09.Gradient Boostring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `간단한 모델`들이 `상호 보완적인 역할`을 할 수 있도록 `단계적으로 학습을 수행하여` 결합함으로써 성능을 향상시키는 앙상블 학습 기법"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 순차적으로 학습하도록 하여, 먼저 학습된 결과가 다음 학습에 정보를 제공하여 이전의 학습의 결점을 보완하는 방향으로 학습이 반복적으로 이루어짐\n",
    "- 데이터에 과적합 될 수 있는 단점"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# data 시각화\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Korean pont\n",
    "matplotlib.rc('font',family = 'NanumGothicCoding')\n",
    "\n",
    "# train, test 데이터 분할 함수\n",
    "from sklearn.model_selection  import train_test_split\n",
    "# Grid Search\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "# from sklearn.tree import export_graphviz\n",
    "# import graphviz\n",
    "\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step01 - 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FAT</th>\n",
       "      <th>AGE</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>NECK</th>\n",
       "      <th>CHEST</th>\n",
       "      <th>ABDOMEN</th>\n",
       "      <th>HIP</th>\n",
       "      <th>THIGH</th>\n",
       "      <th>KNEE</th>\n",
       "      <th>ANKLE</th>\n",
       "      <th>BICEPS</th>\n",
       "      <th>FOREARM</th>\n",
       "      <th>WRIST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.2</td>\n",
       "      <td>46</td>\n",
       "      <td>363.15</td>\n",
       "      <td>72.25</td>\n",
       "      <td>51.2</td>\n",
       "      <td>136.2</td>\n",
       "      <td>148.1</td>\n",
       "      <td>147.7</td>\n",
       "      <td>87.3</td>\n",
       "      <td>49.1</td>\n",
       "      <td>29.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.8</td>\n",
       "      <td>27</td>\n",
       "      <td>168.00</td>\n",
       "      <td>71.25</td>\n",
       "      <td>38.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>79.1</td>\n",
       "      <td>94.5</td>\n",
       "      <td>57.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.2</td>\n",
       "      <td>69</td>\n",
       "      <td>177.75</td>\n",
       "      <td>68.50</td>\n",
       "      <td>38.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>21.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.6</td>\n",
       "      <td>57</td>\n",
       "      <td>147.75</td>\n",
       "      <td>65.75</td>\n",
       "      <td>35.2</td>\n",
       "      <td>99.6</td>\n",
       "      <td>86.4</td>\n",
       "      <td>90.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>31.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47.5</td>\n",
       "      <td>51</td>\n",
       "      <td>219.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>41.2</td>\n",
       "      <td>119.8</td>\n",
       "      <td>122.1</td>\n",
       "      <td>112.8</td>\n",
       "      <td>62.5</td>\n",
       "      <td>36.9</td>\n",
       "      <td>23.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FAT  AGE  WEIGHT  HEIGHT  NECK  CHEST  ABDOMEN    HIP  THIGH  KNEE  ANKLE  \\\n",
       "0  35.2   46  363.15   72.25  51.2  136.2    148.1  147.7   87.3  49.1   29.6   \n",
       "1  11.8   27  168.00   71.25  38.1   93.0     79.1   94.5   57.3  36.2   24.5   \n",
       "2  22.2   69  177.75   68.50  38.7  102.0     95.0   98.3   55.0  38.3   21.8   \n",
       "3  10.6   57  147.75   65.75  35.2   99.6     86.4   90.1   53.0  35.0   21.3   \n",
       "4  47.5   51  219.00   64.00  41.2  119.8    122.1  112.8   62.5  36.9   23.6   \n",
       "\n",
       "   BICEPS  FOREARM  WRIST  \n",
       "0    45.0     29.0   21.4  \n",
       "1    29.0     30.0   18.8  \n",
       "2    30.8     25.7   18.8  \n",
       "3    31.7     27.3   16.9  \n",
       "4    34.7     29.1   18.4  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv(\"./data/체질검사.csv\", encoding = '949')\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step01 - 목표변수(Y)제거 후 따로 저장, 설명변수와 목표변수 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>WEIGHT</th>\n",
       "      <th>HEIGHT</th>\n",
       "      <th>NECK</th>\n",
       "      <th>CHEST</th>\n",
       "      <th>ABDOMEN</th>\n",
       "      <th>HIP</th>\n",
       "      <th>THIGH</th>\n",
       "      <th>KNEE</th>\n",
       "      <th>ANKLE</th>\n",
       "      <th>BICEPS</th>\n",
       "      <th>FOREARM</th>\n",
       "      <th>WRIST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>363.15</td>\n",
       "      <td>72.25</td>\n",
       "      <td>51.2</td>\n",
       "      <td>136.2</td>\n",
       "      <td>148.1</td>\n",
       "      <td>147.7</td>\n",
       "      <td>87.3</td>\n",
       "      <td>49.1</td>\n",
       "      <td>29.6</td>\n",
       "      <td>45.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>168.00</td>\n",
       "      <td>71.25</td>\n",
       "      <td>38.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>79.1</td>\n",
       "      <td>94.5</td>\n",
       "      <td>57.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>177.75</td>\n",
       "      <td>68.50</td>\n",
       "      <td>38.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>98.3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>21.8</td>\n",
       "      <td>30.8</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "      <td>147.75</td>\n",
       "      <td>65.75</td>\n",
       "      <td>35.2</td>\n",
       "      <td>99.6</td>\n",
       "      <td>86.4</td>\n",
       "      <td>90.1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>31.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51</td>\n",
       "      <td>219.00</td>\n",
       "      <td>64.00</td>\n",
       "      <td>41.2</td>\n",
       "      <td>119.8</td>\n",
       "      <td>122.1</td>\n",
       "      <td>112.8</td>\n",
       "      <td>62.5</td>\n",
       "      <td>36.9</td>\n",
       "      <td>23.6</td>\n",
       "      <td>34.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  WEIGHT  HEIGHT  NECK  CHEST  ABDOMEN    HIP  THIGH  KNEE  ANKLE  \\\n",
       "0   46  363.15   72.25  51.2  136.2    148.1  147.7   87.3  49.1   29.6   \n",
       "1   27  168.00   71.25  38.1   93.0     79.1   94.5   57.3  36.2   24.5   \n",
       "2   69  177.75   68.50  38.7  102.0     95.0   98.3   55.0  38.3   21.8   \n",
       "3   57  147.75   65.75  35.2   99.6     86.4   90.1   53.0  35.0   21.3   \n",
       "4   51  219.00   64.00  41.2  119.8    122.1  112.8   62.5  36.9   23.6   \n",
       "\n",
       "   BICEPS  FOREARM  WRIST  \n",
       "0    45.0     29.0   21.4  \n",
       "1    29.0     30.0   18.8  \n",
       "2    30.8     25.7   18.8  \n",
       "3    31.7     27.3   16.9  \n",
       "4    34.7     29.1   18.4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw_y = df_raw['FAT']\n",
    "df_raw_x = df_raw.drop(\"FAT\", axis = 1, inplace = False)\n",
    "df_raw_x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step01 - 데이터 분할"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data X size : (176, 13)\n",
      "train data Y size : (176,)\n",
      "test data X size : (76, 13)\n",
      "test data Y size : (76,)\n"
     ]
    }
   ],
   "source": [
    "df_train_x, df_test_x, df_train_y, df_test_y = train_test_split(df_raw_x, df_raw_y\n",
    "                                                               , test_size = 0.3, random_state = 1234)\n",
    "print('train data X size : {}'.format(df_train_x.shape))\n",
    "print('train data Y size : {}'.format(df_train_y.shape))\n",
    "print('test data X size : {}'.format(df_test_x.shape))\n",
    "print('test data Y size : {}'.format(df_test_y.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 모델 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on trainning set : 0.981\n",
      "Score on test set : 0.584\n"
     ]
    }
   ],
   "source": [
    "gb_uncustomized = GradientBoostingRegressor(random_state = 1234)\n",
    "gb_uncustomized.fit(df_train_x, df_train_y)\n",
    "\n",
    "print('Score on trainning set : {:.3f}'.format(gb_uncustomized.score(df_train_x, df_train_y)))\n",
    "print('Score on test set : {:.3f}'.format(gb_uncustomized.score(df_test_x, df_test_y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> test data의 설명력이 매우 낮음 : `과대적합`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 트리 수 변경에 다른 모델 성능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_n_tree = [n_tree * 5 for n_tree in range(1,51)]\n",
    "\n",
    "for v_n_estimators in para_n_tree:\n",
    "    gb = GradientBoostingRegressor(n_estimators = v_n_estimators, random_state = 1234)\n",
    "    \n",
    "    gb.fit(df_train_x, df_train_y)\n",
    "    train_score.append(gb.score(df_train_x, df_train_y))\n",
    "    test_score.append(gb.score(df_test_x, df_test_y))\n",
    "    \n",
    "#저장\n",
    "df_score_n = pd.DataFrame()\n",
    "df_score_n['Estimators'] = para_n_tree\n",
    "df_score_n['TrainScore'] = train_score\n",
    "df_score_n['TestScore'] = test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Estimators</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Estimators  TrainScore  TestScore\n",
       "0           5       0.506      0.365\n",
       "1          10       0.712      0.512\n",
       "2          15       0.804      0.578\n",
       "3          20       0.850      0.589\n",
       "4          25       0.878      0.597\n",
       "5          30       0.897      0.600\n",
       "6          35       0.911      0.599\n",
       "7          40       0.921      0.599\n",
       "8          45       0.932      0.591\n",
       "9          50       0.942      0.587"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_n.round(3).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc6de9de048>"
      ]
     },
     "execution_count": 8,
     "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": [
    "plt.plot(para_n_tree, train_score, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_n_tree, test_score, linestyle = '-', label = 'Test Score')\n",
    "plt.ylabel('score');plt.xlabel('n_estimators')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> estimators가 30개가 적당하다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - Leaf Node 자료 수 지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_leaf = [n_tree * 1 for n_tree in range(1,21)]\n",
    "\n",
    "for v_min_samples_leaf in para_leaf:\n",
    "    rf = GradientBoostingRegressor(random_state = 1234, n_estimators = 30,\n",
    "                              min_samples_leaf = v_min_samples_leaf)\n",
    "    \n",
    "    rf.fit(df_train_x, df_train_y)\n",
    "    train_score.append(rf.score(df_train_x, df_train_y))\n",
    "    test_score.append(rf.score(df_test_x, df_test_y))\n",
    "    \n",
    "#저장\n",
    "df_score_leaf = pd.DataFrame()\n",
    "df_score_leaf['MinSamplesLeaf'] = para_leaf\n",
    "df_score_leaf['TrainScore'] = train_score\n",
    "df_score_leaf['TestScore'] = test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>MinSamplesLeaf</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.899</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.893</td>\n",
       "      <td>0.586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MinSamplesLeaf  TrainScore  TestScore\n",
       "0                1       0.897      0.600\n",
       "1                2       0.899      0.596\n",
       "2                3       0.896      0.595\n",
       "3                4       0.893      0.586\n",
       "4                5       0.890      0.604\n",
       "5                6       0.888      0.596\n",
       "6                7       0.884      0.590\n",
       "7                8       0.874      0.610\n",
       "8                9       0.879      0.598\n",
       "9               10       0.873      0.587\n",
       "10              11       0.870      0.620\n",
       "11              12       0.865      0.597\n",
       "12              13       0.862      0.606\n",
       "13              14       0.852      0.600\n",
       "14              15       0.847      0.605\n",
       "15              16       0.845      0.600\n",
       "16              17       0.841      0.589\n",
       "17              18       0.838      0.588\n",
       "18              19       0.840      0.589\n",
       "19              20       0.831      0.595"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score_leaf.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc6de912860>"
      ]
     },
     "execution_count": 11,
     "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": [
    "plt.plot(para_leaf, train_score, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_leaf, test_score, linestyle = '--', label = 'Test Score')\n",
    "plt.ylabel('score');plt.xlabel('min samples leaf')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> min_sampels_leaf 가 11개가 적당하다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 분리노드(Splite)의 최소 자료 수 변경에 따른 모델 성능"
   ]
  },
  {
   "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",
       "    }\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>MinSampleSplit</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>28</td>\n",
       "      <td>0.863</td>\n",
       "      <td>0.617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>32</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>34</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>36</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MinSampleSplit  TrainScore  TestScore\n",
       "0                2       0.870      0.620\n",
       "1                4       0.870      0.620\n",
       "2                6       0.870      0.620\n",
       "3                8       0.870      0.620\n",
       "4               10       0.870      0.620\n",
       "5               12       0.870      0.620\n",
       "6               14       0.870      0.620\n",
       "7               16       0.870      0.620\n",
       "8               18       0.870      0.620\n",
       "9               20       0.870      0.620\n",
       "10              22       0.870      0.620\n",
       "11              24       0.868      0.614\n",
       "12              26       0.868      0.617\n",
       "13              28       0.863      0.617\n",
       "14              30       0.860      0.617\n",
       "15              32       0.862      0.611\n",
       "16              34       0.855      0.616\n",
       "17              36       0.853      0.608\n",
       "18              38       0.854      0.610\n",
       "19              40       0.854      0.617"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_split = [n_tree * 2 for n_tree in range(1,21)]\n",
    "\n",
    "for v_min_samples_split in para_split:\n",
    "    rf = GradientBoostingRegressor(random_state = 1234, n_estimators = 30, min_samples_leaf = 11,\n",
    "                              min_samples_split = v_min_samples_split)\n",
    "    \n",
    "    rf.fit(df_train_x, df_train_y)\n",
    "    train_score.append(rf.score(df_train_x, df_train_y))\n",
    "    test_score.append(rf.score(df_test_x, df_test_y))\n",
    "    \n",
    "#저장\n",
    "df_score_split = pd.DataFrame()\n",
    "df_score_split['MinSampleSplit'] = para_split\n",
    "df_score_split['TrainScore'] = train_score\n",
    "df_score_split['TestScore'] = test_score\n",
    "df_score_split.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc6de883e48>"
      ]
     },
     "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": [
    "plt.plot(para_split, train_score, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_split, test_score, linestyle = '--', label = 'Test Score')\n",
    "plt.ylabel('score');plt.xlabel('min samples leaf')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> min_split 는 22개가 적당하다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 최대깊이 변경에 따른 모델 성능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Depth</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.920</td>\n",
       "      <td>0.615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Depth  TrainScore  TestScore\n",
       "0      1       0.703      0.592\n",
       "1      2       0.800      0.600\n",
       "2      3       0.870      0.620\n",
       "3      4       0.900      0.629\n",
       "4      5       0.913      0.613\n",
       "5      6       0.917      0.614\n",
       "6      7       0.920      0.613\n",
       "7      8       0.920      0.615\n",
       "8      9       0.921      0.609\n",
       "9     10       0.922      0.616"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_depth = [depth for depth in range(1,11)]\n",
    "\n",
    "for v_max_depth in para_depth:\n",
    "    gb = GradientBoostingRegressor(random_state = 1234, n_estimators = 30,\n",
    "                              min_samples_leaf = 11, min_samples_split = 22,\n",
    "                              max_depth = v_max_depth)\n",
    "    \n",
    "    gb.fit(df_train_x, df_train_y)\n",
    "    train_score.append(gb.score(df_train_x, df_train_y))\n",
    "    test_score.append(gb.score(df_test_x, df_test_y))\n",
    "    \n",
    "df_score_depth = pd.DataFrame()\n",
    "df_score_depth['Depth'] = para_depth\n",
    "df_score_depth['TrainScore'] = train_score\n",
    "df_score_depth['TestScore'] = test_score\n",
    "df_score_depth.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc6de8ad1d0>"
      ]
     },
     "execution_count": 15,
     "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": [
    "plt.plot(para_depth, train_score, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_depth, test_score, linestyle = '--', label = 'Test Score')\n",
    "plt.ylabel('score');plt.xlabel('min samples depth')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> depth 4개가 적절하다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step02 - 학습률 변경 시 모델의 설명력 변화"
   ]
  },
  {
   "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>LearningRate</th>\n",
       "      <th>TrainScore</th>\n",
       "      <th>TestScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.993</td>\n",
       "      <td>0.478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.997</td>\n",
       "      <td>0.464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LearningRate  TrainScore  TestScore\n",
       "0           0.1       0.900      0.629\n",
       "1           0.2       0.940      0.588\n",
       "2           0.3       0.966      0.566\n",
       "3           0.4       0.981      0.539\n",
       "4           0.5       0.984      0.533\n",
       "5           0.6       0.990      0.527\n",
       "6           0.7       0.993      0.478\n",
       "7           0.8       0.994      0.437\n",
       "8           0.9       0.997      0.464"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score = [];test_score=[]\n",
    "para_lr = [lr * 0.1 for lr in range(1,10)]\n",
    "\n",
    "for v_learning_rate in para_lr:\n",
    "    gb = GradientBoostingRegressor(random_state = 1234, n_estimators = 30,\n",
    "                              min_samples_leaf = 11, min_samples_split = 22,\n",
    "                              max_depth = 4, learning_rate = v_learning_rate)\n",
    "    \n",
    "    gb.fit(df_train_x, df_train_y)\n",
    "    train_score.append(gb.score(df_train_x, df_train_y))\n",
    "    test_score.append(gb.score(df_test_x, df_test_y))\n",
    "    \n",
    "df_score_depth = pd.DataFrame()\n",
    "df_score_depth['LearningRate'] = para_lr\n",
    "df_score_depth['TrainScore'] = train_score\n",
    "df_score_depth['TestScore'] = test_score\n",
    "df_score_depth.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc6de7dd470>"
      ]
     },
     "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": [
    "plt.plot(para_lr, train_score, linestyle = '-', label = 'Train Score')\n",
    "plt.plot(para_lr, test_score, linestyle = '--', label = 'Test Score')\n",
    "plt.ylabel('score');plt.xlabel('learning rate')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Learning Rate은 0.1이 적절합니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step03 - 결론도출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on training set : 0.900\n",
      "Score on test set : 0.629\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=4, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=11,\n",
       "             min_samples_split=22, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=30, n_iter_no_change=None, presort='auto',\n",
       "             random_state=1234, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_final = GradientBoostingRegressor(random_state = 1234, n_estimators = 30,\n",
    "                              min_samples_leaf = 11, min_samples_split = 22,\n",
    "                              max_depth = 4, learning_rate = 0.1)\n",
    "gb_final.fit(df_train_x, df_train_y)\n",
    "print('Score on training set : {:.3f}'.format(gb_final.score(df_train_x, df_train_y)))\n",
    "print('Score on test set : {:.3f}'.format(gb_final.score(df_test_x, df_test_y)))\n",
    "\n",
    "gb_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step03 - 설명변수의 중요도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ABDOMEN</td>\n",
       "      <td>0.775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HEIGHT</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WRIST</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NECK</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HIP</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEST</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGE</td>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>THIGH</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KNEE</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BICEPS</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WEIGHT</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ANKLE</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FOREARM</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature  Importance\n",
       "5   ABDOMEN       0.775\n",
       "2    HEIGHT       0.038\n",
       "12    WRIST       0.025\n",
       "3      NECK       0.023\n",
       "6       HIP       0.021\n",
       "4     CHEST       0.021\n",
       "0       AGE       0.021\n",
       "7     THIGH       0.020\n",
       "8      KNEE       0.017\n",
       "10   BICEPS       0.014\n",
       "1    WEIGHT       0.012\n",
       "9     ANKLE       0.007\n",
       "11  FOREARM       0.005"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_feature_names = df_train_x.columns\n",
    "df_importance = pd.DataFrame()\n",
    "df_importance['Feature'] = v_feature_names\n",
    "df_importance['Importance'] = gb_final.feature_importances_\n",
    "\n",
    "df_importance.sort_values('Importance', ascending = False, inplace = True)\n",
    "df_importance.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '변수')"
      ]
     },
     "execution_count": 20,
     "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": [
    "df_importance.sort_values('Importance', ascending = True, inplace = True)\n",
    "coordinates = range(len(df_importance))\n",
    "plt.barh(y = coordinates, width = df_importance['Importance'])\n",
    "plt.yticks(coordinates, df_importance['Feature'])\n",
    "plt.xlabel('변수중요도')\n",
    "plt.ylabel('변수')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 복부둘레가 가장 큰 유의한 변수로 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best estimator model: \n",
      " GradientBoostingRegressor(alpha=0.9, criterion='mae', init=None,\n",
      "             learning_rate=0.2, loss='ls', max_depth=1,\n",
      "             max_features='auto', max_leaf_nodes=None,\n",
      "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "             min_samples_leaf=6, 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, verbose=0,\n",
      "             warm_start=False)\n",
      "\n",
      " nbest parameter : \n",
      " {'criterion': 'mae', 'learning_rate': 0.2, 'max_depth': 1, 'max_features': 'auto', 'min_samples_leaf': 6, 'min_samples_split': 2}\n",
      "\n",
      " best score: \n",
      " 0.708\n"
     ]
    }
   ],
   "source": [
    "estimator = GradientBoostingRegressor(random_state = 1234)\n",
    "\n",
    "#구하고자 하는 parameter와 범위\n",
    "param_grid = {'criterion':['mse','friedman_mse', 'mae'],'max_features':['auto','sqrt','log2'],'learning_rate':para_lr,'max_depth':para_depth, 'min_samples_split':para_split, 'min_samples_leaf':para_leaf}\n",
    "\n",
    "#설명력이 높은 최적 parameter 찾기\n",
    "grid_gb = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs=-1)\n",
    "grid_gb.fit(df_train_x, df_train_y)\n",
    "\n",
    "print('best estimator model: \\n {}'.format(grid_gb.best_estimator_))\n",
    "print('\\n nbest parameter : \\n {}'.format(grid_gb.best_params_))\n",
    "print('\\n best score: \\n {}'.format(grid_gb.best_score_.round(3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = GradientBoostingRegressor(random_state = 1234)\n",
    "\n",
    "#구하고자 하는 parameter와 범위\n",
    "param_grid = {'criterion':['mse','friedman_mse'],'max_features':['auto','sqrt','log2'],'learning_rate':para_lr,'max_depth':para_depth, 'min_samples_split':para_split, 'min_samples_leaf':para_leaf}\n",
    "\n",
    "#설명력이 높은 최적 parameter 찾기\n",
    "grid_gb = GridSearchCV(estimator, param_grid, scoring = 'r2', n_jobs=-1)\n",
    "grid_gb.fit(df_train_x, df_train_y)\n",
    "\n",
    "print('best estimator model: \\n {}'.format(grid_gb.best_estimator_))\n",
    "print('\\n nbest parameter : \\n {}'.format(grid_gb.best_params_))\n",
    "print('\\n best score: \\n {}'.format(grid_gb.best_score_.round(3)))"
   ]
  }
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