work5_앙상블.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 랜덤 포레스트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BAD</th>\n",
       "      <th>LOAN</th>\n",
       "      <th>MORTDUE</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>REASON</th>\n",
       "      <th>JOB</th>\n",
       "      <th>YOJ</th>\n",
       "      <th>DEROG</th>\n",
       "      <th>DELINQ</th>\n",
       "      <th>CLAGE</th>\n",
       "      <th>NINQ</th>\n",
       "      <th>CLNO</th>\n",
       "      <th>DEBTINC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1700</td>\n",
       "      <td>30548</td>\n",
       "      <td>40320.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Other</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.466002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>37.113614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1800</td>\n",
       "      <td>28502</td>\n",
       "      <td>43034.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Other</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.766030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>36.884894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2300</td>\n",
       "      <td>102370</td>\n",
       "      <td>120953.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Office</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.992533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>31.588503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2400</td>\n",
       "      <td>34863</td>\n",
       "      <td>47471.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Mgr</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.491080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>38.263601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2400</td>\n",
       "      <td>98449</td>\n",
       "      <td>117195.0</td>\n",
       "      <td>HomeImp</td>\n",
       "      <td>Office</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.811775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>29.681827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BAD  LOAN  MORTDUE     VALUE   REASON     JOB   YOJ  DEROG  DELINQ  \\\n",
       "0    1  1700    30548   40320.0  HomeImp   Other   9.0      0     0.0   \n",
       "1    1  1800    28502   43034.0  HomeImp   Other  11.0      0     0.0   \n",
       "2    0  2300   102370  120953.0  HomeImp  Office   2.0      0     0.0   \n",
       "3    1  2400    34863   47471.0  HomeImp     Mgr  12.0      0     0.0   \n",
       "4    0  2400    98449  117195.0  HomeImp  Office   4.0      0     0.0   \n",
       "\n",
       "        CLAGE  NINQ  CLNO    DEBTINC  \n",
       "0  101.466002   1.0     8  37.113614  \n",
       "1   88.766030   0.0     8  36.884894  \n",
       "2   90.992533   0.0    13  31.588503  \n",
       "3   70.491080   1.0    21  38.263601  \n",
       "4   93.811775   0.0    13  29.681827  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_hmeq = pd.read_csv(\"실습파일/HMEQ.csv\", engine=\"python\")\n",
    "ds_hmeq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BAD          0\n",
       "LOAN         0\n",
       "MORTDUE      0\n",
       "VALUE       14\n",
       "REASON       0\n",
       "JOB         81\n",
       "YOJ        205\n",
       "DEROG        0\n",
       "DELINQ      30\n",
       "CLAGE       37\n",
       "NINQ        56\n",
       "CLNO         0\n",
       "DEBTINC      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_hmeq.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_hmeq[\"JOB\"].fillna(\"Other\", inplace = True)\n",
    "ds_hmeq.fillna(ds_hmeq.mean(), inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>JOB_Mgr</th>\n",
       "      <th>JOB_Office</th>\n",
       "      <th>JOB_Other</th>\n",
       "      <th>JOB_ProfExe</th>\n",
       "      <th>JOB_Sales</th>\n",
       "      <th>JOB_Self</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2400</td>\n",
       "      <td>34863</td>\n",
       "      <td>47471.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.491080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>38.263601</td>\n",
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       "      <td>2400</td>\n",
       "      <td>98449</td>\n",
       "      <td>117195.0</td>\n",
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      "text/plain": [
       "   BAD  LOAN  MORTDUE     VALUE   YOJ  DEROG  DELINQ       CLAGE  NINQ  CLNO  \\\n",
       "0    1  1700    30548   40320.0   9.0      0     0.0  101.466002   1.0     8   \n",
       "1    1  1800    28502   43034.0  11.0      0     0.0   88.766030   0.0     8   \n",
       "2    0  2300   102370  120953.0   2.0      0     0.0   90.992533   0.0    13   \n",
       "3    1  2400    34863   47471.0  12.0      0     0.0   70.491080   1.0    21   \n",
       "4    0  2400    98449  117195.0   4.0      0     0.0   93.811775   0.0    13   \n",
       "\n",
       "     DEBTINC  REASON_DebtCon  REASON_HomeImp  JOB_Mgr  JOB_Office  JOB_Other  \\\n",
       "0  37.113614               0               1        0           0          1   \n",
       "1  36.884894               0               1        0           0          1   \n",
       "2  31.588503               0               1        0           1          0   \n",
       "3  38.263601               0               1        1           0          0   \n",
       "4  29.681827               0               1        0           1          0   \n",
       "\n",
       "   JOB_ProfExe  JOB_Sales  JOB_Self  \n",
       "0            0          0         0  \n",
       "1            0          0         0  \n",
       "2            0          0         0  \n",
       "3            0          0         0  \n",
       "4            0          0         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_hmeq_dummy = pd.get_dummies(ds_hmeq)\n",
    "ds_hmeq_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data X size:(2604, 18)\n",
      "train data Y size:(2604,)\n",
      "test data X size:(1144, 18)\n",
      "test data Y size:(1144,)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(seed=1234)\n",
    "\n",
    "msk = np.random.rand(ds_hmeq_dummy.shape[0]) < 0.7\n",
    "ds_hmeq_train = ds_hmeq_dummy[msk]\n",
    "ds_hmeq_test = ds_hmeq_dummy[~msk]\n",
    "\n",
    "ds_hmeq_train_y = ds_hmeq_train[\"BAD\"]\n",
    "ds_hmeq_train_x = ds_hmeq_train.drop(\"BAD\", axis = 1, inplace = False)\n",
    "ds_hmeq_test_y = ds_hmeq_test[\"BAD\"]\n",
    "ds_hmeq_test_x = ds_hmeq_test.drop(\"BAD\", axis = 1, inplace = False)\n",
    "\n",
    "print(\"train data X size:{}\".format(ds_hmeq_train_x.shape))\n",
    "print(\"train data Y size:{}\".format(ds_hmeq_train_y.shape))\n",
    "print(\"test data X size:{}\".format(ds_hmeq_test_x.shape))\n",
    "print(\"test data Y size:{}\".format(ds_hmeq_test_y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.992\n",
      "Accuracy on test set: 0.948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "rf_uncustomized = RandomForestClassifier(random_state=1234)\n",
    "rf_uncustomized.fit(ds_hmeq_train_x, ds_hmeq_train_y)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(rf_uncustomized.score(ds_hmeq_train_x, ds_hmeq_train_y)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(rf_uncustomized.score(ds_hmeq_test_x, ds_hmeq_test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=1234, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_uncustomized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by the scale function.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by the scale function.\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOAN</th>\n",
       "      <th>MORTDUE</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>YOJ</th>\n",
       "      <th>DEROG</th>\n",
       "      <th>DELINQ</th>\n",
       "      <th>CLAGE</th>\n",
       "      <th>NINQ</th>\n",
       "      <th>CLNO</th>\n",
       "      <th>DEBTINC</th>\n",
       "      <th>REASON_DebtCon</th>\n",
       "      <th>REASON_HomeImp</th>\n",
       "      <th>JOB_Mgr</th>\n",
       "      <th>JOB_Office</th>\n",
       "      <th>JOB_Other</th>\n",
       "      <th>JOB_ProfExe</th>\n",
       "      <th>JOB_Sales</th>\n",
       "      <th>JOB_Self</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.546445</td>\n",
       "      <td>-0.994709</td>\n",
       "      <td>-1.227453</td>\n",
       "      <td>-0.020546</td>\n",
       "      <td>-0.26517</td>\n",
       "      <td>-0.337599</td>\n",
       "      <td>-0.962571</td>\n",
       "      <td>-0.019984</td>\n",
       "      <td>-1.424138</td>\n",
       "      <td>0.372555</td>\n",
       "      <td>-1.566391</td>\n",
       "      <td>1.566391</td>\n",
       "      <td>-0.374973</td>\n",
       "      <td>-0.446595</td>\n",
       "      <td>1.217524</td>\n",
       "      <td>-0.587402</td>\n",
       "      <td>-0.135576</td>\n",
       "      <td>-0.184808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.537818</td>\n",
       "      <td>-1.040082</td>\n",
       "      <td>-1.177823</td>\n",
       "      <td>0.250021</td>\n",
       "      <td>-0.26517</td>\n",
       "      <td>-0.337599</td>\n",
       "      <td>-1.117329</td>\n",
       "      <td>-0.663021</td>\n",
       "      <td>-1.424138</td>\n",
       "      <td>0.344145</td>\n",
       "      <td>-1.566391</td>\n",
       "      <td>1.566391</td>\n",
       "      <td>-0.374973</td>\n",
       "      <td>-0.446595</td>\n",
       "      <td>1.217524</td>\n",
       "      <td>-0.587402</td>\n",
       "      <td>-0.135576</td>\n",
       "      <td>-0.184808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.494683</td>\n",
       "      <td>0.598052</td>\n",
       "      <td>0.247041</td>\n",
       "      <td>-0.967531</td>\n",
       "      <td>-0.26517</td>\n",
       "      <td>-0.337599</td>\n",
       "      <td>-1.090197</td>\n",
       "      <td>-0.663021</td>\n",
       "      <td>-0.898712</td>\n",
       "      <td>-0.313747</td>\n",
       "      <td>-1.566391</td>\n",
       "      <td>1.566391</td>\n",
       "      <td>-0.374973</td>\n",
       "      <td>2.239164</td>\n",
       "      <td>-0.821339</td>\n",
       "      <td>-0.587402</td>\n",
       "      <td>-0.135576</td>\n",
       "      <td>-0.184808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.442920</td>\n",
       "      <td>0.633069</td>\n",
       "      <td>0.092557</td>\n",
       "      <td>-1.102815</td>\n",
       "      <td>-0.26517</td>\n",
       "      <td>-0.337599</td>\n",
       "      <td>-1.027931</td>\n",
       "      <td>-0.663021</td>\n",
       "      <td>-0.898712</td>\n",
       "      <td>-0.504711</td>\n",
       "      <td>-1.566391</td>\n",
       "      <td>1.566391</td>\n",
       "      <td>-0.374973</td>\n",
       "      <td>2.239164</td>\n",
       "      <td>-0.821339</td>\n",
       "      <td>-0.587402</td>\n",
       "      <td>-0.135576</td>\n",
       "      <td>-0.184808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.442920</td>\n",
       "      <td>0.642472</td>\n",
       "      <td>0.242451</td>\n",
       "      <td>-0.967531</td>\n",
       "      <td>-0.26517</td>\n",
       "      <td>-0.337599</td>\n",
       "      <td>-0.961666</td>\n",
       "      <td>-0.663021</td>\n",
       "      <td>-0.898712</td>\n",
       "      <td>-0.521514</td>\n",
       "      <td>-1.566391</td>\n",
       "      <td>1.566391</td>\n",
       "      <td>-0.374973</td>\n",
       "      <td>2.239164</td>\n",
       "      <td>-0.821339</td>\n",
       "      <td>-0.587402</td>\n",
       "      <td>-0.135576</td>\n",
       "      <td>-0.184808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LOAN   MORTDUE     VALUE       YOJ    DEROG    DELINQ     CLAGE  \\\n",
       "0 -1.546445 -0.994709 -1.227453 -0.020546 -0.26517 -0.337599 -0.962571   \n",
       "1 -1.537818 -1.040082 -1.177823  0.250021 -0.26517 -0.337599 -1.117329   \n",
       "2 -1.494683  0.598052  0.247041 -0.967531 -0.26517 -0.337599 -1.090197   \n",
       "3 -1.442920  0.633069  0.092557 -1.102815 -0.26517 -0.337599 -1.027931   \n",
       "4 -1.442920  0.642472  0.242451 -0.967531 -0.26517 -0.337599 -0.961666   \n",
       "\n",
       "       NINQ      CLNO   DEBTINC  REASON_DebtCon  REASON_HomeImp   JOB_Mgr  \\\n",
       "0 -0.019984 -1.424138  0.372555       -1.566391        1.566391 -0.374973   \n",
       "1 -0.663021 -1.424138  0.344145       -1.566391        1.566391 -0.374973   \n",
       "2 -0.663021 -0.898712 -0.313747       -1.566391        1.566391 -0.374973   \n",
       "3 -0.663021 -0.898712 -0.504711       -1.566391        1.566391 -0.374973   \n",
       "4 -0.663021 -0.898712 -0.521514       -1.566391        1.566391 -0.374973   \n",
       "\n",
       "   JOB_Office  JOB_Other  JOB_ProfExe  JOB_Sales  JOB_Self  \n",
       "0   -0.446595   1.217524    -0.587402  -0.135576 -0.184808  \n",
       "1   -0.446595   1.217524    -0.587402  -0.135576 -0.184808  \n",
       "2    2.239164  -0.821339    -0.587402  -0.135576 -0.184808  \n",
       "3    2.239164  -0.821339    -0.587402  -0.135576 -0.184808  \n",
       "4    2.239164  -0.821339    -0.587402  -0.135576 -0.184808  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_hmeq_train_x_scaled = scale(ds_hmeq_train_x,axis = 0)\n",
    "ds_hmeq_test_x_scaled = scale(ds_hmeq_test_x,axis = 0)\n",
    "pd.DataFrame(ds_hmeq_train_x_scaled, columns=ds_hmeq_train_x.columns).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.992\n",
      "Accuracy on test set: 0.946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "rf_scaled = RandomForestClassifier(random_state=1234)\n",
    "rf_scaled.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(rf_scaled.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(rf_scaled.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_iter_tree = 30\n",
    "\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "for n_tree in range(n_iter_tree):\n",
    "    rf1 = RandomForestClassifier(n_estimators=n_tree +1, random_state=1234)\n",
    "    rf1.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(rf1.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(rf1.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "    \n",
    "ds_accuracy1 = pd.DataFrame()\n",
    "ds_accuracy1[\"NumberofTree\"] = [n_tree +1 for n_tree in range(n_iter_tree)]\n",
    "ds_accuracy1[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy1[\"TestAccuracy\"] = test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NumberofTree</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.970430</td>\n",
       "      <td>0.926573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.968510</td>\n",
       "      <td>0.937063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.988479</td>\n",
       "      <td>0.941434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.981183</td>\n",
       "      <td>0.942308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.991167</td>\n",
       "      <td>0.942308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.986175</td>\n",
       "      <td>0.944056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.992704</td>\n",
       "      <td>0.947552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.989247</td>\n",
       "      <td>0.944930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.995008</td>\n",
       "      <td>0.944056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.991935</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.995392</td>\n",
       "      <td>0.944056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.994624</td>\n",
       "      <td>0.943182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.996544</td>\n",
       "      <td>0.943182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.995776</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.998080</td>\n",
       "      <td>0.948427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.997312</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.997696</td>\n",
       "      <td>0.947552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.997312</td>\n",
       "      <td>0.947552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0.998464</td>\n",
       "      <td>0.947552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>0.997312</td>\n",
       "      <td>0.946678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0.998848</td>\n",
       "      <td>0.947552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>0.997312</td>\n",
       "      <td>0.946678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>0.998848</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>0.998464</td>\n",
       "      <td>0.946678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0.998464</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>0.998080</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0.998464</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0.998464</td>\n",
       "      <td>0.946678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0.998848</td>\n",
       "      <td>0.947552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0.998464</td>\n",
       "      <td>0.948427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    NumberofTree  TrainAccuracy  TestAccuracy\n",
       "0              1       0.970430      0.926573\n",
       "1              2       0.968510      0.937063\n",
       "2              3       0.988479      0.941434\n",
       "3              4       0.981183      0.942308\n",
       "4              5       0.991167      0.942308\n",
       "5              6       0.986175      0.944056\n",
       "6              7       0.992704      0.947552\n",
       "7              8       0.989247      0.944930\n",
       "8              9       0.995008      0.944056\n",
       "9             10       0.991935      0.945804\n",
       "10            11       0.995392      0.944056\n",
       "11            12       0.994624      0.943182\n",
       "12            13       0.996544      0.943182\n",
       "13            14       0.995776      0.945804\n",
       "14            15       0.998080      0.948427\n",
       "15            16       0.997312      0.945804\n",
       "16            17       0.997696      0.947552\n",
       "17            18       0.997312      0.947552\n",
       "18            19       0.998464      0.947552\n",
       "19            20       0.997312      0.946678\n",
       "20            21       0.998848      0.947552\n",
       "21            22       0.997312      0.946678\n",
       "22            23       0.998848      0.945804\n",
       "23            24       0.998464      0.946678\n",
       "24            25       0.998464      0.945804\n",
       "25            26       0.998080      0.945804\n",
       "26            27       0.998464      0.945804\n",
       "27            28       0.998464      0.946678\n",
       "28            29       0.998848      0.947552\n",
       "29            30       0.998464      0.948427"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_accuracy1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10d456a7f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_accuracy1.set_index(\"NumberofTree\", inplace = False).plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criterion</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gini</td>\n",
       "      <td>0.991935</td>\n",
       "      <td>0.945804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entropy</td>\n",
       "      <td>0.994624</td>\n",
       "      <td>0.942308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Criterion  TrainAccuracy  TestAccuracy\n",
       "0      gini       0.991935      0.945804\n",
       "1   entropy       0.994624      0.942308"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "para_criterion = [\"gini\", \"entropy\"]\n",
    "\n",
    "for criterion in para_criterion:\n",
    "    rf2 = RandomForestClassifier(criterion=criterion, random_state=1234)\n",
    "    rf2.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(rf2.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(rf2.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "    \n",
    "ds_accuracy2 = pd.DataFrame()\n",
    "ds_accuracy2[\"Criterion\"] = para_criterion\n",
    "ds_accuracy2[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy2[\"TestAccuracy\"] = test_accuracy\n",
    "ds_accuracy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "para_max_depth = [i+1 for i in range(10)]*2\n",
    "n_iter_depth = 10\n",
    "\n",
    "para_criterion = [\"gini\"]*n_iter_depth + [\"entropy\"]*n_iter_depth\n",
    "\n",
    "for (criterion, depth) in zip(para_criterion, para_max_depth):\n",
    "    rf3 = RandomForestClassifier(criterion=criterion, max_depth=depth, random_state=1234)\n",
    "    rf3.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(rf3.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(rf3.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "\n",
    "ds_accuracy3 = pd.DataFrame()\n",
    "ds_accuracy3[\"Criterion\"] = para_criterion\n",
    "ds_accuracy3[\"Depth\"] = para_max_depth\n",
    "ds_accuracy3[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy3[\"TestAccuracy\"] = test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criterion</th>\n",
       "      <th>Depth</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>0.908986</td>\n",
       "      <td>0.910839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gini</td>\n",
       "      <td>2</td>\n",
       "      <td>0.913594</td>\n",
       "      <td>0.917832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gini</td>\n",
       "      <td>3</td>\n",
       "      <td>0.931644</td>\n",
       "      <td>0.932692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>0.933948</td>\n",
       "      <td>0.930944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>0.937788</td>\n",
       "      <td>0.936189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gini</td>\n",
       "      <td>6</td>\n",
       "      <td>0.939708</td>\n",
       "      <td>0.936189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>0.943548</td>\n",
       "      <td>0.930070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gini</td>\n",
       "      <td>8</td>\n",
       "      <td>0.947005</td>\n",
       "      <td>0.929196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gini</td>\n",
       "      <td>9</td>\n",
       "      <td>0.947773</td>\n",
       "      <td>0.937063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>0.952765</td>\n",
       "      <td>0.931818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>entropy</td>\n",
       "      <td>1</td>\n",
       "      <td>0.907066</td>\n",
       "      <td>0.909965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>entropy</td>\n",
       "      <td>2</td>\n",
       "      <td>0.910906</td>\n",
       "      <td>0.914336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>entropy</td>\n",
       "      <td>3</td>\n",
       "      <td>0.928187</td>\n",
       "      <td>0.929196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>entropy</td>\n",
       "      <td>4</td>\n",
       "      <td>0.932412</td>\n",
       "      <td>0.933566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>0.932796</td>\n",
       "      <td>0.931818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>entropy</td>\n",
       "      <td>6</td>\n",
       "      <td>0.936252</td>\n",
       "      <td>0.937063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>0.939708</td>\n",
       "      <td>0.937937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>entropy</td>\n",
       "      <td>8</td>\n",
       "      <td>0.946237</td>\n",
       "      <td>0.938811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>entropy</td>\n",
       "      <td>9</td>\n",
       "      <td>0.951613</td>\n",
       "      <td>0.939685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>0.959293</td>\n",
       "      <td>0.938811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Criterion  Depth  TrainAccuracy  TestAccuracy\n",
       "0       gini      1       0.908986      0.910839\n",
       "1       gini      2       0.913594      0.917832\n",
       "2       gini      3       0.931644      0.932692\n",
       "3       gini      4       0.933948      0.930944\n",
       "4       gini      5       0.937788      0.936189\n",
       "5       gini      6       0.939708      0.936189\n",
       "6       gini      7       0.943548      0.930070\n",
       "7       gini      8       0.947005      0.929196\n",
       "8       gini      9       0.947773      0.937063\n",
       "9       gini     10       0.952765      0.931818\n",
       "10   entropy      1       0.907066      0.909965\n",
       "11   entropy      2       0.910906      0.914336\n",
       "12   entropy      3       0.928187      0.929196\n",
       "13   entropy      4       0.932412      0.933566\n",
       "14   entropy      5       0.932796      0.931818\n",
       "15   entropy      6       0.936252      0.937063\n",
       "16   entropy      7       0.939708      0.937937\n",
       "17   entropy      8       0.946237      0.938811\n",
       "18   entropy      9       0.951613      0.939685\n",
       "19   entropy     10       0.959293      0.938811"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_accuracy3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10d45dc8d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_accuracy3_melt = pd.melt(ds_accuracy3, id_vars =[\"Criterion\", \"Depth\"])\n",
    "ds_accuracy3_melt[\"Accuracy\"] = ds_accuracy3_melt[\"Criterion\"] + \"_\" + ds_accuracy3_melt[\"variable\"]\n",
    "sns.lineplot(x=\"Depth\", y=\"value\", hue=\"Accuracy\", data=ds_accuracy3_melt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criterion</th>\n",
       "      <th>Depth</th>\n",
       "      <th>MinLeafSize</th>\n",
       "      <th>TrainAccuracy</th>\n",
       "      <th>TestAccuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.933948</td>\n",
       "      <td>0.930944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.933948</td>\n",
       "      <td>0.927448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.933948</td>\n",
       "      <td>0.929196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.932796</td>\n",
       "      <td>0.926573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.932796</td>\n",
       "      <td>0.932692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.932028</td>\n",
       "      <td>0.930070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.929724</td>\n",
       "      <td>0.930944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.929724</td>\n",
       "      <td>0.933566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.929724</td>\n",
       "      <td>0.930944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.929339</td>\n",
       "      <td>0.930070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Criterion  Depth  MinLeafSize  TrainAccuracy  TestAccuracy\n",
       "0      gini      4            1       0.933948      0.930944\n",
       "1      gini      4            2       0.933948      0.927448\n",
       "2      gini      4            3       0.933948      0.929196\n",
       "3      gini      4            4       0.932796      0.926573\n",
       "4      gini      4            5       0.932796      0.932692\n",
       "5      gini      4            6       0.932028      0.930070\n",
       "6      gini      4            7       0.929724      0.930944\n",
       "7      gini      4            8       0.929724      0.933566\n",
       "8      gini      4            9       0.929724      0.930944\n",
       "9      gini      4           10       0.929339      0.930070"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "n_depth = 4\n",
    "n_iter_min_leaf_size = 10\n",
    "para_criterion = [\"gini\"]*n_iter_min_leaf_size\n",
    "para_min_leaf_size = [i+1 for i in range(n_iter_min_leaf_size)]\n",
    "\n",
    "for (criterion, leafsize) in zip(para_criterion, para_min_leaf_size):\n",
    "    rf4 = RandomForestClassifier(criterion=criterion, max_depth=n_depth, min_samples_leaf=leafsize, random_state=1234)\n",
    "    rf4.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(rf4.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(rf4.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "\n",
    "ds_accuracy4 = pd.DataFrame()\n",
    "ds_accuracy4[\"Criterion\"] = para_criterion\n",
    "ds_accuracy4[\"Depth\"] = n_depth\n",
    "ds_accuracy4[\"MinLeafSize\"] = para_min_leaf_size\n",
    "ds_accuracy4[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy4[\"TestAccuracy\"] = test_accuracy\n",
    "ds_accuracy4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10d362a630>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_accuracy4_melt = pd.melt(ds_accuracy4, id_vars =[\"Criterion\", \"Depth\", \"MinLeafSize\"])\n",
    "ds_accuracy4_melt[\"Accuracy\"] = ds_accuracy4_melt[\"Criterion\"] + \"_\" + ds_accuracy4_melt[\"variable\"]\n",
    "sns.lineplot(x=\"MinLeafSize\", y=\"value\", hue=\"Accuracy\", data=ds_accuracy4_melt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NanumBarunGothic']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "for f in fm.fontManager.ttflist:\n",
    "    if 'NanumBarunGothic' in f.fname: plt.rcParams['font.family']='NanumBarunGothic'\n",
    "    # 폰트 나눔고딕으로\n",
    "\n",
    "print(plt.rcParams['font.family'])\n",
    "\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False # '-'기호 제대로 나오게\n",
    "\n",
    "\n",
    "\n",
    "rf_model = RandomForestClassifier(criterion=\"gini\", max_depth=4, min_samples_leaf=10\n",
    "                                  , n_estimators = 100, random_state=1234)\n",
    "rf_model.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "\n",
    "ds_feature_importance = pd.DataFrame()\n",
    "ds_feature_importance[\"feature\"] = ds_hmeq_train_x.columns\n",
    "ds_feature_importance[\"importance\"] = rf_model.feature_importances_\n",
    "ds_feature_importance.sort_values(by=\"importance\", ascending = False)\n",
    "\n",
    "def plot_feature_importances(model):\n",
    "    n_features = ds_hmeq_train_x.shape[1]\n",
    "    plt.barh(range(n_features), model.feature_importances_, align=\"center\")\n",
    "    plt.yticks(np.arange(n_features), ds_hmeq_train_x.columns)\n",
    "    plt.xlabel(\"설명변수 중요도\")\n",
    "    plt.ylabel(\"설명변수\")\n",
    "    plt.ylim(-1, n_features)\n",
    "    \n",
    "plot_feature_importances(rf_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 그래디언트 부스팅"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.959\n",
      "Accuracy on test set: 0.944\n"
     ]
    }
   ],
   "source": [
    "gb_uncustomized = GradientBoostingClassifier(random_state=1234)\n",
    "gb_uncustomized.fit(ds_hmeq_train_x, ds_hmeq_train_y)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(gb_uncustomized.score(ds_hmeq_train_x, ds_hmeq_train_y)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(gb_uncustomized.score(ds_hmeq_test_x, ds_hmeq_test_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              n_iter_no_change=None, presort='auto', random_state=1234,\n",
       "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_uncustomized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.959\n",
      "Accuracy on test set: 0.943\n"
     ]
    }
   ],
   "source": [
    "# scale\n",
    "gb_uncustomized = GradientBoostingClassifier(random_state=1234)\n",
    "gb_uncustomized.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "\n",
    "print(\"Accuracy on training set: {:.3f}\".format(gb_uncustomized.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y)))\n",
    "print(\"Accuracy on test set: {:.3f}\".format(gb_uncustomized.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 스캐일링 전후 정확도는 유의미한 차이는 없는 것으로 보인다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "for lr in range(1, 100, 5):\n",
    "    gb1 = GradientBoostingClassifier(learning_rate=lr/100, random_state=1234)\n",
    "    gb1.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(gb1.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(gb1.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "    \n",
    "ds_accuracy1 = pd.DataFrame()\n",
    "ds_accuracy1[\"LearningRate\"] = [lr/100 for lr in range(1, 100, 5)]\n",
    "ds_accuracy1[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy1[\"TestAccuracy\"] = test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10d350ffd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_accuracy1.round(3)\n",
    "ds_accuracy1.set_index(\"LearningRate\", inplace= False).plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "para_estimators = [estimators for estimators in range(50, 150, 10)]\n",
    "lr = 0.2\n",
    "\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "for estimators in para_estimators:\n",
    "    gb2 = GradientBoostingClassifier(learning_rate=0.2,n_estimators=estimators, random_state=1234)\n",
    "    gb2.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(gb2.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(gb2.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "    \n",
    "ds_accuracy2 = pd.DataFrame()\n",
    "ds_accuracy2[\"Estimators\"] = para_estimators\n",
    "ds_accuracy2[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy2[\"TestAccuracy\"] = test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10d34da860>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_accuracy2.round(3)\n",
    "ds_accuracy2.set_index(\"Estimators\", inplace = False).plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.2 ; n_estimators = 100\n",
    "para_max_depth = [i+1 for i in range(5)]\n",
    "\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "for depth in para_max_depth:\n",
    "    gb3 = GradientBoostingClassifier(learning_rate=0.2,n_estimators=100,max_depth=depth, random_state=1234)\n",
    "    gb3.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(gb3.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(gb3.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "    \n",
    "ds_accuracy3 = pd.DataFrame()\n",
    "ds_accuracy3[\"MaxDepth\"] = para_max_depth\n",
    "ds_accuracy3[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy3[\"TestAccuracy\"] = test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10d34a2908>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_accuracy3.round(3)\n",
    "ds_accuracy3.set_index(\"MaxDepth\", inplace = False).plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.2 ; n_estimators = 100; max_depth = 3;\n",
    "para_min_leaf_size = [i+1 for i in range(10)]\n",
    "\n",
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "for leafsize in para_min_leaf_size:\n",
    "    gb4 = GradientBoostingClassifier(learning_rate=0.2,n_estimators=100,max_depth=3,min_samples_leaf=leafsize,\n",
    "                                     random_state=1234)\n",
    "    gb4.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "    train_accuracy.append(gb4.score(ds_hmeq_train_x_scaled, ds_hmeq_train_y))\n",
    "    test_accuracy.append(gb4.score(ds_hmeq_test_x_scaled, ds_hmeq_test_y))\n",
    "    \n",
    "ds_accuracy4 = pd.DataFrame()\n",
    "ds_accuracy4[\"MinLeafSize\"] = para_min_leaf_size\n",
    "ds_accuracy4[\"TrainAccuracy\"] = train_accuracy\n",
    "ds_accuracy4[\"TestAccuracy\"] = test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f10d347f518>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_accuracy4.round(3)\n",
    "ds_accuracy4.set_index(\"MinLeafSize\", inplace = False).plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb_model = GradientBoostingClassifier(learning_rate=0.1, max_depth=4, min_samples_leaf=30,\n",
    "                                     n_estimators= 5, random_state = 1234)\n",
    "gb_model.fit(ds_hmeq_train_x_scaled, ds_hmeq_train_y)\n",
    "\n",
    "ds_feature_importance = pd.DataFrame()\n",
    "ds_feature_importance[\"feature\"] = ds_hmeq_train_x.columns\n",
    "ds_feature_importance[\"importance\"] = gb_model.feature_importances_\n",
    "ds_feature_importance.sort_values(by=\"importance\", ascending = False)\n",
    "\n",
    "def plot_feature_importances(model):\n",
    "    n_features = ds_hmeq_train_x.shape[1]\n",
    "    plt.barh(range(n_features), model.feature_importances_, align=\"center\")\n",
    "    plt.yticks(np.arange(n_features), ds_hmeq_train_x.columns)\n",
    "    plt.xlabel(\"설명변수 중요도\")\n",
    "    plt.ylabel(\"설명변수\")\n",
    "    plt.ylim(-1, n_features)\n",
    "    \n",
    "plot_feature_importances(gb_model)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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