16. RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 라이브러리 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = pd.read_csv('./input/wine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>fixed.acidity</th>\n",
       "      <th>volatile.acidity</th>\n",
       "      <th>citric.acid</th>\n",
       "      <th>residual.sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free.sulfur.dioxide</th>\n",
       "      <th>total.sulfur.dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed.acidity  volatile.acidity  citric.acid  residual.sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free.sulfur.dioxide  total.sulfur.dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## quality가 3 ~ 6이면 'Good'  / 7 ~ 9이면 'Best' 할당"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list comprehension\n",
    "wine['grade'] = ['Good' if x<7 else 'Best' for x in wine['quality']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 분할"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed.acidity', 'volatile.acidity', 'citric.acid', 'residual.sugar',\n",
       "       'chlorides', 'free.sulfur.dioxide', 'total.sulfur.dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality', 'grade'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fixed.acidity',\n",
       " 'volatile.acidity',\n",
       " 'citric.acid',\n",
       " 'residual.sugar',\n",
       " 'chlorides',\n",
       " 'free.sulfur.dioxide',\n",
       " 'total.sulfur.dioxide',\n",
       " 'density',\n",
       " 'pH',\n",
       " 'sulphates',\n",
       " 'alcohol']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = wine.columns.tolist()\n",
    "col.remove('quality')\n",
    "col.remove('grade') # 종속변수 제외\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = wine[col]\n",
    "y_data = wine['grade'] # target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Good\n",
       "1       Good\n",
       "2       Good\n",
       "3       Good\n",
       "4       Good\n",
       "        ... \n",
       "4893    Good\n",
       "4894    Good\n",
       "4895    Good\n",
       "4896    Best\n",
       "4897    Good\n",
       "Name: grade, Length: 4898, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train, test split\n",
    "# 굳이 값을 표준화해줄 필요는 없음 : 한 특성에만 반드시 영향받는 것이 아님\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3428, 11)\n",
      "(1470, 11)\n",
      "(3428,)\n",
      "(1470,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n",
    "# reshape 함수 써서 1열(2차원)로 바꿔줌\n",
    "# type(y_train) ... series : np.array() 거쳐서"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest 모델 생성\n",
    "### 참고 : https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=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_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# n_estimators : 나무의 수\n",
    "# 그 외에 나머지 요인들은 의사결정 나무와 동일\n",
    "# default : 100\n",
    "# criterion : gini, entropy\n",
    "# mean_samples_leaf, grid_search, max_feature (auto, sqrt, log2)\n",
    "# 각 모델을 만들 때마다 max_feature가 각각 들어가게 됨\n",
    "# ccp_alpha : 기존 오류에 +a, 마지막 노드의 개수 -> 너무 많아지면 비용 복잡도 train에 과적합, 오류값이 높아짐 \n",
    "# 0-1 사이 값만 -> 음수가 들어가면 오히려 반대의 결과가 나옴\n",
    "\n",
    "model = RandomForestClassifier()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=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_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_jobs=-1) # 분산하여 속도를 빠르게 해줌, 비어있는 코어를 모두 활용하도록\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed.acidity', 'volatile.acidity', 'citric.acid', 'residual.sugar',\n",
       "       'chlorides', 'free.sulfur.dioxide', 'total.sulfur.dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06689515, 0.0848353 , 0.06848756, 0.08338603, 0.08560192,\n",
       "       0.08310574, 0.08230425, 0.11620287, 0.09167989, 0.07333515,\n",
       "       0.16416614])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature_importances_ : 변수 중요도 ... 발표에 용이\n",
    "model.feature_importances_ # 가장 높은 수가 모델을 만드는데 가장 좋은 항목, 너무 안좋으면 빼버려야 함\n",
    "# PCA 등 : 1200개 가량의 columns... 차원 축소"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(model.feature_importances_, x_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed.acidity</th>\n",
       "      <td>0.066895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile.acidity</th>\n",
       "      <td>0.084835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric.acid</th>\n",
       "      <td>0.068488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual.sugar</th>\n",
       "      <td>0.083386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorides</th>\n",
       "      <td>0.085602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0\n",
       "fixed.acidity     0.066895\n",
       "volatile.acidity  0.084835\n",
       "citric.acid       0.068488\n",
       "residual.sugar    0.083386\n",
       "chlorides         0.085602"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {'column' : x_data.columns.tolist(), 'feature_importance' : model.feature_importances_}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>column</th>\n",
       "      <th>feature_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>alcohol</td>\n",
       "      <td>0.164166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>density</td>\n",
       "      <td>0.116203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pH</td>\n",
       "      <td>0.091680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chlorides</td>\n",
       "      <td>0.085602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>volatile.acidity</td>\n",
       "      <td>0.084835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>residual.sugar</td>\n",
       "      <td>0.083386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>free.sulfur.dioxide</td>\n",
       "      <td>0.083106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>total.sulfur.dioxide</td>\n",
       "      <td>0.082304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sulphates</td>\n",
       "      <td>0.073335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>citric.acid</td>\n",
       "      <td>0.068488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fixed.acidity</td>\n",
       "      <td>0.066895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  column  feature_importance\n",
       "10               alcohol            0.164166\n",
       "7                density            0.116203\n",
       "8                     pH            0.091680\n",
       "4              chlorides            0.085602\n",
       "1       volatile.acidity            0.084835\n",
       "3         residual.sugar            0.083386\n",
       "5    free.sulfur.dioxide            0.083106\n",
       "6   total.sulfur.dioxide            0.082304\n",
       "9              sulphates            0.073335\n",
       "2            citric.acid            0.068488\n",
       "0          fixed.acidity            0.066895"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정렬\n",
    "df.sort_values(by='feature_importance', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# column이 많을 때는 heatmap 사용하는 것이 좋음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=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_jobs=-1, oob_score=False, random_state=None, verbose=1,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(verbose=1, n_jobs=-1)\n",
    "model.fit(X_train, y_train)\n",
    "# n_jobs = -1이면 시간이 훨씬 더 짧음, verbose : default = 0, 1인 경우 진행 과정 표시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 변수 중요도 데이터 프레임 만들기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=8)]: Done 100 out of 100 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "# 모델 예측\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 195,   41],\n",
       "       [ 139, 1095]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "accuracy_score(y_pred, y_test)\n",
    "confusion_matrix(y_pred, y_test)\n",
    "# class가 imbalance ... 정확도가 87이라고 해도 좋은 모델이 아님 - 특이도\n",
    "# train으로만 과적합이 된 상태 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 적는 순서에 따라\n",
    "confusion_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## grid search + cross_validation\n",
    "#### parameter를 격자무늬 -> 학습을 하나하나 시키기 때문에 개수가 많을 수록 연산이 많아져 시간이 오래 걸림"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'n_estimators' : [100, 200, 300],\n",
    "             'max_features' : ['auto', 'sqrt', 'log2'],\n",
    "             'ccp_alpha' : [0.1, 0.01, 0.001]}\n",
    "grid_search = GridSearchCV(RandomForestClassifier(n_jobs=-1),\n",
    "                           param_grid,\n",
    "                           cv=5,\n",
    "                           return_train_score=True)\n",
    "# 데이터 나누는 수만큼 iteration이 돌아감\n",
    "# return_train_score : accuracy 출력\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 9s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=-1,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'ccp_alpha': [0.1, 0.01, 0.001],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "# 시간이 얼마나 지났는가\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.001, 'max_features': 'auto', 'n_estimators': 200}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best_params_ : best parameter\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8526794492562406"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best_score_ : best parameter로 돌린 cv 정확도 결과\n",
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.001, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=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=200,\n",
       "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best_estimator_ : 최고 성능을 낸 파라미터를 가진 모형\n",
    "grid_search.best_estimator_\n",
    "# 판단을 해서 다 들어감"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_ccp_alpha</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.901036</td>\n",
       "      <td>1.113717</td>\n",
       "      <td>0.116388</td>\n",
       "      <td>0.005789</td>\n",
       "      <td>0.1</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'auto', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.580362</td>\n",
       "      <td>0.007729</td>\n",
       "      <td>0.114334</td>\n",
       "      <td>0.002249</td>\n",
       "      <td>0.1</td>\n",
       "      <td>auto</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'auto', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.884580</td>\n",
       "      <td>0.004851</td>\n",
       "      <td>0.176560</td>\n",
       "      <td>0.048798</td>\n",
       "      <td>0.1</td>\n",
       "      <td>auto</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'auto', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.341129</td>\n",
       "      <td>0.003835</td>\n",
       "      <td>0.112382</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.1</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'sqrt', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.584462</td>\n",
       "      <td>0.006417</td>\n",
       "      <td>0.114246</td>\n",
       "      <td>0.003053</td>\n",
       "      <td>0.1</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'sqrt', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.887678</td>\n",
       "      <td>0.007157</td>\n",
       "      <td>0.197337</td>\n",
       "      <td>0.042213</td>\n",
       "      <td>0.1</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'sqrt', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.336291</td>\n",
       "      <td>0.007127</td>\n",
       "      <td>0.112617</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>0.1</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'log2', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.582888</td>\n",
       "      <td>0.003207</td>\n",
       "      <td>0.113713</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.1</td>\n",
       "      <td>log2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'log2', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.888820</td>\n",
       "      <td>0.018758</td>\n",
       "      <td>0.198675</td>\n",
       "      <td>0.043829</td>\n",
       "      <td>0.1</td>\n",
       "      <td>log2</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'max_features': 'log2', 'n_...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.344614</td>\n",
       "      <td>0.007594</td>\n",
       "      <td>0.112284</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>0.01</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'auto', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.589174</td>\n",
       "      <td>0.015465</td>\n",
       "      <td>0.113236</td>\n",
       "      <td>0.003432</td>\n",
       "      <td>0.01</td>\n",
       "      <td>auto</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'auto', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.896353</td>\n",
       "      <td>0.009071</td>\n",
       "      <td>0.179877</td>\n",
       "      <td>0.052519</td>\n",
       "      <td>0.01</td>\n",
       "      <td>auto</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'auto', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.344901</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.111573</td>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.01</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'sqrt', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.598231</td>\n",
       "      <td>0.030708</td>\n",
       "      <td>0.109612</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.01</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'sqrt', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.889151</td>\n",
       "      <td>0.007234</td>\n",
       "      <td>0.197831</td>\n",
       "      <td>0.045634</td>\n",
       "      <td>0.01</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'sqrt', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.338693</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>0.112217</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.01</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'log2', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.630224</td>\n",
       "      <td>0.077071</td>\n",
       "      <td>0.112837</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.01</td>\n",
       "      <td>log2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'log2', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.890722</td>\n",
       "      <td>0.008243</td>\n",
       "      <td>0.174236</td>\n",
       "      <td>0.050455</td>\n",
       "      <td>0.01</td>\n",
       "      <td>log2</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.01, 'max_features': 'log2', 'n...</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>0.788630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>10</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788111</td>\n",
       "      <td>0.788476</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788188</td>\n",
       "      <td>0.788215</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.353461</td>\n",
       "      <td>0.004568</td>\n",
       "      <td>0.114270</td>\n",
       "      <td>0.002581</td>\n",
       "      <td>0.001</td>\n",
       "      <td>auto</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'auto', '...</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.854227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.845096</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>9</td>\n",
       "      <td>0.943472</td>\n",
       "      <td>0.938731</td>\n",
       "      <td>0.943107</td>\n",
       "      <td>0.939118</td>\n",
       "      <td>0.941305</td>\n",
       "      <td>0.941147</td>\n",
       "      <td>0.001961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.588970</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.113931</td>\n",
       "      <td>0.002238</td>\n",
       "      <td>0.001</td>\n",
       "      <td>auto</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'auto', '...</td>\n",
       "      <td>0.870262</td>\n",
       "      <td>0.862974</td>\n",
       "      <td>...</td>\n",
       "      <td>0.852679</td>\n",
       "      <td>0.012112</td>\n",
       "      <td>1</td>\n",
       "      <td>0.944931</td>\n",
       "      <td>0.940554</td>\n",
       "      <td>0.944201</td>\n",
       "      <td>0.937295</td>\n",
       "      <td>0.942763</td>\n",
       "      <td>0.941949</td>\n",
       "      <td>0.002765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.085470</td>\n",
       "      <td>0.178417</td>\n",
       "      <td>0.052374</td>\n",
       "      <td>0.001</td>\n",
       "      <td>auto</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'auto', '...</td>\n",
       "      <td>0.868805</td>\n",
       "      <td>0.858601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.849470</td>\n",
       "      <td>0.012951</td>\n",
       "      <td>5</td>\n",
       "      <td>0.944566</td>\n",
       "      <td>0.939460</td>\n",
       "      <td>0.942378</td>\n",
       "      <td>0.942034</td>\n",
       "      <td>0.942034</td>\n",
       "      <td>0.942095</td>\n",
       "      <td>0.001621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.359059</td>\n",
       "      <td>0.014288</td>\n",
       "      <td>0.114728</td>\n",
       "      <td>0.002816</td>\n",
       "      <td>0.001</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'sqrt', '...</td>\n",
       "      <td>0.864431</td>\n",
       "      <td>0.852770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846262</td>\n",
       "      <td>0.010990</td>\n",
       "      <td>8</td>\n",
       "      <td>0.942013</td>\n",
       "      <td>0.939096</td>\n",
       "      <td>0.943472</td>\n",
       "      <td>0.938024</td>\n",
       "      <td>0.941670</td>\n",
       "      <td>0.940855</td>\n",
       "      <td>0.001998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.593226</td>\n",
       "      <td>0.008153</td>\n",
       "      <td>0.113768</td>\n",
       "      <td>0.003229</td>\n",
       "      <td>0.001</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'sqrt', '...</td>\n",
       "      <td>0.861516</td>\n",
       "      <td>0.852770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847721</td>\n",
       "      <td>0.008250</td>\n",
       "      <td>7</td>\n",
       "      <td>0.942378</td>\n",
       "      <td>0.938366</td>\n",
       "      <td>0.942378</td>\n",
       "      <td>0.939118</td>\n",
       "      <td>0.942034</td>\n",
       "      <td>0.940855</td>\n",
       "      <td>0.001746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.902511</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.197063</td>\n",
       "      <td>0.041359</td>\n",
       "      <td>0.001</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'sqrt', '...</td>\n",
       "      <td>0.865889</td>\n",
       "      <td>0.858601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.850054</td>\n",
       "      <td>0.010303</td>\n",
       "      <td>4</td>\n",
       "      <td>0.946025</td>\n",
       "      <td>0.940554</td>\n",
       "      <td>0.944931</td>\n",
       "      <td>0.940211</td>\n",
       "      <td>0.943857</td>\n",
       "      <td>0.943116</td>\n",
       "      <td>0.002337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.347060</td>\n",
       "      <td>0.006354</td>\n",
       "      <td>0.112068</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.001</td>\n",
       "      <td>log2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'log2', '...</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.854227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847723</td>\n",
       "      <td>0.007497</td>\n",
       "      <td>6</td>\n",
       "      <td>0.940554</td>\n",
       "      <td>0.940919</td>\n",
       "      <td>0.943837</td>\n",
       "      <td>0.938389</td>\n",
       "      <td>0.944951</td>\n",
       "      <td>0.941730</td>\n",
       "      <td>0.002367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.588220</td>\n",
       "      <td>0.004780</td>\n",
       "      <td>0.115466</td>\n",
       "      <td>0.004212</td>\n",
       "      <td>0.001</td>\n",
       "      <td>log2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'log2', '...</td>\n",
       "      <td>0.870262</td>\n",
       "      <td>0.860058</td>\n",
       "      <td>...</td>\n",
       "      <td>0.851804</td>\n",
       "      <td>0.011594</td>\n",
       "      <td>3</td>\n",
       "      <td>0.946389</td>\n",
       "      <td>0.940919</td>\n",
       "      <td>0.943837</td>\n",
       "      <td>0.938024</td>\n",
       "      <td>0.944586</td>\n",
       "      <td>0.942751</td>\n",
       "      <td>0.002949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.899419</td>\n",
       "      <td>0.022223</td>\n",
       "      <td>0.177501</td>\n",
       "      <td>0.049089</td>\n",
       "      <td>0.001</td>\n",
       "      <td>log2</td>\n",
       "      <td>300</td>\n",
       "      <td>{'ccp_alpha': 0.001, 'max_features': 'log2', '...</td>\n",
       "      <td>0.864431</td>\n",
       "      <td>0.861516</td>\n",
       "      <td>...</td>\n",
       "      <td>0.852096</td>\n",
       "      <td>0.010062</td>\n",
       "      <td>2</td>\n",
       "      <td>0.943837</td>\n",
       "      <td>0.942378</td>\n",
       "      <td>0.945660</td>\n",
       "      <td>0.941305</td>\n",
       "      <td>0.940941</td>\n",
       "      <td>0.942824</td>\n",
       "      <td>0.001739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.901036      1.113717         0.116388        0.005789   \n",
       "1        0.580362      0.007729         0.114334        0.002249   \n",
       "2        0.884580      0.004851         0.176560        0.048798   \n",
       "3        0.341129      0.003835         0.112382        0.000173   \n",
       "4        0.584462      0.006417         0.114246        0.003053   \n",
       "5        0.887678      0.007157         0.197337        0.042213   \n",
       "6        0.336291      0.007127         0.112617        0.000741   \n",
       "7        0.582888      0.003207         0.113713        0.002240   \n",
       "8        0.888820      0.018758         0.198675        0.043829   \n",
       "9        0.344614      0.007594         0.112284        0.002919   \n",
       "10       0.589174      0.015465         0.113236        0.003432   \n",
       "11       0.896353      0.009071         0.179877        0.052519   \n",
       "12       0.344901      0.007143         0.111573        0.001773   \n",
       "13       0.598231      0.030708         0.109612        0.002232   \n",
       "14       0.889151      0.007234         0.197831        0.045634   \n",
       "15       0.338693      0.007112         0.112217        0.000195   \n",
       "16       0.630224      0.077071         0.112837        0.002186   \n",
       "17       0.890722      0.008243         0.174236        0.050455   \n",
       "18       0.353461      0.004568         0.114270        0.002581   \n",
       "19       0.588970      0.005067         0.113931        0.002238   \n",
       "20       0.848485      0.085470         0.178417        0.052374   \n",
       "21       0.359059      0.014288         0.114728        0.002816   \n",
       "22       0.593226      0.008153         0.113768        0.003229   \n",
       "23       0.902511      0.004611         0.197063        0.041359   \n",
       "24       0.347060      0.006354         0.112068        0.000043   \n",
       "25       0.588220      0.004780         0.115466        0.004212   \n",
       "26       0.899419      0.022223         0.177501        0.049089   \n",
       "\n",
       "   param_ccp_alpha param_max_features param_n_estimators  \\\n",
       "0              0.1               auto                100   \n",
       "1              0.1               auto                200   \n",
       "2              0.1               auto                300   \n",
       "3              0.1               sqrt                100   \n",
       "4              0.1               sqrt                200   \n",
       "5              0.1               sqrt                300   \n",
       "6              0.1               log2                100   \n",
       "7              0.1               log2                200   \n",
       "8              0.1               log2                300   \n",
       "9             0.01               auto                100   \n",
       "10            0.01               auto                200   \n",
       "11            0.01               auto                300   \n",
       "12            0.01               sqrt                100   \n",
       "13            0.01               sqrt                200   \n",
       "14            0.01               sqrt                300   \n",
       "15            0.01               log2                100   \n",
       "16            0.01               log2                200   \n",
       "17            0.01               log2                300   \n",
       "18           0.001               auto                100   \n",
       "19           0.001               auto                200   \n",
       "20           0.001               auto                300   \n",
       "21           0.001               sqrt                100   \n",
       "22           0.001               sqrt                200   \n",
       "23           0.001               sqrt                300   \n",
       "24           0.001               log2                100   \n",
       "25           0.001               log2                200   \n",
       "26           0.001               log2                300   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'ccp_alpha': 0.1, 'max_features': 'auto', 'n_...           0.788630   \n",
       "1   {'ccp_alpha': 0.1, 'max_features': 'auto', 'n_...           0.788630   \n",
       "2   {'ccp_alpha': 0.1, 'max_features': 'auto', 'n_...           0.788630   \n",
       "3   {'ccp_alpha': 0.1, 'max_features': 'sqrt', 'n_...           0.788630   \n",
       "4   {'ccp_alpha': 0.1, 'max_features': 'sqrt', 'n_...           0.788630   \n",
       "5   {'ccp_alpha': 0.1, 'max_features': 'sqrt', 'n_...           0.788630   \n",
       "6   {'ccp_alpha': 0.1, 'max_features': 'log2', 'n_...           0.788630   \n",
       "7   {'ccp_alpha': 0.1, 'max_features': 'log2', 'n_...           0.788630   \n",
       "8   {'ccp_alpha': 0.1, 'max_features': 'log2', 'n_...           0.788630   \n",
       "9   {'ccp_alpha': 0.01, 'max_features': 'auto', 'n...           0.788630   \n",
       "10  {'ccp_alpha': 0.01, 'max_features': 'auto', 'n...           0.788630   \n",
       "11  {'ccp_alpha': 0.01, 'max_features': 'auto', 'n...           0.788630   \n",
       "12  {'ccp_alpha': 0.01, 'max_features': 'sqrt', 'n...           0.788630   \n",
       "13  {'ccp_alpha': 0.01, 'max_features': 'sqrt', 'n...           0.788630   \n",
       "14  {'ccp_alpha': 0.01, 'max_features': 'sqrt', 'n...           0.788630   \n",
       "15  {'ccp_alpha': 0.01, 'max_features': 'log2', 'n...           0.788630   \n",
       "16  {'ccp_alpha': 0.01, 'max_features': 'log2', 'n...           0.788630   \n",
       "17  {'ccp_alpha': 0.01, 'max_features': 'log2', 'n...           0.788630   \n",
       "18  {'ccp_alpha': 0.001, 'max_features': 'auto', '...           0.857143   \n",
       "19  {'ccp_alpha': 0.001, 'max_features': 'auto', '...           0.870262   \n",
       "20  {'ccp_alpha': 0.001, 'max_features': 'auto', '...           0.868805   \n",
       "21  {'ccp_alpha': 0.001, 'max_features': 'sqrt', '...           0.864431   \n",
       "22  {'ccp_alpha': 0.001, 'max_features': 'sqrt', '...           0.861516   \n",
       "23  {'ccp_alpha': 0.001, 'max_features': 'sqrt', '...           0.865889   \n",
       "24  {'ccp_alpha': 0.001, 'max_features': 'log2', '...           0.857143   \n",
       "25  {'ccp_alpha': 0.001, 'max_features': 'log2', '...           0.870262   \n",
       "26  {'ccp_alpha': 0.001, 'max_features': 'log2', '...           0.864431   \n",
       "\n",
       "    split1_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0            0.788630  ...         0.788215        0.000539               10   \n",
       "1            0.788630  ...         0.788215        0.000539               10   \n",
       "2            0.788630  ...         0.788215        0.000539               10   \n",
       "3            0.788630  ...         0.788215        0.000539               10   \n",
       "4            0.788630  ...         0.788215        0.000539               10   \n",
       "5            0.788630  ...         0.788215        0.000539               10   \n",
       "6            0.788630  ...         0.788215        0.000539               10   \n",
       "7            0.788630  ...         0.788215        0.000539               10   \n",
       "8            0.788630  ...         0.788215        0.000539               10   \n",
       "9            0.788630  ...         0.788215        0.000539               10   \n",
       "10           0.788630  ...         0.788215        0.000539               10   \n",
       "11           0.788630  ...         0.788215        0.000539               10   \n",
       "12           0.788630  ...         0.788215        0.000539               10   \n",
       "13           0.788630  ...         0.788215        0.000539               10   \n",
       "14           0.788630  ...         0.788215        0.000539               10   \n",
       "15           0.788630  ...         0.788215        0.000539               10   \n",
       "16           0.788630  ...         0.788215        0.000539               10   \n",
       "17           0.788630  ...         0.788215        0.000539               10   \n",
       "18           0.854227  ...         0.845096        0.009288                9   \n",
       "19           0.862974  ...         0.852679        0.012112                1   \n",
       "20           0.858601  ...         0.849470        0.012951                5   \n",
       "21           0.852770  ...         0.846262        0.010990                8   \n",
       "22           0.852770  ...         0.847721        0.008250                7   \n",
       "23           0.858601  ...         0.850054        0.010303                4   \n",
       "24           0.854227  ...         0.847723        0.007497                6   \n",
       "25           0.860058  ...         0.851804        0.011594                3   \n",
       "26           0.861516  ...         0.852096        0.010062                2   \n",
       "\n",
       "    split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0             0.788111            0.788111            0.788476   \n",
       "1             0.788111            0.788111            0.788476   \n",
       "2             0.788111            0.788111            0.788476   \n",
       "3             0.788111            0.788111            0.788476   \n",
       "4             0.788111            0.788111            0.788476   \n",
       "5             0.788111            0.788111            0.788476   \n",
       "6             0.788111            0.788111            0.788476   \n",
       "7             0.788111            0.788111            0.788476   \n",
       "8             0.788111            0.788111            0.788476   \n",
       "9             0.788111            0.788111            0.788476   \n",
       "10            0.788111            0.788111            0.788476   \n",
       "11            0.788111            0.788111            0.788476   \n",
       "12            0.788111            0.788111            0.788476   \n",
       "13            0.788111            0.788111            0.788476   \n",
       "14            0.788111            0.788111            0.788476   \n",
       "15            0.788111            0.788111            0.788476   \n",
       "16            0.788111            0.788111            0.788476   \n",
       "17            0.788111            0.788111            0.788476   \n",
       "18            0.943472            0.938731            0.943107   \n",
       "19            0.944931            0.940554            0.944201   \n",
       "20            0.944566            0.939460            0.942378   \n",
       "21            0.942013            0.939096            0.943472   \n",
       "22            0.942378            0.938366            0.942378   \n",
       "23            0.946025            0.940554            0.944931   \n",
       "24            0.940554            0.940919            0.943837   \n",
       "25            0.946389            0.940919            0.943837   \n",
       "26            0.943837            0.942378            0.945660   \n",
       "\n",
       "    split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0             0.788188            0.788188          0.788215         0.000135  \n",
       "1             0.788188            0.788188          0.788215         0.000135  \n",
       "2             0.788188            0.788188          0.788215         0.000135  \n",
       "3             0.788188            0.788188          0.788215         0.000135  \n",
       "4             0.788188            0.788188          0.788215         0.000135  \n",
       "5             0.788188            0.788188          0.788215         0.000135  \n",
       "6             0.788188            0.788188          0.788215         0.000135  \n",
       "7             0.788188            0.788188          0.788215         0.000135  \n",
       "8             0.788188            0.788188          0.788215         0.000135  \n",
       "9             0.788188            0.788188          0.788215         0.000135  \n",
       "10            0.788188            0.788188          0.788215         0.000135  \n",
       "11            0.788188            0.788188          0.788215         0.000135  \n",
       "12            0.788188            0.788188          0.788215         0.000135  \n",
       "13            0.788188            0.788188          0.788215         0.000135  \n",
       "14            0.788188            0.788188          0.788215         0.000135  \n",
       "15            0.788188            0.788188          0.788215         0.000135  \n",
       "16            0.788188            0.788188          0.788215         0.000135  \n",
       "17            0.788188            0.788188          0.788215         0.000135  \n",
       "18            0.939118            0.941305          0.941147         0.001961  \n",
       "19            0.937295            0.942763          0.941949         0.002765  \n",
       "20            0.942034            0.942034          0.942095         0.001621  \n",
       "21            0.938024            0.941670          0.940855         0.001998  \n",
       "22            0.939118            0.942034          0.940855         0.001746  \n",
       "23            0.940211            0.943857          0.943116         0.002337  \n",
       "24            0.938389            0.944951          0.941730         0.002367  \n",
       "25            0.938024            0.944586          0.942751         0.002949  \n",
       "26            0.941305            0.940941          0.942824         0.001739  \n",
       "\n",
       "[27 rows x 23 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cv_results_ : cross validation 결과\n",
    "pd.DataFrame(grid_search.cv_results_)\n",
    "# parameter 따라서 모두 출력\n",
    "# mean_train_score : 평균 출력\n",
    "# 나무 100개일 때보다 갑자기 성능이 확 높아짐\n",
    "# model = RandomForestClassifier가 아니고 grid_search.best_estimator_를 바로 갖다 쓰면 됨\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.001, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=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=200,\n",
       "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 혼동행렬\n",
    "y_pred =  grid_search.best_estimator_.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 148,   43],\n",
       "       [ 186, 1093]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ccp_alpha가 들어가서 좀 더 성능이 안좋아짐... 많지 않아서 underfitting이 일어남 ->  상황에 맞춰서\n",
    "accuracy_score(y_pred, y_test)\n",
    "confusion_matrix(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "apt = pd.read_csv('./input/aptPrice.csv', engine = 'python') # C 언어 기반이기 때문에 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>아파트명</th>\n",
       "      <th>전용면적</th>\n",
       "      <th>층</th>\n",
       "      <th>거래금액</th>\n",
       "      <th>최고층수</th>\n",
       "      <th>승강기수</th>\n",
       "      <th>총동수</th>\n",
       "      <th>연면적</th>\n",
       "      <th>주거전용면적</th>\n",
       "      <th>일반관리인원</th>\n",
       "      <th>...</th>\n",
       "      <th>경과년수</th>\n",
       "      <th>계단식</th>\n",
       "      <th>분양</th>\n",
       "      <th>위탁관리</th>\n",
       "      <th>개별난방</th>\n",
       "      <th>주차관제</th>\n",
       "      <th>브랜드사</th>\n",
       "      <th>로그금액</th>\n",
       "      <th>루트금액</th>\n",
       "      <th>변환금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK허브프리모</td>\n",
       "      <td>33.80</td>\n",
       "      <td>7</td>\n",
       "      <td>32900.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35505.34</td>\n",
       "      <td>9680.09</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.401228</td>\n",
       "      <td>181.383571</td>\n",
       "      <td>3972.879806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>대우디오빌</td>\n",
       "      <td>31.81</td>\n",
       "      <td>4</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9782.62</td>\n",
       "      <td>4260.59</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.203592</td>\n",
       "      <td>164.316767</td>\n",
       "      <td>3394.049026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>신동아(22)</td>\n",
       "      <td>35.73</td>\n",
       "      <td>1</td>\n",
       "      <td>58500.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>58816.99</td>\n",
       "      <td>45646.06</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.976782</td>\n",
       "      <td>241.867732</td>\n",
       "      <td>6284.383338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>신동아(22)</td>\n",
       "      <td>35.73</td>\n",
       "      <td>9</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>58816.99</td>\n",
       "      <td>45646.06</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.127263</td>\n",
       "      <td>260.768096</td>\n",
       "      <td>7084.890404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>신동아(22)</td>\n",
       "      <td>84.52</td>\n",
       "      <td>6</td>\n",
       "      <td>92500.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>58816.99</td>\n",
       "      <td>45646.06</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.434964</td>\n",
       "      <td>304.138127</td>\n",
       "      <td>9053.280186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      아파트명   전용면적  층     거래금액  최고층수  승강기수  총동수       연면적    주거전용면적  일반관리인원  \\\n",
       "0  SK허브프리모  33.80  7  32900.0    20     4    1  35505.34   9680.09       6   \n",
       "1    대우디오빌  31.81  4  27000.0    15     2    1   9782.62   4260.59       3   \n",
       "2  신동아(22)  35.73  1  58500.0    13    20    6  58816.99  45646.06       6   \n",
       "3  신동아(22)  35.73  9  68000.0    13    20    6  58816.99  45646.06       6   \n",
       "4  신동아(22)  84.52  6  92500.0    13    20    6  58816.99  45646.06       6   \n",
       "\n",
       "   ...  경과년수  계단식  분양  위탁관리  개별난방  주차관제  브랜드사       로그금액        루트금액  \\\n",
       "0  ...    11    0   1     1     1     0     1  10.401228  181.383571   \n",
       "1  ...    13    0   1     1     1     0     1  10.203592  164.316767   \n",
       "2  ...    20    1   1     1     1     0     0  10.976782  241.867732   \n",
       "3  ...    20    1   1     1     1     0     0  11.127263  260.768096   \n",
       "4  ...    20    1   1     1     1     0     0  11.434964  304.138127   \n",
       "\n",
       "          변환금액  \n",
       "0  3972.879806  \n",
       "1  3394.049026  \n",
       "2  6284.383338  \n",
       "3  7084.890404  \n",
       "4  9053.280186  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apt.head()\n",
    "# 거래금액이 target, 연속형이기 때문에 classifier가 아님"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1516 entries, 0 to 1515\n",
      "Data columns (total 27 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   아파트명    1516 non-null   object \n",
      " 1   전용면적    1516 non-null   float64\n",
      " 2   층       1516 non-null   int64  \n",
      " 3   거래금액    1516 non-null   float64\n",
      " 4   최고층수    1516 non-null   int64  \n",
      " 5   승강기수    1516 non-null   int64  \n",
      " 6   총동수     1516 non-null   int64  \n",
      " 7   연면적     1516 non-null   float64\n",
      " 8   주거전용면적  1516 non-null   float64\n",
      " 9   일반관리인원  1516 non-null   int64  \n",
      " 10  경비인원    1516 non-null   int64  \n",
      " 11  청소인원    1516 non-null   int64  \n",
      " 12  CCTV개수  1516 non-null   int64  \n",
      " 13  총세대수    1516 non-null   int64  \n",
      " 14  면적당관리비  1516 non-null   float64\n",
      " 15  총주차대수   1516 non-null   int64  \n",
      " 16  세대주차대수  1516 non-null   float64\n",
      " 17  경과년수    1516 non-null   int64  \n",
      " 18  계단식     1516 non-null   int64  \n",
      " 19  분양      1516 non-null   int64  \n",
      " 20  위탁관리    1516 non-null   int64  \n",
      " 21  개별난방    1516 non-null   int64  \n",
      " 22  주차관제    1516 non-null   int64  \n",
      " 23  브랜드사    1516 non-null   int64  \n",
      " 24  로그금액    1516 non-null   float64\n",
      " 25  루트금액    1516 non-null   float64\n",
      " 26  변환금액    1516 non-null   float64\n",
      "dtypes: float64(9), int64(17), object(1)\n",
      "memory usage: 319.9+ KB\n"
     ]
    }
   ],
   "source": [
    "apt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['전용면적',\n",
       " '층',\n",
       " '거래금액',\n",
       " '최고층수',\n",
       " '승강기수',\n",
       " '총동수',\n",
       " '연면적',\n",
       " '주거전용면적',\n",
       " '일반관리인원',\n",
       " '경비인원',\n",
       " '청소인원',\n",
       " 'CCTV개수',\n",
       " '총세대수',\n",
       " '면적당관리비',\n",
       " '총주차대수',\n",
       " '세대주차대수',\n",
       " '경과년수',\n",
       " '계단식',\n",
       " '분양',\n",
       " '위탁관리',\n",
       " '개별난방',\n",
       " '주차관제',\n",
       " '브랜드사']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = apt.columns.tolist()\n",
    "col.remove('아파트명')\n",
    "col.remove('로그금액') #종속변수와 관련도가 높음\n",
    "col.remove('루트금액')\n",
    "col.remove('변환금액')\n",
    "col.remove('거래금액') # 종속변수\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>아파트명</th>\n",
       "      <th>전용면적</th>\n",
       "      <th>층</th>\n",
       "      <th>거래금액</th>\n",
       "      <th>최고층수</th>\n",
       "      <th>승강기수</th>\n",
       "      <th>총동수</th>\n",
       "      <th>연면적</th>\n",
       "      <th>주거전용면적</th>\n",
       "      <th>일반관리인원</th>\n",
       "      <th>...</th>\n",
       "      <th>경과년수</th>\n",
       "      <th>계단식</th>\n",
       "      <th>분양</th>\n",
       "      <th>위탁관리</th>\n",
       "      <th>개별난방</th>\n",
       "      <th>주차관제</th>\n",
       "      <th>브랜드사</th>\n",
       "      <th>로그금액</th>\n",
       "      <th>루트금액</th>\n",
       "      <th>변환금액</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK허브프리모</td>\n",
       "      <td>33.80</td>\n",
       "      <td>7</td>\n",
       "      <td>32900.0</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35505.34</td>\n",
       "      <td>9680.09</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.401228</td>\n",
       "      <td>181.383571</td>\n",
       "      <td>3972.879806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>대우디오빌</td>\n",
       "      <td>31.81</td>\n",
       "      <td>4</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9782.62</td>\n",
       "      <td>4260.59</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.203592</td>\n",
       "      <td>164.316767</td>\n",
       "      <td>3394.049026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>신동아(22)</td>\n",
       "      <td>35.73</td>\n",
       "      <td>1</td>\n",
       "      <td>58500.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>58816.99</td>\n",
       "      <td>45646.06</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.976782</td>\n",
       "      <td>241.867732</td>\n",
       "      <td>6284.383338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>신동아(22)</td>\n",
       "      <td>35.73</td>\n",
       "      <td>9</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>58816.99</td>\n",
       "      <td>45646.06</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.127263</td>\n",
       "      <td>260.768096</td>\n",
       "      <td>7084.890404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>신동아(22)</td>\n",
       "      <td>84.52</td>\n",
       "      <td>6</td>\n",
       "      <td>92500.0</td>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>58816.99</td>\n",
       "      <td>45646.06</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.434964</td>\n",
       "      <td>304.138127</td>\n",
       "      <td>9053.280186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      아파트명   전용면적  층     거래금액  최고층수  승강기수  총동수       연면적    주거전용면적  일반관리인원  \\\n",
       "0  SK허브프리모  33.80  7  32900.0    20     4    1  35505.34   9680.09       6   \n",
       "1    대우디오빌  31.81  4  27000.0    15     2    1   9782.62   4260.59       3   \n",
       "2  신동아(22)  35.73  1  58500.0    13    20    6  58816.99  45646.06       6   \n",
       "3  신동아(22)  35.73  9  68000.0    13    20    6  58816.99  45646.06       6   \n",
       "4  신동아(22)  84.52  6  92500.0    13    20    6  58816.99  45646.06       6   \n",
       "\n",
       "   ...  경과년수  계단식  분양  위탁관리  개별난방  주차관제  브랜드사       로그금액        루트금액  \\\n",
       "0  ...    11    0   1     1     1     0     1  10.401228  181.383571   \n",
       "1  ...    13    0   1     1     1     0     1  10.203592  164.316767   \n",
       "2  ...    20    1   1     1     1     0     0  10.976782  241.867732   \n",
       "3  ...    20    1   1     1     1     0     0  11.127263  260.768096   \n",
       "4  ...    20    1   1     1     1     0     0  11.434964  304.138127   \n",
       "\n",
       "          변환금액  \n",
       "0  3972.879806  \n",
       "1  3394.049026  \n",
       "2  6284.383338  \n",
       "3  7084.890404  \n",
       "4  9053.280186  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apt.head() # string type 변수는 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_data = apt[col]\n",
    "y_data = apt['거래금액']\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_data, y_data, test_size = 0.3, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=500, n_jobs=-1, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model = RandomForestRegressor (n_estimators = 500, n_jobs=-1)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=500, n_jobs=-1, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 86972.4  , 139998.   , 204902.   , 124232.   ,  99959.6  ,\n",
       "        92364.8  , 199959.   ,  72150.   , 136260.   ,  53379.   ,\n",
       "        99955.   , 188325.   , 146999.   , 184996.   , 146012.   ,\n",
       "       127934.   , 227969.   ,  58700.2  , 149085.4  ,  78915.144,\n",
       "       160000.   , 104993.   ,  59993.16 , 125001.   , 105010.8  ,\n",
       "       160000.   , 199933.   ,  82124.8  , 131268.   , 164209.   ,\n",
       "       123084.   , 115057.4  , 199928.   , 205035.   , 214966.   ,\n",
       "        99554.2  , 106955.4  , 112749.8  , 151962.   , 205141.   ,\n",
       "        37821.8  , 130000.   , 165010.   , 136978.   , 115015.   ,\n",
       "       114991.4  ,  84029.6  , 201260.   , 127361.   , 136958.   ,\n",
       "       240138.   ,  80394.256, 180000.   , 102028.   , 175025.   ,\n",
       "       242288.   , 147969.4  ,  93708.4  , 114803.2  , 160000.   ,\n",
       "        77944.488, 175025.   , 219128.   , 175993.8  , 110001.   ,\n",
       "       184998.   , 140008.   , 175004.   , 128957.   , 185084.   ,\n",
       "       173995.   , 144015.   , 209194.   ,  29021.8  ,  32180.6  ,\n",
       "       139028.   , 110004.2  , 136983.   , 139479.   , 130012.8  ,\n",
       "       119994.   , 113990.2  , 130003.4  ,  84922.   ,  58356.   ,\n",
       "       140000.5  , 114965.4  , 178937.   , 143881.4  , 110021.4  ,\n",
       "       127991.   ,  58922.   ,  99999.6  , 126885.2  , 114001.4  ,\n",
       "        75894.   , 170616.   , 130000.8  , 142827.4  , 107876.   ,\n",
       "       144991.6  , 151008.   ,  30868.64 ,  99956.   , 157996.   ,\n",
       "       133952.   , 108946.   , 180127.4  , 155521.2  , 154483.   ,\n",
       "        52276.1  ,  90004.4  , 124995.4  , 142848.2  ,  33729.96 ,\n",
       "       173969.   , 169979.6  , 112779.8  , 190027.   , 140008.   ,\n",
       "       136300.   , 104961.9  , 124983.4  , 163123.2  , 138002.   ,\n",
       "       150042.2  , 142136.2  , 135036.   ,  85352.4  ,  90004.4  ,\n",
       "       169037.2  , 124232.4  ,  89735.6  , 130004.2  ,  62233.9  ,\n",
       "       197080.   ,  59947.6  , 154035.   ,  32833.28 , 119994.   ,\n",
       "       204968.   , 124999.4  , 158901.   , 144986.   ,  72640.   ,\n",
       "       176032.4  , 178346.6  , 204946.   , 228020.   , 146841.   ,\n",
       "       165024.   , 172849.   , 200005.   , 147980.8  , 119999.8  ,\n",
       "       178516.2  , 166868.   , 209749.   , 171990.   ,  74967.2  ,\n",
       "        58493.6  ,  64968.4  ,  66662.   , 136978.   , 188287.   ,\n",
       "       213288.   , 199810.   , 140000.   , 151938.   , 139998.   ,\n",
       "       140004.   , 213332.   , 214596.   , 158535.   ,  73866.8  ,\n",
       "       110049.   ,  93035.8  , 142529.5  , 129565.8  , 141155.7  ,\n",
       "       104999.9  , 148985.6  , 190016.   , 144416.6  ,  29394.2  ,\n",
       "        32628.6  , 178346.6  ,  32074.96 , 150014.   , 134996.8  ,\n",
       "        94770.4  , 125576.   , 169999.   , 110032.4  , 209787.   ,\n",
       "       158970.   , 108946.   , 165014.   , 160027.   , 219941.   ,\n",
       "       151890.   ,  88109.6  , 215165.   , 193763.   ,  97932.   ,\n",
       "        80580.552,  66470.4  , 114957.4  , 199979.   , 214056.   ,\n",
       "       188260.   , 107002.6  , 188553.   ,  62853.   , 249220.   ,\n",
       "        67798.988, 182781.2  , 184994.   , 247851.   , 116904.8  ,\n",
       "       139997.   , 105006.9  , 167882.   , 155938.   , 132990.   ,\n",
       "       201396.   , 190022.   , 110039.8  , 108972.6  ,  82999.   ,\n",
       "       130000.   , 220003.   , 119998.   , 237966.4  , 150001.   ,\n",
       "       167869.   , 139028.   , 201362.   , 150058.2  ,  64400.2  ,\n",
       "       193757.   , 164999.   , 125928.6  , 102950.   , 128956.8  ,\n",
       "        79990.184, 209946.   , 157995.   ,  94334.4  , 119994.   ,\n",
       "       127998.   , 203324.   , 161972.2  , 208046.   ,  63279.38 ,\n",
       "       157995.   ,  59852.48 , 171993.   , 235122.6  ,  97930.   ,\n",
       "       229955.   , 141449.6  , 104991.5  , 232311.6  ,  59989.16 ,\n",
       "        51928.   ,  93032.   ,  53284.6  , 130000.   ,  77130.176,\n",
       "       164988.   , 225066.   , 128982.   , 135001.4  ,  64281.6  ,\n",
       "       133882.4  ,  88800.8  , 150007.   , 187566.   , 237066.6  ,\n",
       "       160000.   , 150002.   , 230088.   , 110039.8  , 126976.6  ,\n",
       "       189977.   , 220043.   , 181981.   , 108998.8  , 199915.   ,\n",
       "       213834.   , 154043.5  , 178960.4  , 153019.   ,  78067.424,\n",
       "        72654.   , 239825.   , 123021.4  , 111972.8  , 111971.4  ,\n",
       "       117985.   ,  90008.   ,  99637.4  , 160310.   , 178494.   ,\n",
       "       105178.   , 134969.   , 166011.   , 227720.   ,  93254.2  ,\n",
       "       199842.   ,  60066.74 , 195010.   ,  88494.   , 116014.8  ,\n",
       "       156609.6  , 151859.   ,  84878.6  , 201457.   , 110042.6  ,\n",
       "        60120.62 , 146088.2  , 140008.   , 185047.   ,  62343.18 ,\n",
       "       142066.   , 153435.   , 174979.   , 115858.8  , 169996.2  ,\n",
       "       135008.   , 160027.   , 153014.   ,  86002.6  , 190462.   ,\n",
       "       130009.4  ,  78264.664,  71105.4  ,  95002.   , 197917.   ,\n",
       "       120980.4  ,  30577.56 , 125919.4  , 180008.   , 160000.   ,\n",
       "       177333.6  , 231163.   , 170176.   , 160000.   , 102899.2  ,\n",
       "        86265.4  , 124669.   ,  97873.4  , 135001.8  , 167855.   ,\n",
       "       180005.   , 151928.   , 144997.   , 100940.4  , 160000.   ,\n",
       "       209009.   , 127373.   , 165005.   , 161738.   , 123949.6  ,\n",
       "        64777.8  , 178522.4  ,  75000.8  ,  75421.376, 109984.2  ,\n",
       "       139999.   , 148996.4  , 165032.   , 174978.   , 156836.5  ,\n",
       "       242594.   ,  80151.68 , 187546.   ,  60132.28 , 107004.   ,\n",
       "        90004.4  , 115010.2  , 175076.   ,  69470.712, 108423.8  ,\n",
       "       117971.2  ,  91189.2  , 193065.   , 123962.8  ,  38118.12 ,\n",
       "        71033.4  , 174983.   , 191581.   , 150007.   , 155012.2  ,\n",
       "       139998.   , 142917.   ,  94956.2  , 175003.   , 181983.   ,\n",
       "       161069.   ,  69940.996, 128949.   , 190400.   , 119995.8  ,\n",
       "        94832.4  ,  94837.2  , 180016.2  , 136988.   ,  58510.   ,\n",
       "       199969.   , 123979.8  , 247681.   , 162959.2  , 125010.4  ,\n",
       "       160027.   , 127362.6  ,  93254.   , 157996.   ,  75905.6  ,\n",
       "       117020.8  , 126568.6  , 119023.   , 182788.8  , 219969.   ,\n",
       "       146845.   , 133891.4  , 141188.3  ,  62331.58 ,  70619.6  ,\n",
       "       166090.   , 161176.6  , 112507.8  , 115010.2  , 149110.   ,\n",
       "        84971.6  ,  85019.   ,  94382.4  ,  32840.56 , 189987.   ,\n",
       "       160000.   ,  86471.   , 145004.8  , 115036.2  ,  91137.2  ,\n",
       "       116946.6  , 136073.   ,  61918.58 , 161988.6  ,  94912.   ,\n",
       "        30533.2  ,  89888.8  ,  99959.4  , 154045.   , 172795.   ])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred\n",
    "# regressor는 classifier와 달리 명확한 class 분류의 정답이 없으며 예측값과 실제값을 뻈을 때 차이가 없어야 좋은 모델,\n",
    "# 시험 점수는 100점을 0으로 예측해도 오차가 100, 금액은 정확하게 차이가 난다는 domain 지식이 필요함\n",
    "# RMSE - 오차 판별"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160.733854945055"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE\n",
    "# np.sqrt((실제값-예측값)^2)\n",
    "np.sqrt((y_test - y_pred)**2).sum() # 오차의 합\n",
    "np.sqrt((y_test - y_pred)**2).sum()/len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2, -2])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=[1,2]\n",
    "b=[3,4]\n",
    "#a-b # 계산 불가\n",
    "np.array(a)-np.array(b)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}