day-5-machine-learning-part-II/03 - Random Forests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:46:13.268933Z",
     "start_time": "2019-07-04T12:46:13.261386Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:41:02.881021Z",
     "start_time": "2019-07-04T12:41:01.050794Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:41:37.525298Z",
     "start_time": "2019-07-04T12:41:37.388766Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, \n",
    "                                                    cancer.target, \n",
    "                                                    stratify=cancer.target, \n",
    "                                                    random_state=1)\n",
    "rf = RandomForestClassifier(n_estimators=100).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:41:38.837112Z",
     "start_time": "2019-07-04T12:41:38.821413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05958855, 0.01315869, 0.03209834, 0.07325123, 0.00359689,\n",
       "       0.01471979, 0.05362886, 0.08196504, 0.00442049, 0.00465715,\n",
       "       0.00671823, 0.00317893, 0.01763635, 0.02806233, 0.00362721,\n",
       "       0.00434324, 0.01218108, 0.00806194, 0.00372994, 0.00411391,\n",
       "       0.12964814, 0.01259946, 0.112041  , 0.08871077, 0.01224458,\n",
       "       0.01625372, 0.03657524, 0.13781781, 0.01242649, 0.00894461])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:41:49.190397Z",
     "start_time": "2019-07-04T12:41:48.355207Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:41:49.657513Z",
     "start_time": "2019-07-04T12:41:49.195043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a218ad898>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(rf.feature_importances_,\n",
    "          index=cancer.feature_names).plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "Use a random forest classifier or random forest regressor on a dataset of your choice.\n",
    "Try different values of n_estimators and max_depth and see how they impact performance and runtime.\n",
    "Tune ``max_features`` with GridSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:46:17.995759Z",
     "start_time": "2019-07-04T12:46:17.877585Z"
    }
   },
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race   gender  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  income  \n",
       "0             0              40   United-States   <=50K  \n",
       "1             0              13   United-States   <=50K  \n",
       "2             0              40   United-States   <=50K  \n",
       "3             0              40   United-States   <=50K  \n",
       "4             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('data/adult.csv',index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:46:18.904987Z",
     "start_time": "2019-07-04T12:46:18.689808Z"
    }
   },
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race   gender  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country  \n",
       "0             0              40   United-States  \n",
       "1             0              13   United-States  \n",
       "2             0              40   United-States  \n",
       "3             0              40   United-States  \n",
       "4             0              40            Cuba  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income = data.income\n",
    "data_features = data.drop(\"income\", axis=1)\n",
    "display(data_features.head())\n",
    "\n",
    "### one hot encode data\n",
    "data_one_hot = pd.get_dummies(data_features)\n",
    "data_one_hot.head()\n",
    "\n",
    "### Preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_one_hot, income)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:46:21.718531Z",
     "start_time": "2019-07-04T12:46:20.137736Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8398031996941338\n"
     ]
    }
   ],
   "source": [
    "### Cross-validation with default parameters\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "scores = cross_val_score(RandomForestClassifier(), X_train_scaled, y_train, cv=5)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:53:12.652707Z",
     "start_time": "2019-07-04T12:51:48.448974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=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='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': range(2, 30)}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'n_estimators': range(2, 30)}\n",
    "grid = GridSearchCV(RandomForestClassifier(random_state=0), \n",
    "                    param_grid=param_grid,\n",
    "                    cv=5,\n",
    "                   return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:53:12.983296Z",
     "start_time": "2019-07-04T12:53:12.655821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'param_n_estimators')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = pd.DataFrame(grid.cv_results_)\n",
    "scores.plot(x='param_n_estimators', y='mean_train_score', yerr='std_train_score', ax=plt.gca())\n",
    "scores.plot(x='param_n_estimators', y='mean_test_score', yerr='std_test_score', ax=plt.gca())\n",
    "plt.ylabel('Score',fontsize=20)\n",
    "plt.xlabel('param_n_estimators',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:56:31.247823Z",
     "start_time": "2019-07-04T12:56:07.425067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=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='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'max_depth': range(2, 20)}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'max_depth': range(2, 20)}\n",
    "grid = GridSearchCV(RandomForestClassifier(random_state=0), \n",
    "                    param_grid=param_grid,\n",
    "                    cv=5,\n",
    "                   return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T12:56:31.546985Z",
     "start_time": "2019-07-04T12:56:31.250441Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'param_max_depth')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = pd.DataFrame(grid.cv_results_)\n",
    "scores.plot(x='param_max_depth', y='mean_train_score', yerr='std_train_score', ax=plt.gca())\n",
    "scores.plot(x='param_max_depth', y='mean_test_score', yerr='std_test_score', ax=plt.gca())\n",
    "plt.ylabel('Score',fontsize=20)\n",
    "plt.xlabel('param_max_depth',fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:02:38.041812Z",
     "start_time": "2019-07-04T13:01:50.603427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=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='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'max_depth': range(15, 20), 'n_estimators': range(10, 15)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth': range(15, 20),\n",
    "              'n_estimators': range(10, 15)\n",
    "             }\n",
    "grid = GridSearchCV(RandomForestClassifier(random_state=0), \n",
    "                    param_grid=param_grid,\n",
    "                    cv=5,\n",
    "                   return_train_score=True)\n",
    "\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:02:38.052546Z",
     "start_time": "2019-07-04T13:02:38.044581Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 17, 'n_estimators': 13}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:04:27.964701Z",
     "start_time": "2019-07-04T13:04:27.899963Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_test)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:04:49.263974Z",
     "start_time": "2019-07-04T13:04:49.235384Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred=grid.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-04T13:05:49.941099Z",
     "start_time": "2019-07-04T13:05:49.937290Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
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     "end_time": "2019-07-04T13:06:02.487512Z",
     "start_time": "2019-07-04T13:06:02.466487Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7583834909716252"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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