + "Number of features = 3\n", + "RMSE Training = 0.06010154183752377\n", + "RMSE Testing = 0.06003120250181858\n", + "Out of Bag error = 0.33745097446130284\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 64\n", + "Number of features = 3\n", + "RMSE Training = 0.06024877593573227\n", + "RMSE Testing = 0.060173615539473216\n", + "Out of Bag error = 0.3393674882757437\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 65\n", + "Number of features = 3\n", + "RMSE Training = 0.06035198683173373\n", + "RMSE Testing = 0.060269386947751705\n", + "Out of Bag error = 0.3398913628569875\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 66\n", + "Number of features = 3\n", + "RMSE Training = 0.060191832205992155\n", + "RMSE Testing = 0.060177994380073783\n", + "Out of Bag error = 0.3390324655480253\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 67\n", + "Number of features = 3\n", + "RMSE Training = 0.060457260225578034\n", + "RMSE Testing = 0.060447751620351986\n", + "Out of Bag error = 0.3413603284776936\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 68\n", + "Number of features = 3\n", + "RMSE Training = 0.060769094284179245\n", + "RMSE Testing = 0.060808956822635464\n", + "Out of Bag error = 0.34505206697300644\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 69\n", + "Number of features = 3\n", + "RMSE Training = 0.06061624272504538\n", + "RMSE Testing = 0.06050864560172926\n", + "Out of Bag error = 0.34244402396441614\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 70\n", + "Number of features = 3\n", + "RMSE Training = 0.06099330059216068\n", + "RMSE Testing = 0.060984566041962875\n", + "Out of Bag error = 0.3470927955176909\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 71\n", + "Number of features = 3\n", + "RMSE Training = 0.06033786346252786\n", + "RMSE Testing = 0.06031197211689017\n", + "Out of Bag error = 0.34043250651108536\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 72\n", + "Number of features = 3\n", + "RMSE Training = 0.06070702143904837\n", + "RMSE Testing = 0.06072660557652474\n", + "Out of Bag error = 0.3437801605680195\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 73\n", + "Number of features = 3\n", + "RMSE Training = 0.06072795777070367\n", + "RMSE Testing = 0.06070025142432709\n", + "Out of Bag error = 0.34456988887820766\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 74\n", + "Number of features = 3\n", + "RMSE Training = 0.06030313253879811\n", + "RMSE Testing = 0.060314386030869284\n", + "Out of Bag error = 0.3390874309386811\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 75\n", + "Number of features = 3\n", + "RMSE Training = 0.05977089691135261\n", + "RMSE Testing = 0.059658703551353845\n", + "Out of Bag error = 0.3331347176534764\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 76\n", + "Number of features = 3\n", + "RMSE Training = 0.060465227569251254\n", + "RMSE Testing = 0.06040909799544517\n", + "Out of Bag error = 0.3409142168647058\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 77\n", + "Number of features = 3\n", + "RMSE Training = 0.06066356240390812\n", + "RMSE Testing = 0.060661471696154066\n", + "Out of Bag error = 0.3429050934816857\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 78\n", + "Number of features = 3\n", + "RMSE Training = 0.060273913206822585\n", + "RMSE Testing = 0.060180945396984285\n", + "Out of Bag error = 0.3385153157897655\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 79\n", + "Number of features = 3\n", + "RMSE Training = 0.06069014146090823\n", + "RMSE Testing = 0.0607303076758182\n", + "Out of Bag error = 0.3427954827156764\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 80\n", + "Number of features = 3\n", + "RMSE Training = 0.06024235416629699\n", + "RMSE Testing = 0.05999482654962378\n", + "Out of Bag error = 0.33867882109679315\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 81\n", + "Number of features = 3\n", + "RMSE Training = 0.06030022698400821\n", + "RMSE Testing = 0.06028220587388313\n", + "Out of Bag error = 0.3388553129201502\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 82\n", + "Number of features = 3\n", + "RMSE Training = 0.06025571321060909\n", + "RMSE Testing = 0.060267166475006836\n", + "Out of Bag error = 0.33877192431226966\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + 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0.33795226730172684\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 162\n", + "Number of features = 3\n", + "RMSE Training = 0.060243661754051284\n", + "RMSE Testing = 0.060240228402086385\n", + "Out of Bag error = 0.33706230082006466\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 163\n", + "Number of features = 3\n", + "RMSE Training = 0.06047797652894567\n", + "RMSE Testing = 0.060444738922313626\n", + "Out of Bag error = 0.3399820683172788\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 164\n", + "Number of features = 3\n", + "RMSE Training = 0.06064285465291289\n", + "RMSE Testing = 0.060567648298826174\n", + "Out of Bag error = 0.34282506168227844\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 165\n", + "Number of features = 3\n", + "RMSE Training = 0.060301493484967904\n", + "RMSE Testing = 0.060226683716943154\n", + "Out of Bag error = 0.3377858936595657\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 166\n", + "Number of features = 3\n", + "RMSE Training = 0.06008884362322421\n", + "RMSE Testing = 0.05997586612873792\n", + "Out of Bag error = 0.33600468637619896\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 167\n", + "Number of features = 3\n", + "RMSE Training = 0.060522004734584724\n", + "RMSE Testing = 0.06044944314158755\n", + "Out of Bag error = 0.3404019625988007\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 168\n", + "Number of features = 3\n", + "RMSE Training = 0.0601282736029474\n", + "RMSE Testing = 0.060116885718335865\n", + "Out of Bag error = 0.3357785177972712\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 169\n", + "Number of features = 3\n", + "RMSE Training = 0.059928466762988776\n", + "RMSE Testing = 0.059827769446961346\n", + "Out of Bag error = 0.33432910884248684\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 170\n", + "Number of features = 3\n", + "RMSE Training = 0.060212808831823596\n", + "RMSE Testing = 0.060156253646714494\n", + "Out of Bag error = 0.33725844436959374\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 171\n", + "Number of features = 3\n", + "RMSE Training = 0.060370989071659845\n", + "RMSE Testing = 0.060351890372468464\n", + "Out of Bag error = 0.3387147920684247\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 172\n", + "Number of features = 3\n", + "RMSE Training = 0.059776376044830805\n", + "RMSE Testing = 0.059815852380838176\n", + "Out of Bag error = 0.3322584525600456\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 173\n", + "Number of features = 3\n", + "RMSE Training = 0.06038116365616941\n", + "RMSE Testing = 0.06042246290218768\n", + "Out of Bag error = 0.33888553481197936\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 174\n", + "Number of features = 3\n", + "RMSE Training = 0.06013942162886463\n", + "RMSE Testing = 0.06006615607433241\n", + "Out of Bag error = 0.3365037616278507\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 175\n", + "Number of features = 3\n", + "RMSE Training = 0.06022697983552385\n", + "RMSE Testing = 0.060119789178867365\n", + "Out of Bag error = 0.33705444808107676\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 176\n", + "Number of features = 3\n", + "RMSE Training = 0.06012088756015258\n", + "RMSE Testing = 0.060075432835225574\n", + "Out of Bag error = 0.33598821225059483\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 177\n", + "Number of features = 3\n", + "RMSE Training = 0.06074990388081959\n", + "RMSE Testing = 0.06071862920412371\n", + "Out of Bag error = 0.3427863251850691\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 178\n", + "Number of features = 3\n", + "RMSE Training = 0.06021900298482273\n", + "RMSE Testing = 0.06011230098451089\n", + "Out of Bag error = 0.33690053804598014\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 179\n", + "Number of features = 3\n", + "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", + "Number of features = 4\n", + "RMSE Training = 0.06088152014341284\n", + "RMSE Testing = 0.06081940877209269\n", + "Out of Bag error = 0.34393115735504887\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 81\n", + "Number of features = 4\n", + "RMSE Training = 0.06084642220462405\n", + "RMSE Testing = 0.06081923411798048\n", + "Out of Bag error = 0.34326972099874753\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 82\n", + "Number of features = 4\n", + "RMSE Training = 0.060899413531212\n", + "RMSE Testing = 0.060786451289099576\n", + "Out of Bag error = 0.34366678126566386\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 83\n", + "Number of features = 4\n", + "RMSE Training = 0.06094331067390481\n", + "RMSE Testing = 0.06085018521097019\n", + "Out of Bag error = 0.34387933771381807\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 84\n", + "Number of features = 4\n", + "RMSE Training = 0.06086852712782605\n", + "RMSE Testing = 0.06075448343817134\n", + "Out of Bag error = 0.3433365362908357\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 85\n", + "Number of features = 4\n", + "RMSE Training = 0.060916107189766554\n", + "RMSE Testing = 0.06082255654306161\n", + "Out of Bag error = 0.34420471615775866\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 86\n", + "Number of features = 4\n", + "RMSE Training = 0.06071420039474397\n", + "RMSE Testing = 0.06060075453238593\n", + "Out of Bag error = 0.342505689298383\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 87\n", + "Number of features = 4\n", + "RMSE Training = 0.060972263262722835\n", + "RMSE Testing = 0.06088322750253504\n", + "Out of Bag error = 0.3448589398838598\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 88\n", + "Number of features = 4\n", + "RMSE Training = 0.06089209579626492\n", + "RMSE Testing = 0.060797082894489686\n", + "Out of Bag error = 0.3438218796566104\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 89\n", + "Number of features = 4\n", + "RMSE 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"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", + "Number of trees = 62\n", + "Number of features = 5\n", + "RMSE Training = 0.060443430304165414\n", + "RMSE Testing = 0.060382968914671965\n", + "Out of Bag error = 0.3385804302453199\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 63\n", + "Number of features = 5\n", + "RMSE Training = 0.06052207619610943\n", + "RMSE Testing = 0.06042414964536449\n", + "Out of Bag error = 0.3391607676623953\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 64\n", + "Number of features = 5\n", + "RMSE Training = 0.06042964411239852\n", + "RMSE Testing = 0.0603327493337512\n", + "Out of Bag error = 0.33811490271868483\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 65\n", + "Number of features = 5\n", + "RMSE Training = 0.060606930036977036\n", + "RMSE Testing = 0.060513728108503174\n", + "Out of Bag error = 0.3399485796551505\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 66\n", + "Number of features = 5\n", + "RMSE Training = 0.06060742857694643\n", + "RMSE Testing = 0.060481656205060054\n", + "Out of Bag error = 0.3398340440334576\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 67\n", + "Number of features = 5\n", + "RMSE Training = 0.060596262503731235\n", + "RMSE Testing = 0.060494201706877936\n", + "Out of Bag error = 0.340116048067565\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 68\n", + "Number of features = 5\n", + "RMSE Training = 0.0606265683865012\n", + "RMSE Testing = 0.06052602574817998\n", + "Out of Bag error = 0.34035863415998885\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 69\n", + "Number of features = 5\n", + "RMSE Training = 0.060567523475451426\n", + "RMSE Testing = 0.060459880634075334\n", + "Out of Bag error = 0.3396080855531896\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 70\n", + "Number of features = 5\n", + "RMSE Training = 0.06035997102224093\n", + "RMSE Testing = 0.0602703990074684\n", + "Out of Bag error = 0.33763550643034973\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 71\n", + "Number of features = 5\n", + "RMSE Training = 0.060552818852778675\n", + "RMSE Testing = 0.060428114405788935\n", + "Out of Bag error = 0.3393773879739771\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 72\n", + "Number of features = 5\n", + "RMSE Training = 0.060611498764467434\n", + "RMSE Testing = 0.0605161713564253\n", + "Out of Bag error = 0.34015500720763264\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 73\n", + "Number of features = 5\n", + "RMSE Training = 0.06035386859446602\n", + "RMSE Testing = 0.06027102234472498\n", + "Out of Bag error = 0.33731736758901637\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 74\n", + "Number of features = 5\n", + "RMSE Training = 0.06053095859607668\n", + "RMSE Testing = 0.06043725726435365\n", + "Out of Bag error = 0.3392903874602296\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 75\n", + "Number of features = 5\n", + "RMSE Training = 0.06049890937911736\n", + "RMSE Testing = 0.060388025965544045\n", + "Out of Bag error = 0.3389776805392891\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 76\n", + "Number of features = 5\n", + "RMSE Training = 0.06061131519850242\n", + "RMSE Testing = 0.06050497864231922\n", + "Out of Bag error = 0.3402196972511747\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 77\n", + "Number of features = 5\n", + "RMSE Training = 0.06054418377096158\n", + "RMSE Testing = 0.06044350326841148\n", + "Out of Bag error = 0.3394923362825727\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 78\n", + "Number of features = 5\n", + "RMSE Training = 0.060569521128277716\n", + "RMSE Testing = 0.060476653936981914\n", + "Out of Bag error = 0.339641392828758\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 79\n", + "Number of features = 5\n", + "RMSE Training = 0.060464920513289944\n", + "RMSE Testing = 0.06037971088105024\n", + "Out of Bag error = 0.33846535937050837\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 80\n", + "Number of features = 5\n", + "RMSE Training = 0.06046144810404832\n", + "RMSE Testing = 0.0603612948671318\n", + "Out of Bag error = 0.3384948967380896\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 81\n", + "Number of features = 5\n", + "RMSE Training = 0.060519739996118906\n", + "RMSE Testing = 0.060389843512680966\n", + "Out of Bag error = 0.33905991692293874\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 82\n", + "Number of features = 5\n", + "RMSE Training = 0.06048914838813698\n", + "RMSE Testing = 0.0603789837430538\n", + "Out of Bag error = 0.3389027893499034\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 83\n", + "Number of features = 5\n", + "RMSE Training = 0.06051545622034926\n", + "RMSE Testing = 0.060424221173177785\n", + "Out of Bag error = 0.3391890031339676\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 84\n", + "Number of features = 5\n", + "RMSE Training = 0.06059296811415088\n", + "RMSE Testing = 0.06050789337335798\n", + "Out of Bag error = 0.33992405297693107\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 85\n", + "Number of features = 5\n", + "RMSE Training = 0.060527212142341344\n", + "RMSE Testing = 0.06043576656231141\n", + "Out of Bag error = 0.33947401357565127\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 86\n", + "Number of features = 5\n", + "RMSE Training = 0.06051589309324491\n", + "RMSE Testing = 0.06042034883837283\n", + "Out of Bag error = 0.3391456892138915\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 87\n", + "Number of features = 5\n", + "RMSE Training = 0.060601555243438396\n", + "RMSE Testing = 0.060466573740589266\n", + "Out of Bag error = 0.34001965951946944\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 88\n", + "Number of features = 5\n", + "RMSE Training = 0.06050909701073416\n", + "RMSE Testing = 0.06041177587695864\n", + "Out of Bag error = 0.33902547758647117\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 89\n", + "Number of features = 5\n", + "RMSE Training = 0.06038118524416149\n", + "RMSE Testing = 0.060323636058243965\n", + "Out of Bag error = 0.3375193716165724\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 90\n", + "Number of features = 5\n", + "RMSE Training = 0.060493495759439596\n", + "RMSE Testing = 0.06039605136476937\n", + "Out of Bag error = 0.33884190087193716\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 91\n", + "Number of features = 5\n", + "RMSE Training = 0.06057107503741486\n", + "RMSE Testing = 0.06048749018922674\n", + "Out of Bag error = 0.3396239909490232\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 92\n", + "Number of features = 5\n", + "RMSE Training = 0.06054973534044887\n", + "RMSE Testing = 0.06046629092799266\n", + "Out of Bag error = 0.33946811874279736\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 93\n", + "Number of features = 5\n", + "RMSE Training = 0.06061092808561862\n", + "RMSE Testing = 0.06048907902200158\n", + "Out of Bag error = 0.34015017492387656\n", + "-----------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------------------------------------\n", + "Number of trees = 94\n", + "Number of features = 5\n", + "RMSE Training = 0.060519790612524715\n", + "RMSE Testing = 0.06043462960913791\n", + "Out of Bag error = 0.33910892068505094\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 95\n", + "Number of features = 5\n", + "RMSE Training = 0.06053214405746236\n", + "RMSE Testing = 0.06043549367606608\n", + "Out of Bag error = 0.33929514609714156\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 96\n", + "Number of features = 5\n", + "RMSE Training = 0.060499499722673156\n", + "RMSE Testing = 0.060412889338944474\n", + "Out of Bag error = 0.338790207821698\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 97\n", + "Number of features = 5\n", + "RMSE Training = 0.060462946104966865\n", + "RMSE Testing = 0.06034864588692225\n", + "Out of Bag error = 0.3384303169520767\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 98\n", + "Number of features = 5\n", + "RMSE Training = 0.06046367243358684\n", + "RMSE Testing = 0.06039332891760866\n", + "Out of Bag error = 0.3385637670389797\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 99\n", + "Number of features = 5\n", + "RMSE Training = 0.06045374840865314\n", + "RMSE Testing = 0.06036224262372107\n", + "Out of Bag error = 0.3384335912510298\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 100\n", + "Number of features = 5\n", + "RMSE Training = 0.06054973706751643\n", + "RMSE Testing = 0.06044500851719583\n", + "Out of Bag error = 0.33955928123769547\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 101\n", + "Number of features = 5\n", + "RMSE Training = 0.060438954048713245\n", + "RMSE Testing = 0.06035023450551252\n", + "Out of Bag error = 0.3380949934815079\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 102\n", + "Number of features = 5\n", + "RMSE Training = 0.06053949610812983\n", + "RMSE Testing = 0.06044452510037544\n", + "Out of Bag error = 0.33920295441845577\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 103\n", + "Number of features = 5\n", + "RMSE Training = 0.060508238807270485\n", + "RMSE Testing = 0.06040486227735055\n", + "Out of Bag error = 0.33895666113281175\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 104\n", + "Number of features = 5\n", + "RMSE Training = 0.06044223760812717\n", + "RMSE Testing = 0.06035300695770942\n", + "Out of Bag error = 0.338442337771912\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + 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[ + "-----------------------------------------------------\n", + "Number of trees = 190\n", + "Number of features = 5\n", + "RMSE Training = 0.0604940057664524\n", + "RMSE Testing = 0.06039991926315389\n", + "Out of Bag error = 0.33882302305563455\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 191\n", + "Number of features = 5\n", + "RMSE Training = 0.06053145379315141\n", + "RMSE Testing = 0.06041056038917146\n", + "Out of Bag error = 0.33914918790677673\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 192\n", + "Number of features = 5\n", + "RMSE Training = 0.06044086176357003\n", + "RMSE Testing = 0.0603486126844728\n", + "Out of Bag error = 0.3380447134124191\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 193\n", + "Number of features = 5\n", + "RMSE Training = 0.06039501008271615\n", + "RMSE Testing = 0.06031146045318168\n", + "Out of Bag error = 0.3378094701947227\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 194\n", + "Number of features = 5\n", + "RMSE Training = 0.06050101124967529\n", + "RMSE Testing = 0.060392194466090766\n", + "Out of Bag error = 0.33870780242654336\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 195\n", + "Number of features = 5\n", + "RMSE Training = 0.060537022906515546\n", + "RMSE Testing = 0.06042821632403265\n", + "Out of Bag error = 0.339032667581182\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 196\n", + "Number of features = 5\n", + "RMSE Training = 0.06051249758519792\n", + "RMSE Testing = 0.06040891686195142\n", + "Out of Bag error = 0.33884943202986517\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 197\n", + "Number of features = 5\n", + "RMSE Training = 0.060482333122738395\n", + "RMSE Testing = 0.06038450267735157\n", + "Out of Bag error = 0.3387200434121965\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 198\n", + "Number of features = 5\n", + "RMSE Training = 0.060459132880708887\n", + "RMSE Testing = 0.060360767224025356\n", + "Out of Bag error = 0.3383033102168782\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 199\n", + "Number of features = 5\n", + "RMSE Training = 0.060516140293304745\n", + "RMSE Testing = 0.06039259220413109\n", + "Out of Bag error = 0.33899469343187266\n", + "-----------------------------------------------------\n", + "-----------------------------------------------------\n", + "Number of trees = 200\n", + "Number of features = 5\n", + "RMSE Training = 0.060470622944550655\n", + "RMSE Testing = 0.06035642665436379\n", + "Out of Bag error = 0.33852502883154906\n", + "-----------------------------------------------------\n" + ] + }, + { + "data": { + "image/png": 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\n", 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TDEYSkZi4lPAbxM0i+e9+e62IPCYuRfdGEflSJdtvGIZhlJeKiY64ORWuxs0DsRy4UESW5+z2EaBbVY/DpZO/0m8/HzftwMnAq4FLvCDFgbNU9VW4hJ7nisvJZRiGYRwEVNLSWYlLgb7Fjyy/GZehNMx5uPxK4FLFnO1HfSvQ4HMS1eFyEvX5BHkDfv+Y/7OYb8MwjIOESorOEYxNAb6D8SnFs/uoagqXz2cuToAGcVl5t+PSK3RBdra9dbh5Hf7g862NwydVXCsia9vb28t3VYZhGMZ+M10DCVbikmcuBo7GzYVzDLgpDFT1FFwK+JUSmqI6jKpeq6orVHVFc3NZI/4MwzCM/aSSorOTsfNkLGH8PBbZfbwrbSYuG+x7cVNNJ1W1DZfheUX4QFXtwWUCPrcirTcMwzDKTiVF53HgeBE5WkSqcdP2rsrZZxVuqmNw6cfv8Wm4t+PmmQ/mLzkDeF5EmsXNXxMk3TwHl068Itzw8FZ+8/SuSlVvGIZx2FEx0fF9NJfi5gt5DrhVVTeKyBV+oi9wU7XOFZEW3GRXQVj11UCjiGzEidf1qroeNyfNvSKy3m//g6r+tlLXcMvjrdz+5D5POWIYhmEUoKIZCVR1NTkTUanqF0LLI4RmsAttHyiwfT1wavlbmp+lc+p5sa1/4h0NwzCMkpiugQTTgqPm1tPaPUwmY1HZhmEY5cBEpwhHzqknkcrQ1p9vlmbDMAxjXzHRKcLSOfUAbOscPMAtMQzDODQw0SlCIDrbu4YOcEsMwzAODUx0irB4Vh0RgVYTHcMwjLJgolOE6qoIi2bWmaVjGIZRJkx0JuCoufUmOoZhGGXCRGcCls6pZ3vX8IFuhmEYxiGBic4EHDmnno6BOEOJ1IFuimEYxkGPic4EHOkj2HZ0m7VjGIYxWUx0JqChOgrAcCJ9gFtiGIZx8GOiMwHRiACQslQ4hmEYk8ZEZwIC0cmoiY5hGMZkMdGZgKg40UmbpWMYhjFpTHQmIBJYOiY6hmEYk8ZEZwIC91ra3GuGYRiTpqKiIyLnisgmEWkRkcvylNeIyC2+fI2ILPPbYyJyg4hsEJHnROTf/fYjReReEXlWRDaKyCcr2X6AiLnXDMMwykbFREdEorhpp98CLAcuFJHlObt9BOhW1eOAq4Ar/fbzgRpVPRl4NXCJF6QU8GlVXQ6cAfxjnjrLigUSGIZhlI9KWjorgRZV3aKqCeBm4Lycfc4DbvDLtwFni4gACjSISBVQBySAPlXdrapPAqhqP/AccEQFr4GqIGQ6baJjGIYxWSopOkcAraH1HYwXiOw+qpoCeoG5OAEaBHYD24FvqGpX+EBv+ZwKrMl3chG5WETWisja9vb2/b6IwL1mlo5hGMbkma6BBCuBNLAYOBr4tIgcExSKSCPwC+CfVbUvXwWqeq2qrlDVFc3NzfvdkGwgQWa/qzAMwzA8lRSdncCRofUlflvefbwrbSbQCbwXuFNVk6raBjwErPD7xXCCc6Oq3l7B9gMQ9Z+QRa8ZhmFMnkqKzuPA8SJytIhUAxcAq3L2WQV80C+/C7hHVRXnUjsLQEQacEEDz/v+nuuA51T1WxVse5ase82i1wzDMCZNxUTH99FcCtyF6/C/VVU3isgVIvIOv9t1wFwRaQE+BQRh1VcDjSKyESde16vqeuDPgfcDZ4nIOv/31kpdA4TdayY6hmEYk6WqkpWr6mpgdc62L4SWR3Dh0bnHDRTY/iAg5W9pYWycjmEYRvmYroEE04aqqGUkMAzDKBcmOhNgCT8NwzDKh4nOBEQsI4FhGEbZMNGZALN0DMMwyoeJzgRELHrNMAyjbJjoTIAl/DQMwygfJjoTkE34aZaOYRjGpDHRmQDLSGAYhlE+THQmwBJ+GoZhlA8TnQnwmmODQw3DMMqAic4EiAgRMfeaYRhGOTDRKYFoRMzSMQzDKAMmOiUQEbFxOoZhGGXARKcEqiImOoZhGOXARKcEIiY6hmEYZaGioiMi54rIJhFpEZHL8pTXiMgtvnyNiCzz22MicoOIbBCR50Tk30PH/EhE2kTkmUq2PUw0IpaRwDAMowxUTHREJIqbAfQtwHLgQhFZnrPbR4BuVT0OuAq40m8/H6hR1ZOBVwOXBIIE/Bg4t1LtzkfU+nQMwzDKQiUtnZVAi6puUdUEcDNwXs4+5wE3+OXbgLNFRAAFGkSkCqgDEkAfgKo+AHRVsN3jiJilYxiGURYqKTpHAK2h9R1+W959VDUF9AJzcQI0COwGtgPfUNV9EhoRuVhE1orI2vb29v27Ao9ZOoZhGOVhugYSrATSwGLgaODTInLMvlSgqteq6gpVXdHc3DypxkQjYgk/DcMwykAlRWcncGRofYnflncf70qbCXQC7wXuVNWkqrYBDwErKtjWokQjYhkJDMMwykAlRedx4HgROVpEqoELgFU5+6wCPuiX3wXco6qKc6mdBSAiDcAZwPMVbGtRXEaCA3V2wzCMQ4eKiY7vo7kUuAt4DrhVVTeKyBUi8g6/23XAXBFpAT4FBGHVVwONIrIRJ17Xq+p6ABG5CXgEOFFEdojIRyp1DQGWe80wDKM8VFWyclVdDazO2faF0PIILjw697iBfNt92YVlbuaERG1wqGEYRlmYroEE04qIWMJPwzCMcmCiUwJm6RiGYZSHgqIjIt8MLV+aU3ZdJRs13bCEn4ZhGOWhmKVzZmj5wzllp1agLdMWy0hgGIZRHoqJjhRYPuywjASGYRjloVj0WkREZuCEKVgOxCda8ZZNI2xqA8MwjPJQTHTmAhsZFZpnQ2WH1RM4KkIqkznQzTAMwzjoKSg6qrpkKhsynYlGhHjqsNJZwzCMilAseu1IEWkKrb9BRL4pIv8kIrGpad70wEKmDcMwykOxQIKfA00AIvIq4JdAG3A6Lk3NYYPLvWaiYxiGMVmK9enUq+oOv/w+4EeqeqWIRICnK9+06UNEhLR16RiGYUyaUkOmzwLuBlDVDIdbIEHEEn4ahmGUg2KWzv0i8jPc7J1zgXsARGQhkJyCtk0bzL1mGIZRHopZOp/AZYjeA7xeVRN++2Lg85Vu2HQiIjaJm2EYRjkoFjKdAf43z/YnK9qiaYhNV20YhlEeioVMd4tIV+ivO/y/lMpF5FwR2SQiLSJyWZ7yGhG5xZevEZFlfntMRG4QkQ0i8pyI/HupdVYCC5k2DMMoD8Xcaw8BLcB/AqcA84Dm0P+iiEgUF1r9FmA5cKGILM/Z7SNAt6oeB1wFXOm3nw/UqOrJwKuBS0RkWYl1lp2oWMJPwzCMclBQdFT17cCbgW7gR8AfcCIxQ1XTJdS9EmhR1S2+P+hm4Lycfc4DbvDLtwFni4jgouMaRKQKqAMSQF+JdZYds3QMwzDKQ9FJ3FS1W1V/AJwD/AD4D5zwlMIRQGtofYfflncfVU0BvbhIuduAQVzk3HbgG6raVWKdZcemNjAMwygPxUKmEZGVwIW4uXUexbm97qt8s1gJpHGRcrOBP4nIH/elAhG5GLgYYOnSpZNqjE1tYBiGUR4Kio6IbAb6cS6sjzA6NudkEUFV109Q907gyND6Er8t3z47vCttJtAJvBe4U1WTQJuIPASswFk5E9UJgKpeC1wLsGLFikkphrnXDMMwykMxS2c3rm/lbcBbGZuhQIE3TFD348DxInI0ThguwIlJmFXAB4FHgHcB96iqish2XBaEn4pIA3AG8F+46RUmqrPsRMzSMQzDKAvFxum8bjIVq2pKRC4F7sJN+vYjVd0oIlcAa1V1FXAdTlhagC6ciICLULteRIL5fK4PLKt8dU6mnaVQFbWMBIZhGOWgaJ9OPkTkTODfVPUtE+2rqqtxWQ3C274QWh7B9RPlHjeQb3uhOiuNy0gwlWc0DMM4NCk2OPSNIvKsiPSIyI9FZLmIPIpzc10/dU088EQjmKVjGIZRBoqFTF+Fy792BPBbYA1wk6q+SlVvnYrGTRcses0wDKM8TDRO54+qOqiqtwG7VPXbU9SuaUUk4mIoLOmnYRjG5CjWpzNTRN4R3je87gMBDgui4kQnlVGqIzLB3oZhGEYhionOQ4ztzH84tK64cOfDgmjUWzrWr2MYhjEpioVMv38qGzKdCSwd69cxDMOYHEX7dAxH1LvULILNMAxjcpjolEBELJDAMAyjHEwoOj4n2oTbDmWylo6JjmEYxqQoxdJ5rMRthywRc68ZhmGUhWJZpucDi4A6ETmZ0YSfTUD9FLRt2mCBBIZhGOWhmJvsbcCHcdMHXM2o6PQDn69wu6YVVeZeMwzDKAvFQqavx2V6fvfhlvYml9GMBAe4IYZhGAc5pfTpzBeRJgARuUZEHhORsyvcrmlF1H9K1qdjGIYxOUoRnYtVtU9E/hLXx/Mx4GuVbdb0ImJ9OoZhGGWhFNEJnrRvBX6iqk+XeNwhQxAybWlwDMMwJkcp4vG0iKwG3g7cISKNjApRUUTkXBHZJCItInJZnvIaEbnFl68RkWV++0Uisi70lxGRU3zZe0RkvYhsFJErS73QyZBN+Jk20TEMw5gMpYjO3wGXAytVdQioBT4y0UEiEsVFvb0FWA5cKCLLc3b7CNCtqsfh5u+5EkBVb1TVU1T1FOD9wEuquk5E5gJfB85W1ZcDC6eif8ksHcMwjPIwoeioaho4BvgHv6mulOOAlUCLqm5R1QRwM3Bezj7nATf45duAs0Ukd+6AC/2x+Ha8qKrtfv2PwN+W0JZJYRkJDMMwykMpaXC+C5wJvM9vGgSuKaHuI4DW0PoOvy3vPqqaAnqBuTn7vAe4yS+3ACeKyDKfiuedwJEF2n2xiKwVkbXt7e35dikZy0hgGIZRHkqxWF6rqpcAIwCq2gVUV7RVHhE5HRhS1Wf8ubtxFtctwJ+ArUA637Gqeq2qrlDVFc3NzZNqR9QSfhqGYZSFUkQnKSIRfPCA71cpZZjkTsZaIUv8trz7eMtlJtAZKr+AUSsHAFX9jaqerqp/BmwCXiihLZPC3GuGYRjloRTRuRr4BdAsIl8CHsR3+E/A48DxInK0iFTjBCR3ttFVwAf98ruAe1Q1ELcI8G5G+3Pw2+f7/7OBjwM/LKEtkyI7Tsfca4ZhGJOiWMLPKlVNqepPROQJ4E24/GvnB+6uYqhqSkQuBe4CosCPVHWjiFwBrFXVVcB1wE9FpAXowglTwBuAVlXdklP1t0XkVX75ClU1S8cwDOMgoVjCz8eA0wBUdSOwcV8rV9XVwOqcbV8ILY8A5xc49j7gjDzbL9zXdkwWEx3DMIzyUMy9lhu6fNhi43QMwzDKQzFLp1lEPlWoUFW/VYH2TEtG59M5wA0xDMM4yCkmOlGgEbN4iARZps29ZhiGMSmKic5uVb1iyloyjTH3mmEYRnmwPp0SyCb89JbOo1s6uf+FyWU5MAzDOBwpZukcVhO1FWN05lAnOt+9p4We4QR/ccLkMh0YhmEcbhS0dHy6GwOoygmZHkykGIrnzb5jGIZhFOGwmoxtf8nNSDCcSDOYSJWt/ivvfJ6frdletvoMwzCmKyWJjogcJSJv8st1IjKjss2aXkRz3GtDiXRZLZ1fP7WT+za1la0+wzCM6UopUxt8DDfXzff9piXAryrZqOlGNGdqgyFv6WiZotm6h5IkbBCQYRiHAaVYOv8I/DnQB6CqLwLzK9mo6UYkZ2qD4USKjEI8leHhlg5uf3LHftc9kkwznEyTNNExDOMwoBTRifuZP4HsFASH1YCVwNJJZRRVZSjpXGsD8RQ/fngr37n7xf2uu3c4CUAiZaJjGMahTymic7+IfAaoE5FzgJ8Dv6lss6YX4YSf8VSGwKs2FE8zEE8xktx/wegecnpuomMYxuFAKaJzGdAObAAuwWWN/lwlGzXdCGckGE6MBhAMJlL0j6SIp8YGFezqGS7ZXdY96C2d9GFlPBqGcZgyoeioakZVf6Cq56vqu/zyYfWEDCf8DFxrAEOJFP0jSeIhK2VP7whv/Pp9/HrdrpLq7slaOjbuxzCMQ59Sotc2iMj6nL8/ichVfurqYseeKyKbRKRFRC7LU14jIrf48jUissxvv0hE1oX+MiJyii+7MNSmO0Vk3v5deukECT+dpTM6PmcwnvaWzqjo/OHZPSTSGboG4yXV3T3kLJ2kWTqGYRwGlOI9KmWNAAAgAElEQVReuwP4HXCR//sNsBbYA/y40EEiEsVNdf0WYDlwoYgsz9ntI0C3qh4HXIWfBltVb1TVU1T1FOD9wEuqus4HMXwbOFNVXwmsBy4t8Vr3m1FLRxlK5Fo6KdIZzbrT7tq4FyhdRKxPxzCMw4liudcC3qSqp4XWN4jIk6p6moi8r8hxK4GWYLppEbkZOA94NrTPecDlfvk24LsiIjnuuwuBm/2y+L8GEekEmoCWEq5hUoQDCcKiEx5fE09lGIqneXRLJwCpEkUn616zkGnDMA4DSrF0oiKyMlgRkdfg5toBKJYL5gigNbS+w2/Lu4+qpoBeINdl9x7gJr9PEvgHXFDDLpwFdV2+k4vIxSKyVkTWtrdPLiO0iCDiRCccSLCndyS7HE+muXdTWzYTdamBBD2Be80sHcMwDgNKEZ2PAteJyEsishX3kP+YiDQAX61k40TkdGBIVZ/x6zGc6JwKLMa51/4937Gqeq2qrlDVFc3Nk88GXRUR0jrW0tnbFxKdVIbHtnbRVFtFdTRCMlNi9JoXnbhZOoZhHAZM6F5T1ceBk0Vkpl/vDRXfWuTQncCRofUlflu+fXb4/pqZQGeo/AK8leM5xbdhM4CI3IoL6a44EREyGWUoFEiwJyQ6I8k0Q/EUsxuq6eiP77t7LZVBVRGxaYwMwzh0KaVPBxF5G/ByoDZ4KJYwq+jjwPEicjROXC4A3puzzyrgg8AjwLuAe4L+HBGJAO8GXh/afyewXESaVbUdOAd4rpRrmCzRiDj3WrKAey2VYTiZpi4WpSoaIVXqOJ2hbLIHUhklFjXRMQzj0GVC0RGRa4B64EzghzhxeGyi41Q1JSKXAnfh+oB+pKobReQKYK2qrsK56n4qIi1AF06YAt4AtAaBCL7OXSLyJeABEUkC24APlXSlkyQqY91rjTVV49xrw8kMtbEosWiEZKZUSyeZXU6kMsSiNtuEYRiHLqVYOq9V1VeKyHpV/ZKIfBMXRj0hqroal8EgvO0LoeUR4PwCx94HnJFn+zXANaWcv5xEIs69FgQSzG2sZlvnULY8nkwzknCWTiwqJVk6qkrPcJKG6iiDCUv6aRjGoU8pr9XB6/yQiCwGksCiyjVpehKNCCnvXquNRWisGavXI4F7rTpKVVRKGqfT58f4LGiqBWysjmEYhz6liM5vRGQW8HXgSWAr8LNKNmo6Eo0IGXWBBPXVVTRUjxWduJ+ioC4WJRaJlGS1BEEE85tqXB0mOoZhHOIUda/5zvy7VbUH+IWI/BaozYlgOyyIimSzTNfFotTXRMeUx1MZhhNpamPO0iklei0Il54/w1k65l4zDONQp6ilo6oZXCqbYD1+OAoOBNFrMJxwLrRcSyeYjK2uOkJVJEKqhHE6gaWzwFs6uVkJMhklXWJAgmEYxsFAKe61u0Xkb+UwH0ASieDda2nqq6PUVztLp6nWiU9g6dTFosSqIiX16QSRa80zvOjkuNf+/fYNfPzGJ8p5GYZhGAeUUkTnEtzEbQkR6RORfhHpq3C7ph2Bey0QlgYfSDDPC8bImD4dKcnSGYi7gaZzGlwdue61lzoGebFtoJyXYRiGcUApZT6dGaoaUdWYqjb59aapaNx0IhKkwUmmxlg68xqdYPSNOAGpDaLXUhNbOoNZ0YkB4wMJBuIp+oaLpbczDMM4uChlPh0RkfeJyOf9+pHhBKCHC1ER0unAvVaVtXTmNlQDo/0z9dnBoRNbOoOJNCLQVOtEJ9clN5hI0Tec5DCbM88wjEOYUtxr3wP+jNEUNgOEggsOF6Le0hkNJAj6dGLUVEWy/TN11VGqIqVFrw3GU9THotRUubpy+3QG4ykS6YyFUhuGcchQiuicrqr/iB8kqqrdQHVFWzUNiUaChJ8+kMBbOjNqq5zoDDvRqfW510oJfx5KpGioqaK6yn0NuaIT9Pn0DSfHHWsYhnEwUoroJP0soEEizmbgsHv1zlo6ybEh0zNqY9TEovR691pdLEp1NJKdV6cYA/E0DTVV2SSfYaFKZ5SRpFvvNdExDOMQoRTR+Q7wS2C+iHwFeBD4j4q2ahoSjQgjyTSJVIb6WFV2cGijt3S6w+61qJRk6QzGUzTURPNaOoOhKRT6Rkx0DMM4NChlPp0bReQJ4GzcVNHvVNUpmU5gOnHC/Bn8ap2bDqh+jKVTRW0sSpvPOF0Xi7rBoQX6dEaSaV7cO8DJS2a6Pp3qUfdaeCK3ILINzNIxDOPQoZTote8Ac1T1alX97uEoOABnvWx+tkO/tjrKHB+11jyjhpqqyGjItM8yXcjS+fW6nbzzew/RM5RgMJGiscbNNApjp6wOi46FTRuGcahQinvtCeBzIrJZRL4hIisq3ajpyOuOm5cVh/pYlOPmN3L7x1/LXxzfTE3V6McYuNcK9en0DCVJZ5SOgQRDcReUkHWvhYRqID46WZy51wzDOFQoZXDoDar6VuA1wCbgShF5seItm2Y01FRx+jFzALIDQ09bOptIRLIhzzDqXitk6YSDAwbihS2dobB7bchExzCMQ4N9mabyOOAk4Cjg+VIOEJFzRWSTiLSIyGV5ymtE5BZfvkZElvntF4nIutBfRkROEZEZOds7ROS/9uEaJsXZJ80HnDUTpjYWsnRiznIp2KeTchZM73CCoYSLXotGBJFcS8cCCQzDOPQoZbrqrwF/DWwGbgG+7Kc6mOi4KG4Q6TnADuBxEVmlqs+GdvsI0K2qx4nIBcCVwHtU9UbgRl/PycCvVHWdP+aU0DmeAG6f+DLLwztOOYJ1rT2ccuSsMdvHWDp+cGghSyfuLZ2eoSSDiRQN1VFEhOpopGD0mgUSGIZxqFDKdNWbgT9T1Y59rHsl0KKqWwBE5GbgPCAsOucBl/vl24Dviojo2LwvFwI351YuIicA84E/7WO79ps5DdX81wWnjtteE7J0aqoiVPlxOqpKbnLuwNLZ3TuCKtl0OtXRSN4+ndn1MQskMAzjkKGUkOnvi8hsn2+tNrT9gQkOPQJoDa3vAE4vtI+qpkSkF5gLhAXuPThxyuUC4BYtkJhMRC4GLgZYunTpBE2dHLXe0qmLOaslFnFCk8poduBnQGDp7OoZBshmNqiuyrF0vHtt0cw6s3QMwzhkKCVk+qPAA8BdwJf8/8sr26zsuU8HhlT1mTzFFwA3FTpWVa9V1RWquqK5ublibYRRSyfo66nygQH5+nUCSycQnUY/yDRXdIbiKUTcBG/Wp2MYxqFCKYEEn8RFrm1T1TOBU4EJ+3SAncCRofUlflvefUSkCpgJdIbK8wqLiLwKqFLVaTHDWRAyXRdzApJNa5Mn0/SopeMGk9b7QaaxnHxtA/E0DdVVzKqvnnLRUVV++shWi5ozDKPslCI6I6o6Ai7aTFWfB04s4bjHgeNF5GgRqcYJyKqcfVYBH/TL7wLuCdxlIhIB3k2e/hxcP09BK2eqCQIJgii2WBFLJz7O0gm513IyEjTURGmqrZryh/+61h4+/+uN3Pbkjik9r2EYhz6lBBLsEJFZwK+AP4hIN7BtooN8H82lOHdcFPiRqm4UkSuAtaq6CrgO+KmItABdOGEKeAPQGgQi5PBu4K0ltH1KqB3nXhufwDNgJOlEp9/32QRjfnKj1wZ8BuqZdTH64ykyGSUSmZoZw5/Y1g1AS1v/lJzPMIzDh1ICCf7aL14uIvfiXGB3llK5qq4GVuds+0JoeQQ4v8Cx9wFnFCg7ppTzTxU1oUACgFjED/bMIzq5c+MElk6sKkIiZBkNxlM0VFfRVBdD1YnUzLpY3vOrKv9yyzre/Zojee2x8yZ9PYHovLDXpso2DKO8lGLpZFHV+yvVkIOZIJCgNjbW0skbSJBMj1kPotdqohESqdGyoXjaude80PQNJwuKTjyV4VfrdjG3sWbSoqOqrM2KTn/esG/DMIz9ZV8yEhgFqM2xdLLRa/kCCXItnSCQoErGTFcdpMgJprK++fHt3PTY9rznH0o4sWrrj0/mMgDY0T1Me3+c4+c30j+SKkudhmEYASY6ZSA3ZDoYp5MsydLJ36czmHDTHjTVOVG6+t7NXH1vS97zD/nsBcH0Cnc+s5utHYMTtrtzIM4HfvQY7SFhCVxrF6x0Y5te2Ds9+nWe3dXHJ256qqR5igzDmL6Y6JSB8SHTxaLXMtn9q6si2X3zDQ5tqKli0cw6ABqqo/SP5M9MEAhZe3+cVDrDP930FD9+eOuE7X5mVx8PvNDOxl292W1PbOumoTrKX71yEQAvTpN+nQdb2ln19C52dA8f6KYYhjEJTHTKwGjI9Ng+nUSB6LUFTS6xQxBEAPnG6aRorIly9LwGHvg/Z/LB1y5jIJ4iXwKGsHttb3+cZFrpHExM2O4g60GQ+Rpg055+XraoieYZNcyuj/HiNIlgC1IB7ekdOcAtMQxjMpjolIFx7rWspTNWdFSVkWSGBU01wGi4NDhLJ+jvSWfcfkFetqVz62mqi5HOaFZgwgTbBuIpXvTusO59Ep3ROrd0DHBMcwMiwvELZkybCLZggGxbv4mOYRzMmOiUgdyQ6apQ7rUwgeUzP4+lE074GWSYDqbEBjctNpDXxTYcEo0nt7tkEd1DpYtOcHzvcJKOgQTHNjcCcGxzA9s6J+4bmgqC6zZLxzAObkx0ykBun04QvZbb6R24sRbMcKKTa+kE+wdi0FATFh0XxdafJyXOcMj6eWq7CwQoydLxxwXHb2l3Vs0xXnSaamMF+5Gmmj6f9HRPn4mOYRzMmOiUgaAvp7Z6bO613ECCIAVO4F5ryLV0UrmiMypKgaXTl8/SCYnOOm/pdO2DpRMkId3c7qyaY5obAGeJxVOZaRExlnWv9VkIt2EczJjolIG5DdXUVEVYMttFmlVF8o/TCZJ9zmmoJhqRMe6zWMjSCebSCbvfmrLutfGWzlDIvdYfCg4YztP/EyYrOiFLpyoiLJ1T787vzzkYP/DWTjaQwCwdwzioMdEpA7Mbqnn8c2/ijSe4KRSqq4LotfyWTm0syuz6WPahDs7SSaaVTEYZGCnmXkux6uldfP5Xo7M9DCfyi8JE/TqBuAV9OlvaB1k6tz4bCBGcP9fFtnZrF5+6dR2ZTP4puStBYOkcCn06373nRR7f2nWgm2EYBwQTnTLRVBvLpovJWjoF+nRqqiJ89W9eycdeP5pCrtr3CyXSmaxYzK6vzpaHAwl+v3EPtz0xmgF6OOHqXegDFI6Z59xjE4lOMKg0KzodAxwzr3H0nF50BnIsnXs3tXH7kzt5aQqDDALha+sfyRs2PtVMZtDsd+5u4VdP5c7yYRiHByY6FaBQ7rWwpXPO8gWcuHBGtqw6FHzQkxWd0Vxr4UCCjoE4w8l0VjSGkimqoxEWznSi84ojZgLQPVh8SoSBkCsunVG2dg5xrO/PgVFLJ9e91u2nWtiwo5epIJXOMBBPMbs+RjKtdJUQJFFJntzezV9e9QD3bmrb52PjqTSJdMZmg90H7n5uL3/9vYdIT6FlbVQOE50KELincidxC1s6uWQtnVSGHv9QnxWydBqqo0TEvfF3DriHbvB/JJGmrjrK/BkuQOGVS5zoTBRMEA6Z3tk9TCKVyQYRwGifTn+O6ASiuL5MonPr46188uanCpYH4nj8AifS+9qvk0pn+NJvNrKzpzzZDJ7f7aycOzfs2edjA9epiU7pPLm9m6e292Q/O+PgxkSnAmTH6aSVb/5+E8/sdA/nYBBmEO0WJhAq515L0lAdzQoRgIjQWFOVtXSA7Bv/UCJNXSzKfB8VF1g6PRO611x7RhJpOgddncEYIhgNZMi1dAJR3LCzlAlk3XXf8PDWrKWXy8ObO7jn+cJWQxBEcMIC5/rbu4+is6VjkOsf2sq9Rc6xL2z1bsW7n9+7z2/fgYD2HQai8w//+wRX3vn8pOsJvv/++KH/mR0OVFR0RORcEdkkIi0iclme8hoRucWXrxGRZX77RSKyLvSXEZFTfFm1iFwrIi+IyPMi8reVvIb9IRin0z+S5L/vaeF3G3YDoxmm84lOIDDJlNIzlBhj5QTMqI3RNZTMureyopNMU18d5ag5DdRURXj54qYx5YUYCFk6wfKMUPBCIDq5b5jB+Z/Z2Tem3+qp7d288ev3jhO7mx7bzhdXbeTxl7rztqN/JJU30u7e59u44NpH6Bl29R0/31k6e/cxbDr4HMo15ugln0y1YyDButb811SIoA35Qt8nQ9dggie371tbKs2al7qyIfyTIQgiye1bNA5OKiY6IhIFrgbeAiwHLhSR5Tm7fQToVtXjgKuAKwFU9UZVPUVVTwHeD7ykquv8MZ8F2lT1BF/vtJvjJ+if6fJ9KoErJbB0irrX0mm6hxLMqh8/d05TXWxMhoAgv1rgXnv/nx3F7z7xembUxmiqrcpaJIUIp8HJNyA1cK/l/th7hhLUxaIMJ9PZsT0Aj2/tYmvnEBt39WW3ZTLKDT75aL5wb7c9RSqjYxKeAvzpxQ4e3dKVTcVzbHMjIvsewTYqOuV5U97aMcgZx8yhKiL8/tm9+3Rs8FmW2732P/e1cNEP1kyLIAtw91TXYKIs/W/BZ3Wwu9cyGeUf/vcJ7tuPvsBDiUpaOiuBFlXdoqoJ4GbgvJx9zgNu8Mu3AWfL+BnDLvTHBnwY+CqAqmZUtaPsLZ8kQSBBED0W/GiKWjpBktCU0j2UHBO5FjCjtoqXQg/5Lu8SC9xrtbEox813LqjZDdUT/uCzGQmSmewbeHhsUDCOaCCeIp5K0+ndet1DCf7s2LkArN8x+ia7q8eJQZDZAOC+F9rY2jkEjO8bCgjeZHOtnV2+DybIgj27Icbchpox+dc6BuJ88dfPjJsyIkznPlg6j27p5J1XP1SwvkxG2dY1xCuXzOLVR83m0c2dE9YZJtynM1mBSKQy9PoXixfbBnxwSfGxWcCUCFPgAi0l8SyMn/IjTOCKLKelo6pc+8BmdnQPla3OidjSMcAdz+zh7ucqLzqrnt5Vtj7MclNJ0TkCaA2t7/Db8u6jqimgF5ibs897gJsARGSW3/ZlEXlSRH4uIgvynVxELhaRtSKytr29fXJXso8EfTrBQ7+vBEsnmKytZyjh3Wt5LJ3aqjEP7iCQYCiZziYbDZhdX100ZDqZzmQti7ClExadaESor44yGE/xwz+9xJv/6wFGkmlGkhlOWzqLhupotr8KRkUibP38bE1rdmBroUGmgRgMJceW7+oNRKfPX3+MWfWxMVbCz9Zs54ZHto0Jakh76+pN37qfF/f20+U/p74SLJ0/PruXda09Ba2pXb0u4GLZ3AaWzqnfZ1df8OBMZzQr+vvLP9/yFG//7p9Q1azLr2cCCyqRyvCuax7hq6ufm7D+4USa9/7g0THfcans9p9f91BiwvFc2zoHOfWKP3DnM7vzlgeuyH0VnQdf7OD3G/MHe7R2DfMfq59n1dO79qnOyRDMVdVaYaGLp9J84qanuP7Blyp6nv1lWgcSiMjpwJCqBiMhq4AlwMOqehrwCPCNfMeq6rWqukJVVzQ3N09Ngz0iQlVE9snSWTzLZTPY1TtCz3AhS2dUiERy3Gs5dc5pGBWddEbHPTjCAjCcGO3TCbvXgvWBeIrN7QN0DCTY4gVldkM1Jy6cwfN7RserBA+aLaEJ5Fq7hlixbA5Q2D0SuL0G47mWjqvvuUB06pzbMGyxrPb9ZYHVN5JM86HrH+OLqzbS0jbA41u7s59DKZZOcD2F3tC3drgHxrJ59cybUUPHQHyfBsmGXxom42K7d1MbqzfsobVrmJ09w7R2uXZNFDzy3XtbeGJbd/YBWIyXOgZ5eHMn97+w7y9tu/0LQzqjE4r9jx58ieFkmjue2UM6o3ztzufZHLKW+/bTvfade17kW394IW/ZJj/OaiqjCNdudZ95peeECl52w5/hdKKSorMTODK0vsRvy7uPiFQBM4Gwv+ICvJXj6QSGgNv9+s+B08rX5PJRFR0vOsUsnWCMTWvXEL3DyTFjdAJmhDIYLJ1THwokSI1JHgowqz6WHadzxzO7eft/PzjGlRC8ZdfGIj6QIE11VWRMxBy4wIKBeDo7u+izu50AzK6v5sSFTTy/pz/rrgkeNJvbRm/23uEk8xqrqa6KMJAnc4KqjgY0hN78R5LpbJRefzyFiGtLU10s+xBqaRsYIxKpdIaP3/gkD7Z08OXzXk5EYE/vcMi9NvqAeWFvf97pv5/f466vUMLUYEDs0fMaaG6sIZXRcQ8uVR03MDgg/ODsnaDPrRDDiTRfWrWRBv+d37VxL4HuFXuIPt3aw9X3thCRUau0GMHnv71z9L75xRM7OP+ahwteX8DukKVYSMDXbu2itWuIW9e6gc4PvtjBQy0dfO++zfxu/ajVs7+BBLt6hgu+aASDe6cyivCJ7YHoDE3KxTmRiAcekC0lzB58IKik6DwOHC8iR4tINU5AVuXsswr4oF9+F3CP+m9DRCLAuwn15/iy3wBv9JvOBp6t1AVMhlgkMurWyYpOhupohEgkt9vKWT/zGmt4fk8fqhSIXnOiU1MVYemc+uyPeTiRoa56rIUyJ+Re2+YfGuE3rMDSmddYw0gyzUA8Oca1FtBQU8XAiJvyANy00eBE7WWLZtA7nGRvX9yLhAsw2NU7nBXYnuEEM+tizKipyuteG0yksw/MoZAo5bq3GquriESEptpY1t2yesNugh7AroEEa7d1c8/zbXz2rS/j/X+2jOYZNezuHclaQeEH0Pfv38Jnf7lhTMhze388e52F+sO2dgxSG4uwYEYt8/y4qPaBURebqvKvP1/PX333obzHD4TCfnuHk/ziiR37FHWmqnz2lxvY1jXEN999CgC/Wz/qIiokZD1DCT5+45MsmFHD+884ij19IxMKRyA627pGH153PLObx7d282BL8a7U8PeX77NsaRvgXdc8whu+fi/DyTQXv+EYOgcTfPUOF2Id9AnFU+ns+LZ9iT5MZ5S9fSMFReWFHEvnugdfys5FVQm6Bp2XYGFTLSPJTPa3u7l9gC/8+pkJv4uAbZ2DnHbFH3i4yOcffN6tXUMFhykcSComOr6P5lLgLuA54FZV3SgiV4jIO/xu1wFzRaQF+BQQDqt+A9Cqqltyqv6/wOUish4X2fbpSl3DZIhVRbLWRN+Im/EznkrntXICjphVm+2/yNenE7jX5jXWMLehOvswHU6kxrnXZjdUM5RIM5JMZ3/Abf2jD8eBkOjEUy6QIJ/oNNZUMRiydJ7zls6sumpO9IM1n9vTl33InH7MHFSdayZ4YMysi3nxGv/QCFsf4cSlwZv4Ym8BNtXF/P+q7IPk7uf2ctrS2TTWVNE5mMhaWmedNB+AhTPr2NM3kn3zCz+ANuzsIaNjLYPAyoHCA2u3dgyybG4DkYjQ3OhEp6M/znO7+/jZmu386KGt/OLJHTy3uy9vGPgYS2c4yeWrNnL9Q1vznisfP1+7g9uf2sk/n30C575iIUfOqcvOoQSF+3Q+88sNtPWPcPVFp3HSoiYyCnv7i/dHBaLT2uU+V1Xlad93Fk7DlI/dvSNE/ctV8PmHCe7JM46ey0dfdzQffd3RwOj9FZQHY3RgvKXTP5LkJ49s5eHNHeMerh0Dbgbd/nhqzItF4H4MIiJ7hpKMJNN8+bfPcvW9LUWvaTIEU46845TFAFl36O1P7uAnj2zLehAKEfy+1m7tJpVR1u0oHIoeiE5Gx1qp04WK9umo6mpVPUFVj1XVr/htX1DVVX55RFXPV9XjVHVlWGBU9T5VPSNPndtU9Q2q+kpVPVtVx/tIpgFVIWsmnXEupJFkhpo8/TkBR8yuy1ojhaLXAOY1VjOnoYbOgQSqmh2nEyZ4ILb3x7M/4Pb+uHtwtPaELB13no6B+Lj+HHBh073DyazAZd1rDTFOWujGA23a05/t9H/dcfMAlzw0eKBnRcf32bR2DWUfIOG31/BDepf/ka08es6Ya59RG6NvxEV+7e4d4bjmxmz/VTDtQTDAdVFTrbd0xvbpDMZTtHgXYPgtPMg0EJHCls6evpFs/1tzyNL55u838ZlfbuDLv30229btXeN/8P0jqezDuLVriP54io48D/+WtgG25nGPrH5mN8c2N/BPZx0HwPJF7jsIvv987rVMRrn7uTbeu3Ippy6dnW3/7glcbIHVt6t3mHgqzZ6+Edr748yqj/H7Z/cWdQ/u6R3heB9Jme+zDKzwy9/xcj739uXMb6rlJJ8Wal5jTTZAI+xKyrWUf/inl/jCrzfy3h+s4XO/fGZMWThyKxD6zoE4K79yNz9bsz3rAu4dTmatjvteaC/Z4shHMZfZutYeohHhrScvAka9Ds/sdL+np1sLi8jGXb2c8dW7eWRzJ8/4SM5wFGsuYXfm5iL7HSimdSDBwUyQYSCgdzg5oaWzeGZddnlCS6fRWTJ9wylUGRe9FkyzsKN7OPsDbusf4b5N7Zx39UOs2eKyHM9tCN7WE2MGhgY01lTR2j00rs9gVl01M+tjLJpZy/O7+7Kd/q89NhCdgaxl0RRyr23rHOT1X7uXky+/i0/f+vRYSycx3tJ5jRedrKVT6/KvjSRduqDZDdXM8eHhe/viNFRHsxbbwpm17O4Zzj7gBhIpMhnl2d192esJPxCf29PHgqYaFjTVFhSd3uFk9rsJC/tLHYOsXDaHz771ZXznglOB0cwFYfrjqWxi1iAUvGNgrOj0jyR5z/cf4YJrHx0XSryze5hjmxuzLtrli1z2iRMXzqA6Gsk7NmtnzzDxVIaTvEAF1uNEIbWBGKq6++jpVtfeT//liSRSGX75VGFrZ3fvCMuzg5THi+poqqfR+/zdK47kz4+by1knNWdTHYWt03AQRjqj/HxtK689di6nLZ3FC21jO81394y69wLh2to5SCKd4et3PU8inSEWFXqHk9n+u56hJE+FHv6qyidvfoqfrRl9rx1JpvnfR7exesPuMd/b9s4hXvHFuzj9P/6YNwvDts4hjphVlx3SsKN7GNXRAJ91rYUjBBgOA5EAACAASURBVIMXpPs2tbHRi1Sx/pruwUTW7TwdgwlMdCpEMFYnoHc4STyZoTZWRHRmjYpOcUvHudcAdvS4t+lc99oRWdEZoi1k6QQ3YZBaf96MsKUz3gprrKkaN/ajpiqSFbmTfARb8NZ8THMDi2fW8lLHqKUzq76ahpooA/FU9g1vwYxa7tvUNmZkfniKhl09w8xrrMlOnR2ElDfVuc9gd+8wiXSGOQ0x5jZU0zmQoK1/ZEwan0UzaxlMpEmmlYVNtag64QmHV+daOictbGJ2feExTr1DSWbVVWfbUh2N0NYfp7VrmFOPmsXH3nAMpy2dDZB3qu+BkRQLZ9YSkdFQ8FzRueb+zXQOJtjTN8KP/cBacA/BXT3D2e8WyD7Yj5nXyMz6GL3D49sdhFMf7bOPLwoiJXuKD7JtH4hnLfbtXUOs39FDVUQ4/9VLeM2y2Xz33s15++niKRcEctSchqzrM5fAzRUWnQ+/7mhu/OgZLJxZR8dAnFQ6k70/RNxn93BLB2++6gGuuX8zu3pHeN8ZR3H8/BnjAiPC64HojIZxu/VXLpk1xtIBxoyheXJ7N79et4vfPzsadv2LJ3fwuV89w8dvfJJP3/p0dvuv1u1kKJlmVl31GJEK2NE9xJLZdTTWVDG7PsaO7iHn+vUC8XQRd1ngWvvTix3ZF5WXiohO52CCuQ3VLGiqyUabTidMdCpEVSSP6KTSecOlAyYSnWC8y7wZ7u0eRs30XPfaopl1iDgXTtCX094fz/qSN/g3rMDS6RpK0Fg73roKu9wC6yncthMXNrG5fYAtHYPMa6ymNhZl8aw6dveOjHOvDcZTWavj1ctm0zmYyAZbQI6l0zvC4lm1HOknlAvEJhCfIDhidv2opdPWF8+6vGA0IhDgqLmunr7hJBt29GQtzkBcBuIpXmzrZ/niJuY2jhWdP73YzsZdvSTTGfrjqeyDUkSY11jN+h09JNJu7A7AzPoYs+tj2UGxYQbiKZpqq5hRG8u+AHQPJbMT+K3f0cN1D77EO161mLNOms/37m3JurF6h5MMJtIcEbpPgpRHxzQ3MKsultfSGZ2GfHRG2Jl1sWwfWMDGXb2s2TIaPNoxkODlPo/f9s4h1u/o5cSFM6iNRfnMW19Gx0Cc7z+Q2+U6Orvrolm12e8ml+6hJPXVUWqqxv8eFjTVoOrOH1g682fUMBBPsXZbN5v29vP1uzYxp6GaN71sAYtn1dHeHx/Tr7MrdG3ZCQD9w3tmXQwRePVRs+kfGXVvHjGrjnueH80wEfS1tYbcpHds2MOyufWcs3wBWzpGrYjfPL2L1xw1h7999RH0DifHuR53dA9nv7cls+tp7R7Outb+4oRmNrcPFIxKC8Ty2d19DCbSHD+/ka7BBNs6BznnW/ePm5upazDOnIZqjpnXOKaN0wUTnQoRuNcCMegbTro+naKBBO6mjMjY8OiAwL02t8G512BUdHLda9VVERY21bJ+Zy8p70tq64vT6vcPHvBBBJYqNOaxdMLteNUSNzY3/HZ61knzSaaVXz61k0XePTi/yWUNCIvOjFo33idwZbzM++/DP4rBHPfa4pl1LGyqJRYVZnr3WtCewHU1u76aOV4k2vpHspm2gWx7YPQtv38kxfqdvZxxjBuDHIjgwy0dJNPK64+fN8bS6RtJcslPn+CqP7yQfQCGr795Rg3rvEsmEDa33JC3E3cgnqKxNsbMuhjh4T2dAwn+577NnHf1Q8yojfF/3nwinzrnBPpGUvx2g4tOC9xhYdFZPKuOa973ai46fSkz62J5+3S2dAwyo6Yq6w4Mjsu1Dq68cxMX/XAND/nIqI6BOCctmEF9dZSXOgZZv6OHV/p74NSls3n7Kxfxgwe2MJxIs71ziG/+fhPJdCb7kFw0s7ZgZoyeoSSz6sa/5ICzgsH1nwUP4sWz6hgYSbG3b4QZNVW89ti5fPyNx1JdFWHxLLd/2KW2q2c462IKWzr11VEue8tJvPOUI1jgreLAIn3nqYt5Ye8AHQNx9vSOcMcze6iuitDaPUwm46bUeGRLJ289eREnLGhkd4+LANy0p58X2wb4q1ctys66Gx4AOpJM09YfZ8lsV7Zkdh07uofYsLOXiMAFr1mKKjxTIGv7nlBQBsA7XuWCEa59YAsvtg3kEZ2EE53mBra0D06b1EgBJjoVIhCdI/2N1jvsomSKWzruRzCzLpY3rHrpnHped9w8XnvcXOZ4CyUYe5PrXgN3cz/pBwHObaimfSA+5q0tIoz54TdU5wmZDonZyX7KhPBDd+XRc/jHM48d0/7m/9/emYfXVV2H/rfuPOheSVeyZMmabMvyhG08YiYbghkDdQJhCgXapJlKZpqW13wvL0n78jXkpenwSPOSQBtSMrQZGprBJE5CQgIEDBgjMAZsjAckT/Igax72+2OffXTupMGWri5m/75Pn662zrl33X3O2WuvYa9dEuZgZ5876y6NBomHtNIx9ehanMw346/2yYh7zbiRasui+H3Cl29ZyZ+ep7ObTGzHtXTiISriIfqHhtl3tMcdSEAPeoZGxwppP9HLrkNdrGp0st4cS+s3Lx0iHvKzqjGlExOcgfIHT+2ju3+IQyf73cywUk+f6ZRzbaUYS0e/juWO6ThZgqUZA+7hk33c9/tXWdOUYvPH11OfirG4NsnsyjibWrV7Z78zYfC61wCuOGsmZbEQZbF8lk4Xc2bE8VaYqi2NsD/DvXaos4/BYcX7v/kUuw930dHVz4xEmIZUjG8/sYcTvYOsbxlZaH3dijp6BoZ4Zu9RHvjDa/zzr17h//1mJ795SbuoZpVFXddnJvmK2sKIhXrgxMjEpbYsysm+QQ529jGrPMq33rOWP3M2QTT94VWibcd7me1cDzNZaD/ey8zSCDevaeBLN57tXoNdh7sI+IRz5+h45Ittnfx42+sMDStuW9tI/+Awh0728YsX9OLVq5bUUFceY3BYcaCzj/9+9nV8AlcuqXEtc+9zZpSw8RTUOQlDD+84SHNVCWvn6LilNyNNKeUuT2g70cvqpnJiIT8hv4/Lz5oJjGQQtmVcxyOu0ilJc1EWC1bpTBEmpmNuNO1eG93SScVDhAO+nK410Gt5/v3PzmHBzKTrRjLZKbEcCqOuPObecGfNKqWjq589Hd2u6y8eDqS55UpyWFfG5RYO+NytBUxMw/CxDS28Y2Wdm5lTlYzQ2TvoJjAkIwHiTmzoSFcfiUjAHSh2HurC7xPKYiHX+mrdf4Lu/iEW1mjFdOmiahocK2LEvdbl9plRwIPDKs3SMVs9gFYCMDKbbK4qoTwe5Gi3zgB8eMchzmuuJBTwkYqH6OwbpHdgiPsffw3QmU+59jky1yHsWJaGxoo4rx/ryUrlPdk3QDIyonRM9uCejm4OdfaxrmWG+z8R4bLF1Ty28wjHuwdyWjpeSqOhnJbOq4e7XEvPkMvS6ejq49w5FXT2DfLdLXsZGlZUloRoSMXoGxzmxlX1XOEMeAArGssRgSde7eBRpwbdP2x+mXt+vZNrl89idmV8FPdaP+Xx3JaOuW56nY3eoLAyHtJK50RvmgvV2x/exIjXj/W4mySaZ6DteE/aRMT08+4jXZTHQ+799mL7CbbuPcassigXzNOKaE9HN5ta26lPRVlcmxxJ1Ono5tGdh1nZWE5lSdhVOt7MRTMxNOdctaSGaNDPtn3HOau2lLJYiKaKWFoG26bWdq76p0d4du8x2o/30JCKsb5lBisay5hdGSfgE7fCSfZ11ErntnMbaf3M5VkTnOnGKp0pIuhsWV2VjOD3ybgsHRFhVlk0Z+ZaJiXhAE0VMdeSiYayL2WdZ0a8xPHN9w0Os9opSxMPBdLkybdOB/TgatxVmYNFwO/j/1y/jI1nz3KPBW3FlIQDBPw+1y22/2gP5bGQa5HsPtxFiaP8TMr0T1vb8PuEDQuzy+qZ2M5rzkOdioVIeeTxKppwwO8mXBhLZ5sTy2qoiOm0865+dh46yf5jPVw0X8/iTbxsU2s7uw51UVsa4fDJPjf47X2IzXdtrIilWadNlTGGVfqC3IGhYXoHhtMsnbPrddKBuY7GPWO4YvFMBocVv3zxAK8f6yES9LnyZVIaDWaVwenpH2L/sR7mzChJa68ti+oYUd+IddnR1c/ZDWXUp6JuzbLKRJjLF8/kskXVfGbj4qzPWzAzyebtB2h9/Ti3ndtIaTTI8oYyPnftEkTEWU/W77p4zALMYz0DWZMXQ0U8jN8nWun0DpCMBihx3LMHTvSlWbMwYhmZxAizUNlY02mWTnLkmTDX4NVDXaRiISpKwlQlwrzQdoJn9x1jWX2pq0RePdTFH17t4C3zqxAR11W2p6ObHe2dLK7Vz1cyouN56Uon3UJd3lDO7/7qYv5m42LucFLfl9WXudmBgFu9fMtrRznY2cfM0ihfuvFs7vuT1QT9Pvc+SYQD7vIC0BsWHuseIBUPE/T7yK6fPP1YpTNFGEvHzGrHY+kA3Li63h28x2JpXZm73iUazGXpZAecAS5eoAfXeNifFgsaTelUloTdlO58bhFDlat0Ot0H2yQk7D3ardOcYyECPmFwWJGIaKXT3T+EUopNre2cN7eC8hyDq7F09nZ04/cJiUjAtXRgJB5gmFkaIRL0ucroeUfpNFbEScWCdHT18cjLOoZhXEdmUP/+0/sI+X1cv6qe3oFh9+Euy3CvmffzYv72ZrC5RVUjAddNuLxBx0i2OEqnKeN9ltWVMTMZ4Wet7ex3XI75BpKyWNDJ1htZa2KynLw7wsKIK9TMkk/0DjIwpKiIh1jRUO5a0JUlYa5bWcdXb1uVc8K0pqmc1v26isY1y2rZ/PH1fPe957rHphzX58m+QZ56rYNLv/Rbnni1Q8d08kyu/D6hKqHX6pzoGSAZCVISDuoqA529VCfTLZ1wQO+aa75Lu8edlQjrWn1DjivMa+mYz+/qH3Kv+YKaJI/vPMLejh6W1ZUxq0wn5Dz0fDvd/UNuCn9tWQQReGzXEbr6h9w1RgD1qRh7Orp5+UAnm1rb2HdU36teSzgRCXLruU1uduayujLaT/TSfryXoWHlbn/w8I6DKKVdxZGg3/VozK6MEwv5ueKsmWkJIcYFXJFnYlIMWKUzRZiN3BKRAMlIgOM9g2NaOgDvWz+X289rGtdnmG2pITuRAHBnY5UlobTMuPObKwn69U6kXnnyLQ4FPaNPRgN8+JJ5XL20ZlS5qpyBf09Hd7bS6eghFdMxK6OcEpEg0VCA7oEhdhzo5NXDXWluHC/hgI+Q38fAkKLMiX15H7CqjAGppjRKRTzsWlqvH++lsiRESVgrq6NdAzy3/zhVibDbX2YAenTnEc5uKHOVt1lQmJlIACPuO4OJJ+xoH0mU8G4fYfqlxQnUm1TYhoz38fmEty6t4eEdB2ndfyKva80rl9fFZhI15lSmWzrG3Za5SDYVD7Gysdw9rrIkvT8zWTNbJ2REg36W1ZVRHg+l1e8zfdnR1c8zTuWE7W0n8lZSN1QlI46lM0giGnTvQ6VG7i8vtWVR171m6vE1VsRJRAKccHbbHRpWaRmNXos15bg5F9Yk3MnFsvoyIkE/1YmIW/R0VaNWOuGAbje70Zo1UKCVzt6Obj7z3y9wx7ee4anXjlJTGnHHhFwsq9eTj2f3HWPr3qMc7R4gFPDxuJNN6JUb4GOXtvDPNy+nqTLOse4B10vgvY7FilU6U0TQcbUknEyl8Vo6E8HcqJCdMg0jlk5VIpLmB2+qiDOnskQP9sExYjpORltlSRgR4eOXtriuhHyYgX9YjTzYZuFpz8CQG7Mya2oSkQDxkJ+e/kE2v3AAEbhsUW6lIyKui81YQt4HbEbGgPThS5r57MbFhAN+t++NayIVD3Kkq48X2zpZ6Bk0jBIbGlacO6fCHXh3HjqpC49GxrZ0yuMhFsxM8NuXDjE0rBcZmoSARCTgfof6VJTKkjADQ4ryWDCn//3mNfUMDCn2dHSPqnTMuT97ro07Hnia/sFhfrn9IIlwIMvSaalO4BPY7gzQZgFnyrF03P4cQ+msnl3u/E5lFYsFbyHbHjcw3rr/OMMq97IA97xk2K2dZmr3GTItHdBxHWPp/PyFdtfNZwrEejPqDN6+Ntd8oVNlwycjLumGlE4aqCuPpg3+deVRjnYPIIJbEsocv/doD4/uPMzQsOLxXR1pXodcLK5NEvAJz+49xi+3H8TvE65bMYuBIZUlN+gY7SULq0cs1uM9tO4/7n7PYrZ0skcZy6QQ9Fo60SDb205wvGcgzeI4XRbXJvGJHtxzWTpmrU51MuwOjhXxEPFwgM9du4SgX9KVTk73mll9P/6bOBUL4fcJQ8Mqy9KBEWVhBg+z/uj1YwPsPtJNdYaSzCQZCXL4ZD8pZ9DS6z18ae9lMCm+oJVF38k+V0Gk4jrz7KUDnVzYUpklH8DaORXuotldh7pIRoJp6auLapNsWFidltVluGh+FV9/ZBc/fa6NH219nd86s+WScJAVDeUsqyulqSJOZUmIPR3dWYrL0FyVYHVTOU/uPjoupfOPv3yZwyf7mT8zwU+ea+Om1fVZFnYk6Gd2ZdytdWYyzCriYRbMTBAN+hkaVq5yzEdVIsKfnt/Eunm5tw9ZVl+GT/RiZFNCyaSYj+amrU5G+P0rRyiNBt1FlYbMiQVod9fm7QcYGNKK9pIFVQT9PqdA7ADtjgvKqzQiQT+hgI/+wWFXAZrJx7yqhHvP1qdiPLG7w42FGurKo2x57SizK+Jpz19DKubWe5tfnWDHgU5mlaVbsJlEgn4W1CT43SuHOdzZx+qmctbOqeDbT+gtyWqSua+7ibNu3XOMT3zvWdeFl5rA81porKUzRZiYjrF0DnX24fcJb1s+vnjNeIiFAm6wNFfKdCjgY15VCfOqE25WVp0zy1/ZWM7SurI0yyuX0knFQyysSbIy44EbDZ9P3KwsMxB63zvlKh1j6Wj3Wo9TnDTXTNaLcZWZhAYTsK5ORkYNnBqFZNbTmNng4LByZ7igYzYiuv+WN5RR4Sjs/cd6slxCJeEAX799lRtw9nLx/BkMDis+/eDzwMhK+JJIgLVzKvjRBy9wq4t75crFzWsagOx0aS9mED98Uq9y//tfvET/4DDvPKch5/ELa5JukVPXLVMSIuD3say+lIqS0LgC0f/rmsVc7BRZzSQZCbKoNskjLx9yXXmvOItV863TAZ0GfrJvkP3Hekg6i4sN+SydvsFhftbazvGeAS5brC1lXSB20GPppPefuT/Nurc5M+KEAz7O9ngR6lP6nFVN5WnnGnfsgppEWruxpM+aleSvrpzvHDv2ZHNZXRnb9h3n8Ml+/vKKBa5HIRr051X+Js76n0/tZViN1Cy07rU3IV5Lx9zYF8+vysq8OV2W1ZURCviyar0ZvveB87jzshZAF4dc7nmYQCsIo3hyKZ1QwMfPPnJhzpn8aBi/uxmkve9t2qo97rVY0E93/yCHOvvSStnkwgThvQ+WyTwajUSG0vFaNN6BI+D3URoNsqJB+/S9rorRBspMVjaWk4joMjDeQSyznysTuV10Xq5eWssnr1rIpYtybpSbJdsnr1royrDAo1C9LKxJsrejh87ekVIw5rt+4vL5fOrqRaN9vXFzzuwKnt5zjMFhxayyKGatYr6UadA1/N63Tq/DSUaCaYuUc1nBc52aZnd9fxuRoM+9X0csnV5CAV/WPlWlGfdS0O/j/net4ePOMwPaWhHBXVBsMIoks3/1mii4dnkd61uquOPiuW516dEwSu2zGxezoqGc2ZVxokE/NaX5J1PVpbovHt/VQXks6Fo6o7kupxvrXpsiAm5MZ0Tp3LS6frRTTokPvqU57ywTRrK9AO5/15qcx0RDfvoGh3MmEpwqRgEkXffaiCVm3GIjiQQBfDJEd/8Q/YO9WTPKTMx38rpn7rpyQZrbK+d5jiwj7jUz0IibRWS489IWty0S9OssqL5BSifwMAf8Pta1zOAn29q4+x1Luf4rj3G8ZyCr2oSJm2QmI3gJBXy8xxmE82Hus4ZUjHc7WwV4kwIyMetSdrR3cuRkP7GQ33XDrWwcv2U7FufMTnGvs3XyVUtm8rVH9OuxsiDvvGw+Xf2DXLqo2lXU5bFgztI5FzRXcvd1S/nGY7tZ3ZRy3V0JZ6fZPR3d1OYYvF2l45HlnAzlcvnimfzqzouy1jqZ+2hRTbrSqSmN8tMPX0hLdQK/T/jE5QtG/Z6Ga5bWsmBm0nXx+X3CisaynJmphnBAW8qHT/ZxXnMlt65t5HcvH847CS0GrNKZIkymSjISZF3LDF470u2uA5lM6lOxnK6dXOSqcgDafD/GQM6Cn6eKSSZw3WsRr6WT7V5TSqcUD+fJTvJiXA3egeL85sp8h7u4lk4qPUutuSqR9ZDeem5T2t8VJXrB6EQsHYCPbZjH+nkzaKlO8JYFVfzwmf2jWDrju475MNt5v335LETEXbGfDzND3952wq3XNRWYWEgs5Gddy4wRpTNGX4YCPv72bUuAkaKo+TwFIsINq+u5IWNil4xqS+fJ3Ue5oLki6zxX6YwSA/H5JEvhgFamX/njlTknfQtrcluXoxHw+7LO+/ItKxnLw1lbpteRnT+3krVzKrIssmLDKp0pIugfsXSK/UaIBnV5jVwzyFPFzN7NQx0O+An6hYEhlSOmE2BoWLm1yMaO6ej3zLWOZzTKYyGSkYD7+Sk3Yykx2mmAdt/tPtI9roW7XpqrEjRX6fe/4+K5NFeVZFmU6+ZV8tYlNWNmBY6F3ydsvnN9mjIejZrSCKXRINvbO93KxFNBeTzEopokJeFA2uA9kZXyRlGP5XrNJOlMaA6f7MuyYLwynIrC9fkkb2r/ZDGePqopjbBt33F3L6tiZ0qVjohcAfwj4Ae+rpT6u4z/h4H7gZXAEeBGpdRuEbkF+ITn0KXACqXUVhF5GKgBzIqoy5RSBykyzMw5V5yk2AgH/TnTpU+HGcmROnKGeDjg7IGj2+bOiPOu82dz0fwqHmodKR8/ZkzHkTU1SkwgFx+4aC5vc6wA8z7nza1wg86jYQbkiVo6XrwKyEtjRZx7bllxyu/rZSwr0YuIcNasJM/sOYZPGDMmdjr8yx+vwCdCTWmUkN9HJOgbdd1KJuGAj6BfJiyjNwBvNgT0Yu7PYo6BjMX5zZV09w9lrfEqVqZsRBQRP3APcCmwD3hSRB5USr3gOezdwFGlVLOI3AR8Hq14HgAecN5nCfBfSqmtnvNuUUptmSrZJ4OGVEzXSCpi36ohGvRNqmsNRuIT3vUFJY7SMeVPAn4fn7pGB6u964wyqwpkkjzFgaKuPOZmHIEedL/1nqzNaXNiMtgmEtN5I3B+cyV3b9pBSTiQN+FgMvAmSdSnou76k/EiItywqp6L5uePX+bCxP8qS0LMyeEie+vSGqIhf1HHQMbitnObuC3DHVzMTOU0fA3witmCWkS+A2wEvEpnI/Bp5/X3gP8rIqLSa3HfDHxnCuWcEm47t5Fb1zZOtxjjIhryu+txJosLmivZ9NELmedZNFcSDpAIB3IuIvSuc8isKpBJQypGyO8bNX14sjHrlE7H0ilG1s2bwd2bdnCyb9BNG55qltWXuVW8J8L/fvuSCZ9jJihrZqdyZoCtbkplrb+xTC1TqXRmAXs9f+8Dzsl3jFJqUESOAxXAYc8xN6KVk5d/FZEh4PvA36ocG0aIyHuB9wI0NORepzCViMiYAcBi4boVdTl3gDwdRCRr5hwPB/LGYUxNqYBPxoxJrG+ZwR/++pIJx3ROB2PpTDSmU+wsqknq7QemMKaTyeevW0qhtngxls45s4s3pvpmo6htShE5B+hWSrV6mm9RSi0BLnR+bs11rlLqq0qpVUqpVTNmTH7W2JnEtSvqsrK1poKa0oi70C4T416rSoTzZtkZRKSgCgdGFg+eaUrH5xMudMr3F2pBYdDvy2ntTgWLapPcdeUC3r5i8hZlW06Pqbzy+wFv/mKd05bzGBEJAKXohALDTcC3vScopfY7vzuBb6HdeJY3AJ+7dgn3vDN3wNxVOpO8eHayuHh+FXde2uLunnomsc5ZSFko91oh8fuE96+fm7ZezTK9TKV77UlgnojMRiuXm4B3ZhzzIHA78BjwDuBXxlUmIj7gBrQ1g9MWAMqUUodFJAhcDWyewu9gmURGe/CNe20qM6hOh3g4wIcumTfdYkwJVy2pof1EL+fNfWOk3Fre2EyZ0nFiNB8EHkKnTN+nlHpeRD4LbFFKPQjcC3xTRF4BOtCKybAO2GsSERzCwEOOwvGjFc7Xpuo7WAqHsXQmu0yQZWwiQT9/flHzdItheZMwpYtIlFI/BX6a0fYpz+te4Po85z4MrM1o60Kv6bGcYUQ9MR2LxXLmUtSJBJY3DxXxEB/b0DLuXVMtFssbk+JfLm95UyAifGTDmRkzsVgsI1hLx2KxWCwFwyodi8VisRQMq3QsFovFUjCs0rFYLBZLwbBKx2KxWCwFwyodi8VisRQMq3QsFovFUjCs0rFYLBZLwZAcW9GccYjIIeC1CZ5WSfq+PsVEscpm5ZoYxSoXFK9sVq6JcbpyNSqlJnVvmDeF0jkVRGSLUmrVdMuRi2KVzco1MYpVLihe2axcE6MY5bLuNYvFYrEUDKt0LBaLxVIwrNLJz1enW4BRKFbZrFwTo1jlguKVzco1MYpOLhvTsVgsFkvBsJaOxWKxWAqGVToWi8ViKRhW6eRARK4QkR0i8oqI3DWNctSLyK9F5AUReV5EPuK0f1pE9ovIVufnqmmQbbeIPOd8/hanLSUivxCRl53f5dMg13xPv2wVkRMi8tHp6DMRuU9EDopIq6ctZx+J5p+ce26biKwosFxfEJEXnc/+oYiUOe1NItLj6bevFFiuvNdNRP6H0187ROTyqZJrFNm+65Frt4hsddoL2Wf5xohpv8/yopSyP54fwA/sBOYAIeBZYNE0yVIDrHBeJ4CXgEXAp4G/mOZ+DiP8ggAABvtJREFU2g1UZrTdDdzlvL4L+HwRXMt2oHE6+gxYB6wAWsfqI+Aq4GeAAGuBPxRYrsuAgPP68x65mrzHTUN/5bxuznPwLBAGZjvPrL+QsmX8/4vAp6ahz/KNEdN+n+X7sZZONmuAV5RSu5RS/cB3gI3TIYhSqk0p9bTzuhPYDsyaDlnGyUbgG87rbwBvm0ZZAC4BdiqlJlqNYlJQSv0W6MhoztdHG4H7leZxoExEagoll1Lq50qpQefPx4G6qfjsico1ChuB7yil+pRSrwKvoJ/dgssmIgLcAHx7qj4/H6OMEdN+n+XDKp1sZgF7PX/vowgGehFpApYDf3CaPuiYx/dNhxsLUMDPReQpEXmv01atlGpzXrcD1dMgl5ebSB8IprvPIH8fFdN99y70bNgwW0SeEZHfiMiF0yBPrutWTP11IXBAKfWyp63gfZYxRhTtfWaVzhsAESkBvg98VCl1AvgXYC5wNtCGNu0LzQVKqRXAlcAdIrLO+0+lbflpy8cXkRDwR8B/Ok3F0GdpTHcf5UJEPgkMAg84TW1Ag1JqOfBx4FsikiygSEV33XJwM+mTm4L3WY4xwqXY7jOrdLLZD9R7/q5z2qYFEQmib6YHlFI/AFBKHVBKDSmlhoGvMYVuhXwopfY7vw8CP3RkOGBMdef3wULL5eFK4Gml1AEojj5zyNdH037ficifAFcDtzgDFY776ojz+il07KSlUDKNct2mvb8ARCQAXAt817QVus9yjREU8X1mlU42TwLzRGS2M1u+CXhwOgRxfMX3AtuVUn/vaff6YN8OtGaeO8VyxUUkYV6jg9Ct6H663TnsduBHhZQrg7TZ53T3mYd8ffQgcJuTXbQWOO5xj0w5InIF8JfAHymluj3tM0TE77yeA8wDdhVQrnzX7UHgJhEJi8hsR64nCiWXhw3Ai0qpfaahkH2Wb4ygSO8zwGav5fpBZ3i8hJ6hfHIa5bgAbRZvA7Y6P1cB3wSec9ofBGoKLNccdObQs8Dzpo+ACuCXwMvAZiA1Tf0WB44ApZ62gvcZWum1AQNo3/m78/UROpvoHueeew5YVWC5XkH7+s199hXn2Ouca7wVeBq4psBy5b1uwCed/toBXFnoa+m0/xvw/oxjC9ln+caIab/P8v3YMjgWi8ViKRjWvWaxWCyWgmGVjsVisVgKhlU6FovFYikYVulYLBaLpWBYpWOxWCyWgmGVjuWMRUSUiHzR8/dfiMinJ+m9/01E3jEZ7zXG51wvIttF5NeetiWeCsYdIvKq83rzVMtjsZwuVulYzmT6gGtFpHK6BfHirGIfL+8G3qOUutg0KKWeU0qdrZQ6G7125RPO3xtO43MsloJglY7lTGYQvUf8xzL/kWmpiMhJ5/dFTpHGH4nILhH5OxG5RUSeEL1/0FzP22wQkS0i8pKIXO2c7xe9N82TTpHK93ne9xEReRB4IYc8Nzvv3yoin3faPoVe/HeviHxhPF9YRDaIyMMi8mP04j9E5HZH/q0i8mUR8TntV4rIYyLytOi9YeJO+xdE78+yzchisUwWdiZkOdO5B9gmIndP4JxlwEJ0KftdwNeVUmtEb5D1IeCjznFN6Fpgc4Ffi0gzcBu6tMhqEQkDvxeRnzvHrwDOUroUv4uI1KL3sFkJHEVX736bUuqzIvIW9H4yWyYg/yr0HlB7ROQsdPmY85RSgyLyVXT5mM3ofVYuUUp1O4U+PyIi96JXtC9WSilxNnOzWCYLq3QsZzRKqRMicj/wYaBnnKc9qZx6VCKyEzBK4zngYs9x/6F0IcqXRWQXsABdh26px4oqRdfe6geeyFQ4DquBh5VSh5zPfAC9adh/jVPeTB5TSu1xXm9w3n+LLtNFFF3uphu92dejTnsI+B1a0Q4DXxORnwA/PkUZLJacWKVjeTPwD+gaWP/qaRvEcS877qaQ5399ntfDnr+HSX9mMmtIKXRtqw8ppR7y/kNELgK6Tk38CeP9HAHuU0r9zwx53g5sUkrdmnmyiKwCLgWuBz6AVqQWy6RgYzqWMx6lVAfwH+igvGE32p0Fet+d4Cm89fUi4nPiPHPQhScfAj4gutw8ItJiYiWj8ASwXkQqnerENwO/OQV5crEZuMEkU4hIhYg0AI86nznHaY+LyDzR1cOTSqkfo2NhyydJDosFsJaO5c3DF4EPev7+GvAjEXkW2MSpWSF70Aojia403CsiX0fHep52ys4fYoxtu5VSbSJyF/BrtGXyE6XUpGwLoZR6TkQ+A2x2LLoBR9YnReTdwHdFb+EB8NdoF+QPnHiUD70JmcUyadgq0xaLxWIpGNa9ZrFYLJaCYZWOxWKxWAqGVToWi8ViKRhW6VgsFoulYFilY7FYLJaCYZWOxWKxWAqGVToWi8ViKRj/H0iBeon3/0uVAAAAAElFTkSuQmCC\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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7wq7nhJaFGwOMMbNlZrbCzGY090Bmdr2ZZZhZRn5+/tFV3A7pKfGs/5/LOHtkX3r1iAWgqExdMyLSfXTUAdUYYDQwDZgLPGpmvRqv5Jyb75yb7JybnJaW1kGbbl58rBeA3j18gM5SFZHupT3hvhcYEnZ9cGhZuBxggXOuxjm3E8gkGPZdrndisOWucBeR7qQ94b4KGG1mI8zMB8wBFjRa558EW+2YWSrBbpodHVjnUVPLXUS6ozbD3TlXC9wMLAI2Ay845zaa2T1mNiu02iKgwMw2AUuAHzvnCo5X0UciPSUej8HOg+VNbvPXTfguIhJl2nWGqnNuIbCw0bK7wi474LbQzwklMS6GMenJrM1u+EXZuwvK+Oojy7nxwpFce96ILqpOROT4iMozVBubOLQ36/YUEQi11Israrj2iVXklVaxZGuXjtoUETkuukW4Txrai9LKWrbnHwbgD+9uY3dBOROH9mJtdlGz3TOllTU8uXyXum5EJCJ1j3Af1huANdmHqKr188raHGac0p9vnz2Mw1W1ZOaWNrnPXz/ayV2vbWRZ1sHOLldE5Jh1i3A/KTWRngmxrNldxNsbcykqr+FrU4YweVgfAFbvbtgfHwg4XszIAeDDbcfvZCsRkeOlW4S7mTFxaC/ez8zj4fe3M6hXAueOTGVw7wTSkuNYvqOAP723jTWhg64fby9gb1EFPXxelmYeZHv+Yb72l+XkllR28V8iItI+3SLcAeZMGULAwab9JcyZMgSPxzAzzhzamzfW7+f+dzKZ/0FwaP4LGXvomRDLDReMZGtuKT95aT0rdxby5ob9XfxXiIi0T7cJ9xmnDOCTn17C0h9fxE3TRtYvv3hcP3xeD8P79mDT/hL8AceSLXnMPKU/l05IBz7vtnk/U100IhIZuk24Q7B7ZmjfHsR4P/+zvzp5MOvvvoyvTh5CdmE5a7MPUVpVy5ThfRjXP5nUpDhSk+L42uQhrNhRQGWNv/6+/oDj6RW7Ka0MTkq2v7iifrjlsdiQU8z6nKJjfhwR6b66Vbg3x8yIj/Vy8sAUAJ5ZmQ0ER9h4PMbvrz6dh785iRmn9qeyJsAnOwvr7/tBZh4//+dnPL0imz2F5Vxw7xKeXL7rmGv60YvruPOVDcf8OCLSfUXld6gejZMH9gTgjfX76ZPoY3jfHgBcMCY4e2VFtR9fjIf3t+bXL1uwbh8A72w6gMegxu94emU23zln+FF/MUh+aRWZuYfxxXjwBxxej75gRESOXLdvuddJS46jX3Ic1f4AE4f0ahLOCT4vZ53Ulw8yg2e0VlT7eWdTLgmxXtbuKeK5VXuI8RhZeYdZk330XSrLdwSn5KmuDbC7oOzo/6BOVOsP1J8gJiInBoV7mLqumYlDm0xFD8CFY9LYnl/GnsJylmzNo6zaz+2Xj8U52HmwjJumjaSHz8vzq7Lbtb2yqloOlVWTV1LJA+9k8vr6fXwcdtJUcydXdTTnHKt2FR7TsYIXMnK45P4PeHvjgQ6sTESOhbplwpw8sCdLtuYzaWjvZm+fNjaNX7wOH2Tm897mXNKS47jmnOE88fFO9hRW8JUzB3PwcBUvZuRwzshUqmr9rNxZyKh+SXx96lB6haYfBqis8TPzDx+SXfj5bJWxXiMpLobzRqWybPtBMnMPM+OUhjVU1wb427KdlFf7mTi0F9PGHtvX1b6/NZ/vPrGKe798GldPGdLk9soaP4Vl1QzsldDKYwQ/zdz+4qcsHJjC4N49jqkmETl2Cvcw/3baADJzS+unK2jspNREBvdO4G/LdrI9v4zbLxuD12N895wRrNtTxLC+idx5xXi255dx6/PrAOjdI5ZX1uzls73F/PkbZ9Y/1tMrdpNdWM4NF55Ej9gYpo1N48anV7O/uJJLxvdjz6FytjZquTvnuOOV9byyZi9mEOMxVv1sOr4YDy+vzmHD3mLmTh3KxBbenJrz0urgmbjPrNzdJNwPV9XyzcdW8tneYn546RhuvHBkk2MAtf4Ay7cXcP7oVNZmF3Hfoq08OGdiu7dfxx9wVNT4SYqLIb+0imVZBzl5YAqj+iUd8fGL7IJyhvRJoLImwD2vb+SGC0YyPDXxiGsSiWQK9zDjB6Qw/9uTW7zdzJg2No2nV2TTMyGW75wzHKDBlMEp8bE8ee1U/vjeNk4b3JPLT+7Pb97awqNLd7CnsJwhfXpQUlnDvCVZnDcqlTtnjq+/7x/nTuSOl9czfXw6y7IKyDwQDHd/wPHohzt4b3Muq3Yd4rZLx3DxuH588U8f8caG/azefYhX1gS/HKvG75qE+6KNB1ibXcTBw1Vk5R2mX3Icc6cOZdLQ3rwT+gTyaU4xn+0t5pRBPeu3ef2TGWzYW8zZJ/Xld4u2kl9axd2zTm7w2Ov3FlNaVcvXpgwhLSmODzLzCQQcnkZvAuXVtdz71lYmD+9NjMdY8Ok+fnTZWEamJbG3qIKbnl7NvqJK3vvRhfz3Pz/jrVAXzw8uGc0PLx3T5LnwBxwb9hZzxpCGXWhvbzzA9U+t5s/fmERFtZ9nP9nDwcPVPNrK89qS51dlM7h3D84dldpg+R/e3UbAuWbrakteSSXLdxQw6/SBVNUGWJN9iLNP6tuuN7DSyhp++cZmrj1vBGPSk494220Jf94+21tMcUUNo/sl0S8lvsO31R109YAI9bkfoWljgt0g150/guT42GbXiY/18pMZ45hxygDMgi17jxl//WgnNf4Atz3/KYfKa/jJjLEN7jdleB/e+9E0hvTpwdj+Sew8WEZ1bYC3Nx7gN29uobSylh9fPpbvXzyqvlX7+Ec7eW3dPq45ZziXTkhn3Z7PD+bW+gPcvWAjNzy1mr9+tIOlmfnEx3pYk13Ed59Yxax5H1FdG+CBq88gLsbD/KU76mfBXJZ1kI+3F3D3rJN56ntTQ91Pu3hzw35yDpUTnMIflm0LHiM4Z2Qq54xKpaCsmi0Hmh4rWLwljyc+3sXN/1jLjU+vYeGGA9y3aCsHiiuZ9aeP2JFfxsHDVdzx8nre2niAb589jMsmpPPIB9s5UNx02of5S3fwpXnLWLLl8ymbq2r9/HLhZgD+/vEuXl0bfMN7Z1Nuk/n8IdiddNvz6+r/lvzSKm58ajWvrMmhsKyan//zM37x+qYG99m8v4QH38vkoSVZ7CksJxBwFJfX1D+GP+DIyiulxh9osj1/wHHTM2v4wXPrWLwlj9+8uYWvP7qSPy3OCvu7tvOrhZvJK6kkY1che8K67R77cCfPrdrDLc+upaSyhmdW7ia7oOmX0EAwqIsrGn4p/GMf7uDdTblN1t1bVMHtL37KuLveYlnWQVbvLuSLf/qIbzy2ksseXErB4apmtxHuUFl1s39zuLzSyman8HDO8eaG/Vzxhw+5b9FWNu4r5g/vbuPdTbkdct5Ie+WXVnHPvzZx5Z+XsXl/SYP6jkTOoXLuXrCR8Xe9xVPLd3VskUdALfcjdNG4fjzwtdOZecqAdt+nf894Zp0+kKdW7GbxljyyC8u5Z/bJnDa4+QO3AGPSk6kNOHYcPMyzq/YwoGc8b9xyfoOWwJfOGMh9b2fi83q48cKRvLp2L+9syuVQWTW9E308tCSLJz7exX+cN4I7Zo6rP3mrxh/gqeW7+e1bWxjXP5lzR/XlW2cN47GPdrLzYBmPfOtMXlmTQ0p8DFdPHoyZccfMcazYUcBNz6wBYPYZA3nwa2fwQWY+Jw9MoU+ij3NH9QWCbwz7iirYnn+YM4f1ZvLwPqzcUUiiz8tDX59EbcCxNvsQD3+wnQMllRyuquVf3z+PeUuyeG3dPhJ9Xm67dAyllbUsuf99frVwM/9++kBeXZvD2uwifnnlKfz5/WAgPvhuJtPGpmGhN8/dBeVMG5vG+1vzMYP/OG8Er67dy/efXcuZw3pT4w8wKi2JW6eP4TdvbmHLgVJumjaSuBgvcx9dwd6iCjJ2HyK/tIoav2PLgVIyc0vrW8q/WriZ5LgYKmr8PPBuJjsPlrE2u4j4WA8DeiZQXFFDYVk1k4b2Yt43JjGgZ/BYhT/g+MvS7azefYikuBj+91+b2FdUQd9EH79/JzM439GoVO59ayu1Acf8pcGpMOJjPfxp7iSmDO/N4x/tZGRaIlsOlHLOrxdzuKqWHj4vt04fzVfOHEKNP8CB4koCzvGrhZtZn1PMCzeczelDerHzYBm/XLiZUwb2ZPqEdCpr/DgH2YXlfOOxFZRU1hIf4+H372TSJ9FHrx6x/OaqU7n5H2v5zZtb+M45wykqr+G80Q0/xQDkllRy+YNLGZGayJPXTuX19fvZVVBGamIcs88YSL+UePYWVTD7oY9I8HlZ/KNpxIadSPiL1zfz+LKdDOwZz0NLsnhoyedvduP6J3P3rJM566Tgayu/tIr80ir6pQRPLmzL4dCghcG9EzAzav0Bnvh4F9PHpzfoqnt3Uy4/fGEd5dV+kuNjuOrPH/OXb53JeaNSufovy0lJiOWPcyeSFBfD6+v38cA7mdx26Vj+7bTPcyCvtJJbn1vHx9sL8HqMAT3j+fWbW7hoXD8G9+7BgeJKNu8v4eRBKfRLPv6fhuxI35U6yuTJk11GRkaXbLsrFJZV8/D7WWzcV8LMUwfwrbOGtbr+nsJyLr7/fU4b3Is12Yf4/sWjua1RN8CewnIu+N0S5kwZyq+vOpUVOwqYM38Ff7tmCiPTkpj+wAfMOLk/f5zbfB/4vqIKvB4jPSUe5xyvr9/Pna9sYEx6Epv3l3LlpEH86spT69fPLankjfX72XHwME+vyOb0wT35NKeYH18+lv+8aBQAF9//PhDs964Ntbqe+O4UfrVwM/17JvDktVPr98e5v1lMRY2fO2eO44YLR7K7oIxLH1jKdeeP4MeXjwPg7gUbeeLjXQCkxMeQGBfD/uJKzOCac4bzt2W76v++W59by6UT0vnVlady9q8XU+0PsOT2aew6WMbD729nf0kFhpFdWM6XJw3m5TXB4w33zD6ZzNxSXlqdw+2XjeX/3tiMz+thYK94sgvL+c+LRjF+QAp//Wgnq3cf4uf/Np7M3FJeyMgh1mvceOFIKqr9HCipxOf1MCo9iXmLs0jwxfDEd6fw/tY8/rJ0B6WVtUwf348vTRzEzf9YS6LPy7s/upBbn1vHpn0lzDy1Py+tzuGv10xhXXYRo9OTeHTpDj7NKaZ3j1iKKmp48wfn89wne1i6LZ/bLh3DCxk5LG1mWozk+BgSYr3Eej28cct53Pf2Vp5eERzFtepn07nhqQzW5xQT6/WQHB/DP647i+XbD/Lfr20E4JaLR3HbZWP59Zub+UtoziUzeOWmc9iwt5jX1u3jr9+ZTM+EWK5/ajUfZOZT6w/QwxfD4apaYr1Gjd/h83q4YEwauwrKyC4op9of4LdfPpXR6cnklVSSkhDL1x9dyde/MJR7Zp3Mmuwithwo4bIJ/Vm+4yD3Lcpkb1EFV04cxKShvfi/NzZTVRvA5/Xw5Pem1of+w+9vJ9Zr/Mf5JwHBN9PX1+/jf/+1icKyavolx/Hrq05la24p9761lX7JcTx/w9kM69ODB9/bxh/f28Ypg1L4w5yJJMfF8I3HVlJWVcuPZ4zlh89/CsCY9CRGpCayaGMuiT4vZdV+po1No29iHBeNS+ORD7azPa+M/zdtJF+aOAgzuOyBpQzuncDQPol8kJlHjT/4P3HDBSdx5xXjORpmtto512Y/o8L9BPb8qmz+6+UNmMGHP7mo2VEoq3cfYlz/ZBLjYiirquXUuxdx80Wj+GxfCSt3FLD49mmkH0Gf6Uurc7j9xeCL+aUbz2by8D5N1nHOcf1Tq3lnUy7fv3gUt106pr7P+K7XPuPJ5bsZ2DOeZ647i68+spzhfXuQsftQgzcBCM6Zv3JHAX/+xqT6TxUHiitJTfLVX6+s8ZOx6xAJPi/jByRTUlHL9/6+iqkj+nDnzPFM//0H9SOOpgzvzd+vnUoPXwx3L9hIzqEKHvtOw/+BQMAxZ/4KPtlVSGqSj1ivh1MH9WTtniKmDO/NvK9PYva8ZazPKeauL05g8ZY8Vu8+REWNn5FpiVw9eQjfO28EOYcquOmZNdw6fTSXn9y/yT7allvKdx7/hP0llTgHl05I54pT+zPzlAH4vB7+6+X1nD2yL1dNGsyTuiE/AAAK0ElEQVSug2Vc/uBSqmoDXDYhvcFxn/LqWv62bBeb9pUwrn8y379kdJPnYtP+Et7dlEdKQgyDe/egvLqWs07qy/7iSr76yMf0S47n4OEqxvVP5tOcYr57bvBN8ZJx/UhJiOWWS0YzIjWRyho/F9y7hOKKGpbdcTGpSXGUVdXyXy+vZ/yAFJ5avhuPUf83TR/fj3H9U3hoSRY/vWIcfRLjmLcki1unj2bW6QPZXVDO48t28lHWQYrKa7j/6tN54J1M9hSWU1JZW98nPahXAotuvYAEn7fJfqyo9jNvSRbzl+6g2h/g7JP68u2zh/G7RVs5XFXLwh+cjz/gOOc3i/EHHPfMPpn80iqeX7WHvNIqTh/ck6smDeb5VXvYlleKYUwZ0ZvN+0spr66lf0o8uwrK+cqZg/m/L51CfGywhrqGks/rYVDvBO6cOY773t5KRY2f80al8dMrxvHAO9v4ePtB8kurKCirxusxHv32mVw8Lr2+/tfW7eVPi7MIBBznjU7lsgn92XKghAkDUzhnZNNPQe2hcI8S85ZkUVZVy09mjGvX+jP/8CF7Css5XFXLz/9tfH1Lpr2cc9zy3DoyD5Ty1q3nt3igr6rWz478MsYPSGmwfFnWQb7391U8ee0XmDqiD799awsPv78dgJdvOpszhzV9szhSda9ZMyOvtJIPtuZzqLyauVOHtngcJNz2/MPMfmgZt1wyim25h3l5TQ4BFzygPev0gSzZmsc9/9rEyzedw+Itedz+4qfMmTKEX3zplAbdCW3ZX1zBz179jIvG9eObXxja6kHTeUuy+N2irTx3/Vn1rdGOsGJHAb94fROZuaW8ccv5zJ2/goKyauJjPXzys+mkNNpfH2cdpLC8mi+eNrDJY72zKZfrnsxgwoAUvnj6AO59aysAV00cxL1fOa3BnE2NOecwMxZvyeXaJzKYPj6dyyak8+yqbH56xXimNNOICLcj/zCrdx/iyomDiPF62Ly/hC/NW8bUEX2YNLQ3f3hvG6cO6smGvcVA8I3nyomDmXFKf7weo7iihm/9dSX7iyt56wfnU1RRw1PLd7PlQAn/fvpAvj616fNz3ZMZvLMpl9995TS+OrnpMOE6/oDjg8w84mK8TQ6+Hw8K927qp69u4B8rs5k+Pp1Hv33mUU2DEAg4As61+s/amlp/oP6+uwvKuPB37xMf62H9/1yOL+bEOIZfXl1LD18Mr6zJ4bYXPsXn9bDmrktJimt4GMo5R2buYcakH/mQzCMRCDgy80oZ1z+l7ZWP4rEPlVfTNymOHz6/jlfX7uXLkwZz/9WnH/FjLd6Sy2mDe9E30cdjH+5kVHoSFx3huRa7DpYxtE+PJiOqjtSzn2Rz5ysb8HqMc0el8uDXzuCJZTu54rQBze7HWn+A8hp/kze0luwvruDFjBxumjbyiN7Uj7f2hrsOqEaZ2acP5EBxJfdfffpRh5HHY3g4+n+88DeFYX0TufzkdDxmJ0ywA/TwBV/6Z48MtpLPH53aJNgh+OlgbP+OH3bYmMdjxyXY6x67b+jg4/Tx6by6di9f/8LQo3qs8C6H6y44sk+FdTrqnIM5U4Iztb62bh/f/MJQ+iT6uO2ysS2uH+P1kHIEIT2gZwK3NOoGiyRquctxF96NciKqO+fg9CEtj16KFs45dheUR81JXZU1fj7ZWcj5o1NP2NdXR1PLXU4YJ/o/XfhB3mhnZlET7BA8p6RullZpqF2fUcxshpltNbMsM7ujmduvMbN8M1sX+vmPji9VRETaq82Wu5l5gXnApUAOsMrMFjjnNjVa9Xnn3M3HoUYRETlC7Wm5TwWynHM7nHPVwHPA7ONbloiIHIv2hPsgYE/Y9ZzQssa+bGbrzewlM2t5UKiIiBx3HTU27V/AcOfcacA7wN+bW8nMrjezDDPLyM9vesq0iIh0jPaE+14gvCU+OLSsnnOuwDlXN3XcY8CZNMM5N985N9k5NzktTUe4RUSOl/aE+ypgtJmNMDMfMAdYEL6CmYVPkTgL2NxxJYqIyJFqc7SMc67WzG4GFgFe4HHn3EYzuwfIcM4tAG4xs1lALVAIXHMcaxYRkTZ02RmqZpYP7D6Ku6YCB9tcq/OpriN3otamuo7MiVoXnLi1HUtdw5xzbfZrd1m4Hy0zy2jPqbedTXUduRO1NtV1ZE7UuuDEra0z6jpxZnISEZEOo3AXEYlCkRju87u6gBaoriN3otamuo7MiVoXnLi1Hfe6Iq7PXURE2haJLXcREWlDxIR7W9MOd3ItQ8xsiZltMrONZvaD0PK7zWxv2NTHV3RBbbvMbENo+xmhZX3M7B0z2xb63buTaxobtk/WmVmJmd3aVfvLzB43szwz+yxsWbP7yIL+GHrdrTezSZ1c1+/MbEto26+aWa/Q8uFmVhG27x7p5LpafO7M7M7Q/tpqZpd3cl3Ph9W0y8zWhZZ35v5qKR869zXmnDvhfwiePLUdOAnwAZ8CE7qwngHApNDlZCATmADcDdzexftqF5DaaNm9wB2hy3cAv+3i5/IAMKyr9hdwATAJ+KytfQRcAbwJGHAWsLKT67oMiAld/m1YXcPD1+uC/dXscxf6P/gUiANGhP5vvZ1VV6Pb7wfu6oL91VI+dOprLFJa7ifUtMPOuf3OuTWhy6UEp1tobqbME8VsPp/M7e/Al7qwlkuA7c65ozmBrUM455YSPJM6XEv7aDbwpAtaAfRqNN3Gca3LOfe2c642dHUFwbmdOlUL+6sls4HnnHNVzrmdQBbB/99OrcvMDLgaePZ4bLs1reRDp77GIiXc2zvtcKczs+HARGBlaNHNoY9Wj3d290eIA942s9Vmdn1oWbpzbn/o8gEgvfm7doo5NPyH6+r9VaelfXQivfauJdjCqzPCzNaa2Qdmdn4X1NPcc3ei7K/zgVzn3LawZZ2+vxrlQ6e+xiIl3E9IZpYEvAzc6pwrAR4GRgJnAPsJfizsbOc55yYBM4H/NLMLwm90wc+BXTJEyoITz80CXgwtOhH2VxNduY9aYmY/Izh30zOhRfuBoc65icBtwD/MLKUTSzohn7swc2nYiOj0/dVMPtTrjNdYpIR7m9MOdzYziyX4xD3jnHsFwDmX65zzO+cCwKMcp4+jrXHO7Q39zgNeDdWQW/cxL/Q7r7PrCpkJrHHO5YZq7PL9FaalfdTlrz0zuwb4IvCNUCgQ6vYoCF1eTbBve0xn1dTKc3ci7K8Y4Crg+bplnb2/mssHOvk1Finh3ua0w50p1J/3V2Czc+73YcvD+8muBD5rfN/jXFeimSXXXSZ4MO4zgvvqO6HVvgO81pl1hWnQmurq/dVIS/toAfDt0IiGs4DisI/Wx52ZzQB+AsxyzpWHLU+z4PcbY2YnAaOBHZ1YV0vP3QJgjpnFmdmIUF2fdFZdIdOBLc65nLoFnbm/WsoHOvs11hlHjzvih+AR5UyC77g/6+JaziP4kWo9sC70cwXwFLAhtHwBMKCT6zqJ4EiFT4GNdfsJ6Au8B2wD3gX6dME+SwQKgJ5hy7pkfxF8g9kP1BDs3/xeS/uI4AiGeaHX3QZgcifXlUWwP7budfZIaN0vh57jdcAa4N87ua4WnzvgZ6H9tRWY2Zl1hZY/AdzYaN3O3F8t5UOnvsZ0hqqISBSKlG4ZERE5Agp3EZEopHAXEYlCCncRkSikcBcRiUIKdxGRKKRwFxGJQgp3EZEo9P8B/s0Ja/Ht/5EAAAAASUVORK5CYII=\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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yl6wThHgL4d4Xt/OfT212j0XUQlAUJYvJOkGItxBe3NLEs5sa3WMRTTtVFCWLyTpBiF/+eiASpS9u5dNInMvIGDMh7VMURZkoMhIEEblURLaISJ2I3J7ieFBEHrGPrxSRGrv8ehFZF/cvJiIn2seuFZENIrJeRJ4UkfIjeWPpCPg8RGOGSDRGfzhG72C8IFhCETND1oKiKEq2MKogiIgXuBu4DFgEXCsii5Kq3Qi0GWPmAncBdwIYY35ljDnRGHMi8DFgpzFmnYj4gB8A5xtjjgfWA7ccqZsaCXcbzWiM/kiUvnhBiBMBjSMoipJtZGIhLAPqjDE7jDGDwMPA8qQ6y4EH7NePAReKiCTVudY+F0DsfyG7XiFw4BDaP2YCtiAMhGMMhGP0DEZc91C8IKSbvKYoivJuJRNBqAb2xr3fZ5elrGOMiQAdQFlSnY8Av7brhIG/AzZgCcEi4P4xtv2QCPq8gGUhWLGCIWsgHDc/QVNPFUXJNsYlqCwipwK9xpi37Pd+LEE4CZiG5TL6YppzbxKR1SKyuqmp6bDbEm8h9NvZRE4cIRrvMtJMI0VRsoxMBGE/MCPu/XS7LGUdOz5QBLTEHb8G2zqwORHAGLPdWP6aR4EzUn24MeY+Y8xSY8zSioqKDJo7Mk4MYSASpd/OMOoZiABDE9Os4yoIiqJkF5kIwipgnojUikgAq3NfkVRnBXCD/foq4Dm7o0dEPMDVDMUPwBKQRSLi9PAXAZsO7RbGhmMh9A5G3ZiBk3rqLF0B6jJSFCX78I1WwRgTEZFbgKcAL/BTY8xGEfkasNoYswLL//+giNQBrVii4XAOsNcYsyPumgdE5KvASyISBnYDnzhSNzUSjoXQ1R9xyxyXUUQtBEVRsphRBQHAGPME8ERS2R1xr/uBD6c59wXgtBTl9wL3jqGtRwQnqNzRF3bLegdtl5HGEBRFyWKybqay4zLq7I8ThIEj4zL6ycs7eGJD/WG2UFEUZWLIOkFwXEad8RaCHUMIRw0ee/bEobiMfvTSDh5dvXf0ioqiKJOQ7BWEOAuhz3YZRaIxQkHLizZWC6E/HKWpa4C23vDolRVFUSYhWSgIw2MIPbbLKBIzhAK2ICTFEKKjrG20r60PgPbewSPWVkVRlPEk6wTBjSH0DWUZOWmnkaghLzg0k9khFjOcdedz/Nvv30q7Cuq+tl4A2npUEBRFeWeSdYKQymXkZBlFYjHyg8MthO7BCPUd/Tzw2m6+9/TWlNd1LITO/oi7aqqiKMo7iawThECqoPJgCpdRXAyh256zUJLn57+fr3MFJJ69toUA0N6ncQRFUd55ZJ0gOBZCwjyEgSGXUY7fg0cSs4ycSWwnzSzBGGjsHBh2XcdCAI0jKIryziTrBMHntTr8TruTD/g8btppJGbweT0Efd4EQegesMRjdnkIgMau1ILg91o5q5pppCjKO5GsEwSwMo0cl1FpXiAh7dTvFYJ+DwNxW2s6FsKcynwAGrv6h11zX2svC6cUAlZg+bE1+1jx5rhs8aAoinJEyEpBCPg8rgVQEgokpJ16PR6CccdhSBBcCyHJZdQ7GKGlZ5DF04sAaO8Nc88LdTzw6q6jfSsuo6XFKoqijEZWCoITRwAoDfnjXEYx/B5J4TKyBGFGaR5+rwxzGe234weLqy1BaO4ZYF9rHy3dw11LR4PtTd0s/H9/oq6xa1w+T1GUdyfZKQh+67Z9HqEg6I9zGRl8XrEthOFZRgU5Piryg8NcRk6G0fyqfAJeD5vruxiMxmgZpzkJ2xq6CEcNu1t6R6+sKIqShqwUhIDXuu0cv5e8gNd1GYWjtsvI70mYh9A1EEEEQgEfFYU5NCVZCM1dVsdfWZBDcZ6fN/e1W+f1R8ZlXwVHeJz0WUVRlEMhKwXBWb4i6POQF/QmbJDj9w53GXX1h8kP+PB4hMqC4LAYguNSKsjxUZIXSBipt/Uc/Yyjlm5HEIbPj5is/H7dftbtbZ/oZiiKEkdWCoIzOc2yEHxDM5WjBp/HQ8DrYTA+htAfIT/HmrBWWWC5jN7a38FnH1pLJBpzt+DMC/goCfkTPqt5hDhC32B0xOOZ4sQq3kkWwlf/8Db//VzdRDdDUZQ4slIQnKBy0O8h1++lPxwjGjOEYzErhuBPiiEMRChwBSGHtt4wD762m8fX19PUPUDPYJSA10PA56EkL5DwWa1JcYQ9Lb1s2NcBwF3PbOXqe1877Ptpfoe5jGIxQ3vvIJvqOye6KYqixJGVguBaCD4rhgDWAnfRmMHnkZRpp84aR5WFQQCe3HgQsKyHnoEIIXtRvGJbEKqLcwFo6Um0AO58cjOff/gNAHa39LC7tZfYYaaMDlkI7wyXUfdghJiB/e19CTPGFUWZWLJSENwYgt9Dnt3R9w5GCEcdQUiKIQxEyM+xXEGVBZYgOB1Z14AlCHn2GkgleVa9k2YWA0P+fYcDHX1u2mpbT5hozCQstJcpsZjhrztaEj7DCY5PdjriZnJvVitBUSYNWSoIcRaC3xIHJ7XUWroicaZyd384wWUUT3d/hJ7BIQvCcRkdV12EzyPDUk+bugboHrCyj1rtNY8OJT31mU0NXHPfX1m/r911S/W9Q1xG8VaBuo0UZfKQ3YLg97guo05XEIT8HF9Cp9XVH6EgyWXk0D0QoWcg6rqMSkKWINSU5VEaCtAaZyEYY1zroLVn0N07ITnOkAlbDlqT0DbXd7nC0hseH0FYuaOFF7Y0HvL57b3xgqCT6RRlspCVghCfZZTrCIItAD6PMLUoh57BKF22Kyc+qFwWCiACC6cUWMf6I3QPRNytN+dX5RPweTh2WhFl+cGEGEJnX8TNXmruGqTNsRC6xy4I25u6AVizuw1nz57egfGJIXzv6a1864lNh3x+e58zbyPIpoNqISjKZCErBcHNMvJ53I7c8eP7PB6mFFkB4YMd/URjht7BKPlBKzbg83q45fy5fOE98wErhtA7GHH3UTh+ejFvf/USZpTmURYKJLiDmrqHZjjvbOnBiSUfioWwo7kHgFW7W92y8coyauwaoL5j+AJ/meJYCKfPKWPLwa5JtaFQW88g+9v7Rq+oKO9CslIQEiwEv2MhWKNrv1eYUmjFCeo7+t3YgjMPAeC2ixdw0aIqwMkyirrCApZoAJTlBxJG//ET2uoau93XrT1jm4tgjGG7ff6OJksYgj5P2iyj/nCUHz67jaXfeJpnNzWM6bNS0djZT5dtGR0KjjvulJpSBiKxhL0kJpqvP/42N/z09YluhqJMCFkpCE6WUY7f63bkTifl9XiYWmQJwsHOfrrsvRAK4gTBqifk+r10D4Rtl5F32OeUhgIJo/+muElo2+MEYaxB5cYua+5DfpwIzSjNS2sh3PX0Vr779Faauwd58zBnB3cPROixP+dgCishHI3xqV+sZqWdAZWK9t5BcvweNzW3bYI3FHr7QCdv7GkDYFdzDzuausdlyZHx4A9vHuCHz26b6GYo7xAyEgQRuVREtohInYjcnuJ4UEQesY+vFJEau/x6EVkX9y8mIifaxwIicp+IbBWRzSJy5ZG8sZEIJLiMrI7cEQSfV9zA8cGO/qFlKYK+YdfJz/HR7biMUhwvzw/SPRCh3w72prMQxhpDcOIH5y6ocMtmlOSmFYT1+zo4cUYxZaEATYcQr4insXNIBFIJwob9HTz9dgN3Prk57TXae8MU5wYoslN0j9SWo69ub+azD60d87yOr/xhI1/87QbAuqeY4V2zUOCjq/dy74vbMUaXR1dGZ1RBEBEvcDdwGbAIuFZEFiVVuxFoM8bMBe4C7gQwxvzKGHOiMeZE4GPATmPMOvucLwONxpj59nVfPBI3lAlDM5W9ru/fEQRnLaOyUID6jn53L4T8nOEdfkHQR0v3IOGoSRitO5TaGUeOldDUPUDQ56E8P8BOOwZQURAccwxhu+0muugYy23lEZhWnJvWZbSrpYfZ5SHK84OHvVRGQ5yo1XcMd/Ws3mXFNNbuaXdfJ9PeF6Y4z09xriUInYcgCFsOdnHJXS/x4tYmt+x/XtjO4+vrU+5oNxI7mnrY09pLNGZosM91XHFjIRyN8er25jGfNxqNnf3uoOLh1/fw+s7UzzUVe1p76RmMalxEyYhMLIRlQJ0xZocxZhB4GFieVGc58ID9+jHgQhGRpDrX2uc6fBL4dwBjTMwYc+T/ktIQn3aa6/ciAh125ovXYx2rKsyhobM/bulr/7Dr5Of4aLBHzE76ajxltiA4FkBjZz+VhUFKQwEG7UDqvMr8jF1GK3e0sOybz/CHdQfIC3g5fU4ZYAlPftDnunLi6RuMUt/RT015iPKCQMIeDQ+t3MMnfvb6mEbU8Ut/p7IQVu1qo7o4l+I8Pz96aUfKa3T0hinK9VNkC0L7GLcc3XKwi2vue40tDV28tr3FbdcrddZPaE9r5qP7zv4wzd0D9A5G2XKwy91oaEdz9yhnDueJDfVc9+OV7G4Zu5ikIxYzXP5fL/PD57ZhjOFrf3ybH7+c+rkmE4nG3L06tjYc3fTeWMzQ2NnvruuVTfx+3f4jshdJfUcfa3ZnLvZHg0wEoRrYG/d+n12Wso4xJgJ0AGVJdT4C/BpARIrtsq+LyFoR+Y2IVI2x7YdMwIkh+Lx4PEKe3ztkIXgsHZtalGNZCPYPPJUFkB/0uSPmVC6jsnxbEOygcWPXABX5QddyCPo8TC/JzTio/NqOFhq7Bnh9VyuzK0JUFgTJD/ooCwXJC/gYjMSG7Zy2u9XqnGrLQ5SFgjTb4vTzV3bypd9t4IUtTXSPYckLx+2V4/dQ35koCMYY1uxu4/Q5ZVxzykye3dSQchZ2e98gxXljEwRjjOv2+MnLO4hEDRUFQfbanf8f3qx3s7b2jkEQdjUPdd6r4iyaQ7EQDrT3J/zvsK+tl/d878Uxtcthf3sfzd2DbDzQSVOXJVzx8aeRqO/oJ2I/lC0Hxy5wsZhhc4ZpwV/9w0aWfetZjvvKUzx/GHNUxsJP/7KTT/1i9bh8Vjr2tvbyhUfW8YNnD3+hxn/7/UY+8dNVE+reG5egsoicCvQaY96yi3zAdOBVY8wS4DXgO2nOvUlEVovI6qamplRVxkz84nZgdeZDMQSrbEpRDgc7+ty5CMlBZbAEwQkUpxKMspAVi3AshKauASoLctzy0lCA0pDlMsrkR7CtoZuqwiDzq/I5paYUEWF+VT5Ti3NcCyXZbeR0eLVxLqP+cJRvPL7JjYukG9Wt39fuxiscGjr7yfV7qS3PpyHJQtjR3ENrzyBLZ5VwzvxyYgZWpXBvdPRZMQSf10NB0OfOSxiJf3psPTf8bBVgLf8xtyqfY6YWutbA79ftZ0FVASJjsxB2xgnC67YgFOf5E8ozxXHHJbvlXqlrpq6xm9WHMPpzYk07m3vcNu1u7U1YjTcd8QK07RAshCc3HuSyH7yckbvp7fpOZleEEOCN3W1j/qyx0tUf5q5ntvL02w3D9icZT36zei/G/p0fTkfePRDhha1NdA1EJtS9l4kg7AdmxL2fbpelrCMiPqAIiE8zuQbbOrBpAXqB39rvfwMsSfXhxpj7jDFLjTFLKyoqUlUZM/GL20GSINgWwpRCa1VTpzNPKQg5PndEnsplVJqfGENo7BqgomDIQijJC1AWChCOGtp6wzy+vp6vrNiY1ke8rbGLxdXFPPn5c7jjfVYY5wfXnMS3PriYvKAjCIluI2e+guMyclwjkZjh1NmWEee4xeKpa+ziw/e+xhU//IvrinHuobIwyDTbgorHiRksrSllycwSAj6Pu95SPO29VgwBoDDXn3aBu+e3NPKTl3fw5Fv1PLZmH2t2WX909R39TC3KYWZpLntae2nrGWT9vg6uOHEaUwpz3B3sMiHeEnDE6/TZZexoGvuI2hGC5A7q7QPWKDtdoPrFrU08+NfdKY85rp69rb1stcUhGjPsst1SXf1hfvDMtpTLljjCOLcyny2HIAg7m3swBtctGk/PQIRbH13nTlDc29rHSTNKmFGa5/7mjiYPrdzjxvcmys0SjRkeXb2PgNfDwc7+w0qffn5zoyvy2xrG/ts7UmQiCKuAeSJSKyIBrM59RVKdFcAN9uurgOeMLZci4gGuJi5+YB/7A3CeXXQh8PYh3sOYCcbNQwAIBb2j4l3qAAAgAElEQVTugms+ry0IdurppvpOPII7XyGe+MyjVBZCQdBHwOuhuWeAgUiUjr4wlXGCYFkI1utbH13HZx9aywOv7eKjP1nJU/Zqqg7haIydzT3Mr8rH4xGcEM2M0jymFefGWQiJHcOu5h7K8y3XUnm+ZZk4KZaLptqzreMshD5bML7wyDryAl6ml+TxsftX8r4fvswTG+pp6OynqiDHsqCSOop1ezsoyvUzpyJEjt/LSTOKeS1JEPrDUQYiMTfDqDjPn7DYXTz3vbiDbzy+iU//ci0APYNR2nvDHOzoZ0phLjNL8+joC7Nyp/UZx08vYkZp3phcMzube5heYsU8GrusoP+SmSW09YbdpUUcjDF8989beGt/R8prOUKQbCG8ba/XtCeFILywpZEbf76Kr6zYmNJS22p3DjEDL24ZspAdy+HZTY3c9cxWHlm1Z9i5e1p78XuFc+ZVUNfYPcydmMwf1x9IEEInRpT8/XT0hrn6R6/x27X7+e3a/QxEojR09TOjNJfa8lBG1tXmg53c+ui6ES2d1p5Bbn5w9TCBHYzEuP8vO1lWW0rQ52HVrqNvkaTipa1NHOzs5zPnzwFg5RiC/Q794ShNXQM8+dZB14V6tOM9IzGqINgxgVuAp4BNwKPGmI0i8jURucKudj9QJiJ1wK1AfGrqOcBeY0xyJOxfgK+IyHqsDKTbDu9WMicQF1QGa2MbJ1bg8wy5jMAym0+tLWN4jDwx8yhVDEFE3PWMnB91ZWHQjS2UhAKuFfHCliY+eFI1q7/8Ho6ZVsjnH37DzSwBq2MPRw3zqwpS3pOz2qrTqRhjiERj7GrupbY8D4AKRxDsuQiLphUCiYJw5f+8yiXff4m39nfy7SuP59GbT+eW8+fSMxDljt+/RX1HPxWFQaYW5dDaM5jQxv3tfcwszXOf1elzyth4oDPBAnDiBcW51n0X5/nTpp129IWpLQ9x8qwSPnOe9Ue36WAnvYNR20Kw7uupjdZku2OmFjKjJI+9ralHag2d/bQnzXnY1dJDbXmIGSXWtaYV5zK7IgQwbKTb2R/hh8/V8cf19W7Zql2tXPfjvzIQiaZ0GcVixl2vKd6V1TMQ4fvPbOXmB9dQmOsnGjO8sWf4HJFtjV3uCrqv1DUzrSgHkSFBcCyFn7+6i72tvXxlxUb3Hne39lJdnMvCqQUMRGI8/fZB1u9LPQ9lT0svn/v1G/zoxaE/U0fwk116P3h2G5vqOzl/QQXN3QNsPdiNMTC9JM8VhNHcJ3/acJDfrt3vbjebihe3NvLUxoZhmVtbG7po7Brgo6fN4oQZxaze1craPW18+0+bOTCO7pZfv76H8vwAf3feHIpy/a6FaYxJO8hJ5o7fv8Up33yGJ96q5/0nTKWyIOgOAiaCjGIIxpgnjDHzjTFzjDHftMvuMMassF/3G2M+bIyZa4xZFt/5G2NeMMacluKau40x5xhjjjfGXGiMGT7EOUo47h9nVJ8f9LnrATkWgjM5LeD18K0PLU55HWc5C8BNX02mLN9avsIJPlcW5AxZCHl+NxMJ4FNnz6YsP8gnz6yhPxxL9AHbHcDcyvyUnxO/rwPA1/74Nhd890W2NHRRU2Z1cI6FsHZPG3kBLzNLrXJHRGIxw7bGLi5fPIUn/v5sLjl2CkV5fm69eAH/cukCmrsH2dPaa1sI1qSyeHfCgfY+phUPrQZ72uwyjCHBBeZ0Lo7LqDg3MKyTdujoC3PSjGL+9+/O4PLFUwFYY48GpxTlMMMWhGc2NVBVGKQ8P8jM0jwauvoThMrh+p+s5F//7y33vTGGnU1WSu70Eut+phTmMK/SEt3nNifO6nbSbDviOshHV+3l1e0t7GzucQP2zXFzPfa29dI9ECHo87C7tZe+wSiffnANJ339ab7/zDbec0wVv/vMGXhkKIbhEIsZ6hq7udBOL+4LR1k4tZDq4twhQbBFa1dLL+/9r5f5+au7eHS1lQOyt7WXGaV5LLAHEZ/+5Vquuve1lBbUL1fuxhioi7MQnO82Pui/o6mbX7y2i4+cMpNrls0E4KVtluUyvSSX2eUhegejCenJqXCELD6QPxCJJixjsnF/asvKsUDmVeZzSk0Jbx3o5O9+uYZ7X9zOef/5Al9ZsTFhvszh8qMXt/Ohe15JKGvs6ufZzY1cefJ0gj4vp9SUuPfy/JZGln7z6YziNn/Z1szCKQVcvKiKG06vYX5VQYKF0NI9QHgcl3bJypnKS2aW8OOPL+WUmlIg0f/vxBCqi/Mozw/ypcuPobY8lPI6+XGzk1PNVAbLLdTSM+iOXKYV5w7FEOJcRqfWlroj9ll2B74r7g9ha0MXHhlJEBIthDf3trOntZeOvjA1dvsdy2RvqzWSdwSx295Hoa3XmlOxrGaoLQ7nLah061fZFgIMZdQYY6hv72OqLRRg7QlRmOPje09vda2QIQshPoaQOqjd2Rd2XUvOrOZVdsByapwgdPVHWDTVau+M0lyMvfkOWHGNva29HOzop66x2/Xng9Vxdw1ELAvBvtbUohxmluXxwZOquffFHazdM+SOqG9P7CCNMfzFjq/saOoZmm8S5+Jwlvc+d36F5RrYWM+TGw9y5ZLp/PYzZ3D39UuYVRbimKmFw+Zt7G/vo3cwysmzSlwxrykLMbcyP85C6GVZbSnTinKIxgwzSnNdC2ZPay8zS/M4dlohnzyzli9ffgwegW8nTRrsG4zyyCpLROoau93RveMyiheEb/9pM0Gfh1svms+cCuu36MwFmV6SS225VTZa2q4jZKt2ttLcPcAtD63lhK/+mc8+tNats9GJvSQJmHNuTVmIpTWlRGOG5u5B7v3oyXxoSTUP/nU3l//Xy25cxRjD3c/X8Zdth5bZ/uzmRtbuaU9Is/7fNfuJxgwfWWqFV0+pKXWTKtbv6yAcNfxmzT63/vee3so9LyRmIh1o7+NARz8fOWUGP/rYUuZVFTCvyvpuYzHD+n3tnPHt57jiv18Zt2Xis1IQRISLFlXhsTv/eP+/384yyg14ef1LF3LDGTVprzOaywisUXlL94A7upxWnJhlNKUwh4sXVfGPlyxwz6kpszqn+Hz2bQ3dzCzNc+MeybgWgv1HsKe1j9Nnl7GstpRz5lnBeEcQwIo9OO3vtjOpnFFdVWHing9gxVsuPtYaqVYWBplbmY/XIzz9tjWK7uy3lrRwOm6wlgj5r2tPYmtDF194eB3GGLdzSYgh9A3PsopEY3QNRFy/anGen7yA181gmVKUQ2GO37U0HAFz3Eh77WDz9T9ZyZd+t8GNM8Rn6Dj+8tqK/CELwRa6ry4/limFOVx972uc/R/P8dcdLW4Q3VlqY3tTt1vmxGVEEl1Gbx/oxOsR3mOvffXoqn3k+r18bfmxLJlZ4tY7paaUN/a0J4wGt9m57fOr8plti3pteR5zK/LZ0Wx1GrtbephTkc8v//ZU/vj3Z/Ox02axfl8Hb+3voL03zMzSPHxeD3e8fxGfOmc2N50zh8fX1/O3D6zmP57cbAdG99LRF+aSY6vo6AvT0jNIOBpzM+gcl99r21v489sNfOb8uVQUWNaY1yOs3d2G12OtAVZru9tGiiMYY9zjq3e38d0/b+WpjQc5fnoxT21s4K87WjDGsPGAFatJZSFMK8ohN+Dl5Fkl5Ad93HL+XC49bgrfvvJ47rl+Cc3dg+6I/ccv7+A/n9rCT1/ZmbZNDm09g3zy56v43zX7MMYQixl3EOEMDoyxntmy2lJm26J4jD0g2drQ5d7bb9fuJxKNsaOpm/9+bhv3PL89wXJdY/+Wl84qdcsWVBXQF47yxt42bn5wDaWhAE1dAyy/+5VxcYdlpSAkkxfn7vF6hmIFHs/wuEE8jsvIa2+7mQpnPaMD7f0UBH0U5PiZXRHimlNmcN78SnxeD/fFWStgbcNZmONLyErZ2tDF3MrU8QMYcln1DkbpHYzQ3D3AWfPKefTm01k8vQiwOuhCWwRmlua5Vo0zoa3BnnRWmUIQAK5aMh2A2vJ8qgpz+OBJ1fxq5W4aO/vdH+vU4sRzz1tQyecumMszmxqo7+h33S3OVqPFuX7CUTMsGO7sT+EIgohQXZxL10AEkaGNihwBWDTVuscZcYLw6Oq9DERivLq9hT9tsIL0UbsTdZ4pWB2uIwiO5VOY4+fBG5fxqXNm09AxwPObG11Rd0TtZXvE6fcKa23/f21ZiJbuIYF7Y287cypCbuzntR0tLK0pcQceDqfUlNIXjroB6z9tqOebj2/C6xHmVha4VmpNuWUh9IdjbNjfQVtvmNryPGZX5FNbHnJdazfZ+fkLpiT+Zj597mwuWlTFrpYe7nlhO7c9uo5//9MmzphTxnWnzgKsdbaaugZcN2p77yCxmOEbj7/NtKIcbjyrFrBicTNL84jEDFOLcvB5PUwtzCHH72FHUw/Pb25MGShv6w3T2R9hcXURXf0RHl61h6tOns4vPrmMqUU5/PufNrOvrY/O/ggBr8edS+Owo7nHFZ7CHD8rv3QhX3jPPPf42fPK8XuFV+qaWbmjhW//aTNej7h7iIzEo6v38tzmRm77zZv882Pr2dPa61q3a+0OfFdLLzube7jihGnuefOqLGHY1tjNzuYeQgEvzd0DvLi1ibuf307MWLG6+Jn1a3a3kev3snBqQdx1rNfX3reStt5BfnLDUv78D+fwjQ8cx7S4wdbRQgWBRNeP3zuyCCSeZ3WuoYA3ZdAZrFF572CU7U3d7hfq93r49pXHM9O2BFJRUx5y/az94Sg7mns4Zmp6QciNm4fgBFWdzjGecnsL0JmleQR9XgJej5u+5/hdq5I2AXI4Y245L//z+Zw4w5pX+LkL5hKJGe55YXucBTT8R3vufMtCWb+vnS0Hu8nxe9wAt9PhO6PQHU3d7Gvrdd87xwG30y7PD7qJAc49OhZCRX6QUMDLb9/Yzy9e282M0lyiMcOTGw+69+XMrdh8sIvCHB9TCnM4dloRxXl+TphR7H7e7Ip8/uXShdSWhxKsAadtf9nWzKyyPBZOKWTDPqsjP2ZqIYPRGJ19EZ7f3MjL25q59LipzIr7Lk6bnTxnE06ptayFl7Y2s7e1l888tBaPCHdft4SiXL/rKqwtD7npwr+0U1UdF6P1jPJYOquEpu4B/vW9x7jP3iEv4OPHH1/KM7eey0dPm8n/rTtAKODj+x850f2MuqbuhAyy9r4wr25vYeOBTv7xkgUJVqpjuTjfjccj1JSFePj1PfzNz1fxvae3MhCJsvzuV/i/N6xsdWcEffVSa4AhwM3nzCHH7+XWi+bz5t52vvG4lXR49rxyGjqtuTN9g1GMMexo6k5w44aCvoS/v7yAjyUzS/hLXTP//XwdFQVB/u7cOexvH5pXtKOpm0u//1LC8ivGGB5ZtZeTZ5Vw/akzeWztPncjqOI8v2shOAs3xn+PUwpzyA/62NbQxc6mHq44sZry/AC3PPQGv3tjHx8/fRYleX7+uL6eR1ft5Ucvbuf1na2cOKM4YXCwYEoBBTk+jq0u5HefOZNjpxVRGgpw9dL4zP+jR2o/R5aRF4y3EDLXyOTgdCqcoPFb+zsSOpvRmFUWclcmfbu+k2jMcFx1Udr6obh5CE42y8xUgpAfZEdTj3ssFPS6ozjHZVRRkFoQIFFkZpWFeO/iqfzfuv3MsUds04qGC8IxUwvxe4U393Wwerf1R+B06I7Lp613kKc2HuSbj2/ijLnl3HqRtd9EvCBUJ43iAY6vLmLNrja3w/V4hG99aDH/9Jv1DEZj3HP9Eu56eivbGru5eukMfvhcnet/33KwiwVTChARqgpzWHfHxSnveU5liE31XUwrtqwYx0J4fVcr7zt+Kp19ETbYI/uFUwp4fEM925u7uf2361lQVcBnz59DwJ6E1zUQSSkIlQU5nDGnjEdX7yUSs9xGP//kMtcF95FlM5hRmsd0OxtqVlkev3/zAICbNOBwz/VL6AtHE4QiFf/2/mMJBX1cvKiKysIcYjFDrt/L9sYeSm0Lrjw/SHtv2B2cnDGnPOnZ5PPs5kY3SwtgdkWIzQe7CAW8/H7dAY6ZWsibe9v52Ss7+cBJ1a4gnDm3nNkVIU6YXuzGua5cMp1fv76HpzY24PUIFx9bxbObG3l+cyOff3gd3/zgcXT2R9xYRTrOmlvOd5/eCsA/XbLADaxvbejm5FklrNndxuaDXTy7qZGPnmZZRq/vbGVHcw/fOX8ui6uL+NXKPdzzwnb8XuEDJ1bz0Mo9DESivL6zlfL8gPubB8uCnVuZz8odrXQNRJhflc/HTjuVR1fvZXtTN7ecP5dw1PDIqj38wf7ewBpUxZMf9LHySxe6qyiMN2ohkOj/943hS3CEIG9EQbA617be8JhMvlmleexv7yMcjbHR7mwWjyAIziS7njhBmFEy/PPK7TiC07E7K7aClWZYGgq4y4NnwlnzymnvDfPytmZ8HkkpJjl+LwunFLqjzHj3WJGdfrrizQN89Q9v4xFJayFUF1ttnhLn0vrbs2fz/D+el/DHs/zEan5902nccv5cLl5Uxftt0/68BZVMK8pxA6dbGrqGuVRSMacinz2tva4Lry8cpaV7gK7+CLPKQgkiudD2Jd//8k4aOgf49ysXE/RZFuTMsjxy/V6On576e7zu1Jnsb+/jRy/t4Ky55QnxmMIcP5ceN8V9f/6CSjcWkiz8lYU5o4oBWJbqFy87hpNtH7bHI8ypDCVYCAunFNDRF6a+oy/l9ztkIQy14bpls/jMeXP47tUn0Nw9wNf/aI3239zXwc7mHnY19+D1CDNK81hxy1nceeXx7rmOoPs8kuBq+8Gz2xiMxtxVdGenSfRwOHOeJVxBn4drl810v2fHbeRkUMXPk3nwr7spyPHx3sVT3bhNY9cA8yoLOG12KYPRGG/t72TlzlaW1ZYO8wrMi5sAWFseYtG0Qr5yxbE8eOOpVBbm8KEl1RjghtNn8Z0Pn8CM0lwuOXYKyeQFfBMiBqAWAmC5fBySfbsj4QRl0wWUYWi2MpDwBz4as8ryiMYM+9v62LC/g9JQIGFknIzH3p+hbzBCZ1+YUMDrZjDF42SrOCZ+KDAkCI2d/VSOYB2kwuncX9jSRFVhTkIMJp7jp1sjLrBmMjs4FsKKdQfI8Xu4csl0Vqw74KaiOsdhyEKIF1avR1x3WTwnzyrh5FmWG+aTZ9VSXZzLkpnFzKnMZ3tTj7uS7YI08zrimVORTzRm2NfWR17Aa832tv/wpxTmuKKV6/e6nfOf3z5IbXmIk+KswuUnTqO1J5z2N3bxoimU5wdo7h4c1UVw3oIKfv7qLqYU5qS8/0NlTkU+q3e1ccyUAgI+DzXleWw80EF9e3/K73eO7WaaHjf4OGteOWfNK2cwEqMkz09bb5i/PauW+1/ZyR/ePMDOFmsyoN/rSfksFk4p5M4rjycv4HWtn80Hu/B6xE3pTZf553B8dRHl+UEuPa6K0lCA4lw/oYCXLfbaTI7776/brQD2hv0d/HF9PZ85b477PC89bgr3vLCd46oLWTKrBBFrb5H97X3cdM7sYZ8ZnwE4O4UFc0pNKWv/9SKK8/yICFedPH3Ee5gI1EIgsUNP16GlYmgeQ/o/yPLQUAc7UoeejGNC72rpYcP+To6rLkobp3AIBb30DEbd/PNU9a9eOoM73rfI9QMX5PjcpSsaOgfcLJuM21mWR3m+tXrrSIJ3wnSrY/QILJk51Ek6nWl9Rz+n1JRSXWIFjp0RXGGChZCYCZQp+UEfV548HRFhTkU+25u63UXbFkwpHOVs3IlqVn3b9WCPNCsLg64IVBQE3RF0OGq4aFFVwndw0zlzuP2yhWk/J+DzcMPpNUwtynF35EvHabPLCPo8zBohDnUozK3IZ3+7NQipKgxSkhegoy/M/va+lM99ycwSvnT5Qi45bvhIN+Dz8IGTqgkFvHzugnmcUlPKo6v3sn5f+zA3VzJXnjydyxZPpTjP77pmv3DhPHweweeRBAFKhc/r4c//cA53vO9YwBowzZ9S4Aq5k0La0jPI1oZuvvHHTe4kMwcnQH/89GIqC3K47aL5bprxstpSknECy36vuIOXZEpCgVH/jicSFQQSJ5WNJagc9HnweyUhSymZeAthrC4jsDJhtjV0sbh69I4rN+Clz3YZpYofABxXXcQn7SwRsMSwZ9ARBGtZirEgIu5IPDnDKB4nfrJwSmHCUuLxFsDpc8rc7CFnPZd4l9H8qnzmVIQ4pWYoXXOszK3Mp3cwyhN21lEmFoKTWghD6YVb7PZVFQ7NmC7Pt0aizqBitE49FbdcMJeX/vn8tOnFDjl+L1+8bCF/c2bNmD9jJC5bPJVcv5dXt7e41k/MWNkzqQY0Xo9w0zlz0sbR/uXShTxz27kU5fn59Lmzae4eYG9rX0JmzUiICLPK8vB5hI+dPosrTpjGsdVF7iKUI1EaCrixKrC+6y0HuzDGcLCzn/l2B/7Zh9by+q5W/uGi+Qm/zeOqi3j05tP5sB38/uz5c7nxrFrmVuan/N04ExpnlYXGNLCcTKjLCNyF4YCMfmgOIkJ+0DdiUDkU8BL0eRiIjDyCTsZZBO+eF7YTiRmOm5Y+fjD0WZb7Z09r77DsknTkB33saeklEo3R3D2QNsNoJE6pKeWpjQ0jCt7cynxKQwHOnJsYUM31W5lOg9EYZ8wpd7NAtjVa2Ujx8YyCHD/P3nbemNsXz5lzywkFvDy2Zp/V4cUJUjryg1Ym0sHO/oR8c7AEIcfnwecRyvODeDxCWShAJGYS5hlkiohkPCj5xJm1o1caI3Mr8/mPq47nc79+gylFuW56cGvP4CGlPeb4ve5kxQsWVrH+3y5ha0NXgtU1GstPqObMOQMU5wX49pXHEzvEVUUXTCng4VV7aeoa4GBHPxcfO4XewSh1jd3cfO5srj1l5rBz4i0BEeH/vW8R/2pMylF+dXEuOX7PqO6syYwKAolZQmMJKgN84oxajp2WfvQuYnUQ9Z39KSd8jXTejz++lFseWks7YXcuwUjkBrxsa+hiIBIbMaU1nvygJSItPYPETPo5CCPhxARG6jC8HuGJvz87wSIA6z4Lc/0MhKMcN63Q3Q2urrE7wTo4UtSWh/jdZ8/k5gfXcNLMzLO+5lSGONjZ7y4IuNXOonF+OxcfW8Uy+zmcM7+CaUXp4ymTnfefMI2YMcypyE+YnTvlEH4byQR8nhGz5VLxqTh/fSDNfJ9McMT8zX0dtPQMMrUoh/+46nj6BqPu8iCZkM7l4/EIX7r8GOZWjJwBNZlRQSD10hWZ8vm4CTHpKMsPEomZMf+YT55VwhN/fzbbGrsTsjjSkR/08caedoI+T8rUxnTndMf57MciWg4nTC/iWx9czHuPnzpivXS+/+qSXKbZE5ucoHb3QGRMFtVYmF9VwHO3ncsoi38mMK+ygFU729zJgV0DkYRMl3uuP9l9/Z0Pn3DE2jpRLD/R2gMrfmbttBFcgu8EHEFwNvCZUpgzLI32cPn46TVH9HrjjQoCiRbC0RjVza4IDRsZZ0pJKJAygJWKz5w3l3PnV/CBk6rdbKLRCAV99NrbbEL6SWkjISJcd+pwcztTfvLxpQnzEhwX0tGwEBxEhDGEi/j0uXO4YGElhTk+/F4hHDVUHsKzeqcR/7udmmKOyTuJolw/00tyeW6TLQhjTE7IBlQQGFq6whe3z8CR5NsfOnS/51g4fU6Zu89ypjgZHM7s3UOxEA6X+Nx2ESvXfX97X0KG0UQzpSjH7UCKcgN2vOXd36E480RgbFlyk5VFUwv5s73+1rvhfo40mmWE5ZcMeD3u0tdHmtyAd8S5ChOJ064397YTCnjHPA/haOCMvI+mhXA4OKPm7BAE6179XsnY6pzMxK/iW6WCMIzJ2UtNAHlBL9HoxG1uPVE47rI39rQzt6pgUuRIO6I0aQXBbtdkEM+jTcDnIRTwUhIKTNjs2SOJs0x6KOBN2PFQsVALwSYU8OE9ShbCZMYRBGuK/uTIjnDmIkxaQcgiCwGslWlTrVH1TuRYO8NpSlHOpBj8TDZUIm1CQS8DI+zv+m4lfk+HySMI1sj7UAPxRxvHr54tgnDO/IpRZwa/U5hWZE2204ByalQQbEJBn7sMdDYRP0vbmXo/0Uz2GEKJayG8+11GAP+eZgvZdyIiwucvnJc1Yj5WVBBsQgHfO3Yi0eFQkGAhZLacwNHGmRw3WQVhmj0jVTuVdybxS7coiagg2ISC3jGtdPpuwckyyvV7j9pEsLFyam0pnzq7NuP5F+PNdafO5IKFlaOuN6Qo7zRUEGyuXjpjXPYsnWw4G+vMrcyfNFkkeQEfX37vooluRlpy/F53NVpFeTehgmAzlrVM3k0422hOlviBoigThwqCwu2XLXSXsFYUJXtRQVA0yKYoCqAT0xRFURSbjARBRC4VkS0iUicit6c4HhSRR+zjK0Wkxi6/XkTWxf2LiciJSeeuEJG3jsTNKIqiKIfOqIIgIl7gbuAyYBFwrYgkp4DcCLQZY+YCdwF3AhhjfmWMOdEYcyLwMWCnMWZd3LU/BHQfkTtRFEVRDotMLIRlQJ0xZocxZhB4GFieVGc58ID9+jHgQhm+UMi19rkAiEg+cCvwjUNpuKIoinJkyUQQqoG9ce/32WUp6xhjIkAHkLww/0eAX8e9/zrwXaB3pA8XkZtEZLWIrG5qasqguYqiKMqhMC5BZRE5Feg1xrxlvz8RmGOM+d1o5xpj7jPGLDXGLK2oyGzjeEVRFGXsZCII+4EZce+n22Up64iIDygCWuKOX0OidXA6sFREdgF/AeaLyAtjabiiKIpyZMlEEFYB80SkVkQCWJ37iqQ6K4Ab7NdXAc8ZY+0ZKSIe4Gri4gfGmP8xxkwzxtQAZwFbjTHnHc6NKIqiKIfHqBPTjDEREbkFeArwAj81xmwUka8Bq40xK4D7gQdFpA5oxRINh3OAvcaYHYfb2DVr1jSLyO4xnlYONB/uZx8lJmvbtN+NVmYAAASzSURBVF1jY7K2CyZv27RdY+Nw2zUrk0pixmHz94lERFYbY5ZOdDtSMVnbpu0aG5O1XTB526btGhvj1S6dqawoiqIAKgiKoiiKTTYIwn0T3YARmKxt03aNjcnaLpi8bdN2jY1xade7PoagKIqiZEY2WAiKoihKBryrBWG0VVrHsR0zROR5EXlbRDaKyOft8q+IyP641WAvn4C27RKRDfbnr7bLSkXkaRHZZv8/7rvniMiCpJVyO0XkCxPxzETkpyLSGL8qb7pnJBb/Zf/m1ovIknFu13+KyGb7s38nIsV2eY2I9MU9t3vHuV1pvzcR+aL9vLaIyCVHq10jtO2RuHbtEpF1dvl4PrN0fcT4/s6MMe/Kf1hzJrYDs4EA8CawaILaMhVYYr8uALZirRz7FeAfJ/g57QLKk8r+A7jdfn07cOck+C4PYuVSj/szw5pLswR4a7RnBFwO/AkQ4DRg5Ti362LAZ7++M65dNfH1JuB5pfze7L+DN4EgUGv/zXrHs21Jx78L3DEBzyxdHzGuv7N3s4WQySqt44Ixpt4Ys9Z+3QVsYvgCgZOJ+NVrHwA+MIFtAbgQ2G6MGeukxCOCMeYlrAmX8aR7RsuBXxiLvwLFIjJ1vNpljPmzsRaYBPgr1lIz40qa55WO5cDDxpgBY8xOoA7rb3fc2yYigrWqwq9THT+ajNBHjOvv7N0sCJms0jruiLV50EnASrvoFtvk++lEuGYAA/xZRNaIyE12WZUxpt5+fRComoB2xZO8FtZEPzNI/4wm0+/uk1ijSIdaEXlDRF4UkbMnoD2pvrfJ9LzOBhqMMdviysb9mSX1EeP6O3s3C8KkQ6w9IP4X+IIxphP4H2AOcCJQj2WujjdnGWOWYG2A9FkROSf+oLHs0wlLRRNr/awrgN/YRZPhmSUw0c8oFSLyZSAC/MouqgdmGmNOwtqH5CERKRzHJk267y0F15I48Bj3Z5aij3AZj9/Zu1kQMlmlddwQET/WF/0rY8xvAYwxDcaYqDEmBvyYo2gqp8MYs9/+vxH4nd2GBsf8tP9vHO92xXEZsNYY0wCT45nZpHtGE/67E5FPAO8Drrc7EWyXTIv9eg2Wr37+eLVphO9twp8XuKs0fwh4xCkb72eWqo9gnH9n72ZByGSV1nHB9k3eD2wyxnwvrjze5/dBYFz3lhaRkIgUOK+xApJvkbh67Q3A78ezXUkkjNom+pnFke4ZrQA+bmeBnAZ0xJn8Rx0RuRT4Z+AKY0xvXHmFWNvhIiKzgXnAYS84OYZ2pfveVgDXiLUve63drtfHq11xvAfYbIzZ5xSM5zNL10cw3r+z8YigT9Q/rEj8Vixl//IEtuMsLFNvPbDO/nc58CCwwS5fAUwd53bNxsrweBPY6DwjrN3ungW2Ac8ApRP03EJY+2oUxZWN+zPDEqR6IIzlq70x3TPCyvq42/7NbQCWjnO76rB8y87v7F677pX2d7wOWAu8f5zblfZ7A75sP68twGXj/V3a5T8HPp1UdzyfWbo+Ylx/ZzpTWVEURQHe3S4jRVEUZQyoICiKoiiACoKiKIpio4KgKIqiACoIiqIoio0KgqIoigKoICiKoig2KgiKoigKAP8ftO5VMR2duu4AAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -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", 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\n", 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\n", "text/plain": [ "
" ] @@ -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",