007. machine learning ensemble - random forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 출처\n",
    "\n",
    "- https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python\n",
    "- https://www.kaggle.com/lsjsj92/simple-titanic-kernel-82-for-beginner-like-me\n",
    "- https://www.kaggle.com/startupsci/titanic-data-science-solutions\n",
    "- https://www.kaggle.com/ash316/eda-to-prediction-dietanic\n",
    "- https://www.kaggle.com/mjbahmani/a-comprehensive-ml-workflow-with-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../datas/titanic/train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age null값 비율 : 19.865\n",
      "cabin null값 비율 : 77.104\n"
     ]
    }
   ],
   "source": [
    "print(\"age null값 비율 : {0:.3f}\".format((data['Age'].isna().sum() / len(data)) * 100))\n",
    "print(\"cabin null값 비율 : {0:.3f}\".format((data['Cabin'].isna().sum() / len(data)) * 100 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Embarked'].fillna('S', inplace = True)\n",
    "data['Fare'].fillna(0, inplace=True)\n",
    "data['Fare'] = data['Fare'].map(lambda x : np.log(x) if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Initial'] = data['Name'].str.extract('([A-Za-z]+)\\.')\n",
    "data['Initial'].data(['Mlle','Mme','Ms','Dr','Major','Lady','Countess','Jonkheer','Col','Rev','Capt','Sir','Don', 'Dona'],['Miss','Miss','Miss','Mr','Mr','Mrs','Mrs','Other','Other','Other','Mr','Mr','Mr','Other'],inplace=True)\n",
    "mapping = {\n",
    "    \"Mr\":0,\n",
    "    \"Miss\":1,\n",
    "    \"Mrs\" : 1,\n",
    "    \"Master\":2,\n",
    "    \"Other\":3\n",
    "}\n",
    "\n",
    "data['Initial'] = data['Initial'].map(mapping)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping_sex = {\n",
    "    'male' : 0,\n",
    "    'female': 1\n",
    "}\n",
    "\n",
    "mapping_em = {\n",
    "    'S' :0,\n",
    "    'C' :1,\n",
    "    'Q' :2\n",
    "}\n",
    "\n",
    "\n",
    "data['Sex'] = data['Sex'].map(mapping_sex)\n",
    "data['Embarked'] = data['Embarked'].map(mapping_em)\n",
    "\n",
    "\n",
    "data.drop(['PassengerId', \"Ticket\", \"Cabin\", \"Name\"], axis = 1, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Initial\n",
       "0    32.739609\n",
       "1    27.834615\n",
       "2     4.574167\n",
       "3    45.888889\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Initial')['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[ (data['Age'].isnull()) & (data['Initial'] == 0), 'Age' ] = 32\n",
    "data.loc[ (data['Age'].isnull()) & (data['Initial'] == 1), 'Age' ] = 28\n",
    "data.loc[ (data['Age'].isnull()) & (data['Initial'] == 2), 'Age' ] = 5\n",
    "data.loc[ (data['Age'].isnull()) & (data['Initial'] == 3), 'Age' ] = 45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Initial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch      Fare  Embarked  Initial\n",
       "0         0       3    0  22.0      1      0  1.981001         0        0\n",
       "1         1       1    1  38.0      1      0  4.266662         1        1\n",
       "2         1       3    1  26.0      0      0  2.070022         0        1\n",
       "3         1       1    1  35.0      1      0  3.972177         0        1\n",
       "4         0       3    0  35.0      0      0  2.085672         0        0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Survived']\n",
    "X = data.drop('Survived', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\soojin\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(random_state=0)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 :0.810\n"
     ]
    }
   ],
   "source": [
    "pred = rf.predict(X_test)\n",
    "print(\"정확도 :{0:.3f}\".format(accuracy_score(y_test, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_param_grid = {\n",
    "    'n_estimators' : [100, 200, 300],\n",
    "    'max_depth' : [4, 6, 8, 10, 12],\n",
    "    'min_samples_leaf' : [3, 5, 6, 7, 10],\n",
    "    'min_samples_split' : [2, 3, 5, 7, 10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\soojin\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 128 candidates, totalling 384 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    4.4s\n",
      "[Parallel(n_jobs=-1)]: Done 176 tasks      | elapsed:    9.7s\n",
      "[Parallel(n_jobs=-1)]: Done 384 out of 384 | elapsed:   16.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=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=10, n_jobs=None,\n",
       "                                              oob_score=False, random_state=0,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'max_depth': [6, 8, 10, 12],\n",
       "                         'min_samples_leaf': [3, 5, 7, 10],\n",
       "                         'min_samples_split': [2, 3, 5, 10],\n",
       "                         'n_estimators': [100, 200]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid = GridSearchCV(rf, param_grid = rf_param_grid, scoring=\"accuracy\", n_jobs= -1, verbose = 1)\n",
    "rf_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최고 평균 정확도 : 0.8174\n",
      "최고의 파라미터 :  {'max_depth': 8, 'min_samples_leaf': 3, 'min_samples_split': 2, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "print(\"최고 평균 정확도 : {0:.4f}\".format(rf_grid.best_score_))\n",
    "print(\"최고의 파라미터 : \", rf_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time',\n",
       "       'param_max_depth', 'param_min_samples_leaf', 'param_min_samples_split',\n",
       "       'param_n_estimators', 'params', 'split0_test_score',\n",
       "       'split1_test_score', 'split2_test_score', 'mean_test_score',\n",
       "       'std_test_score', 'rank_test_score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(rf_grid.cv_results_)\n",
    "result.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.sort_values(by=['rank_test_score'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>{'max_depth': 8, 'min_samples_leaf': 3, 'min_s...</td>\n",
       "      <td>0.817416</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>{'max_depth': 8, 'min_samples_leaf': 3, 'min_s...</td>\n",
       "      <td>0.817416</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>{'max_depth': 8, 'min_samples_leaf': 3, 'min_s...</td>\n",
       "      <td>0.817416</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>{'max_depth': 8, 'min_samples_leaf': 10, 'min_...</td>\n",
       "      <td>0.814607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>{'max_depth': 12, 'min_samples_leaf': 5, 'min_...</td>\n",
       "      <td>0.814607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>{'max_depth': 12, 'min_samples_leaf': 5, 'min_...</td>\n",
       "      <td>0.814607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>{'max_depth': 10, 'min_samples_leaf': 3, 'min_...</td>\n",
       "      <td>0.814607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>{'max_depth': 10, 'min_samples_leaf': 3, 'min_...</td>\n",
       "      <td>0.814607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>{'max_depth': 12, 'min_samples_leaf': 5, 'min_...</td>\n",
       "      <td>0.814607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>{'max_depth': 12, 'min_samples_leaf': 5, 'min_...</td>\n",
       "      <td>0.814607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                params  mean_test_score  \\\n",
       "32   {'max_depth': 8, 'min_samples_leaf': 3, 'min_s...         0.817416   \n",
       "34   {'max_depth': 8, 'min_samples_leaf': 3, 'min_s...         0.817416   \n",
       "36   {'max_depth': 8, 'min_samples_leaf': 3, 'min_s...         0.817416   \n",
       "56   {'max_depth': 8, 'min_samples_leaf': 10, 'min_...         0.814607   \n",
       "105  {'max_depth': 12, 'min_samples_leaf': 5, 'min_...         0.814607   \n",
       "107  {'max_depth': 12, 'min_samples_leaf': 5, 'min_...         0.814607   \n",
       "68   {'max_depth': 10, 'min_samples_leaf': 3, 'min_...         0.814607   \n",
       "66   {'max_depth': 10, 'min_samples_leaf': 3, 'min_...         0.814607   \n",
       "111  {'max_depth': 12, 'min_samples_leaf': 5, 'min_...         0.814607   \n",
       "109  {'max_depth': 12, 'min_samples_leaf': 5, 'min_...         0.814607   \n",
       "\n",
       "     rank_test_score  \n",
       "32                 1  \n",
       "34                 1  \n",
       "36                 1  \n",
       "56                 4  \n",
       "105                4  \n",
       "107                4  \n",
       "68                 4  \n",
       "66                 4  \n",
       "111                4  \n",
       "109                4  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[['params', 'mean_test_score', 'rank_test_score']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 0.8603\n"
     ]
    }
   ],
   "source": [
    "model = rf_grid.best_estimator_\n",
    "pred = model.predict(X_test)\n",
    "acc = accuracy_score(y_test, pred)\n",
    "print(\"정확도 : {0:.4f}\".format(acc))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ft_importances = pd.Series(feature_importances, index = X_train.columns)\n",
    "ft_importances = ft_importances.sort_values(ascending=False)\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.title(\"feature importances\")\n",
    "sns.barplot(x=ft_importances, y = X_train.columns)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}