{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img align=\"center\" src=\"http://sydney.edu.au/images/content/about/logo-mono.jpg\">\n",
    "<h1 align=\"center\" style=\"margin-top:10px\">Machine Learning Using Python (MEAFA Workshop)</h1>\n",
    "<h2 align=\"center\" style=\"margin-top:10px\">Lesson 5: Trees and Random Forests</h2>\n",
    "<br>\n",
    "\n",
    "In this lesson we can consider a case study from customer relationship management to discuss decision trees and random forests.\n",
    "\n",
    "<a href=\"Customer Acquisition Data\">Customer Acquisition Data</a> <br>\n",
    "<a href=\"#Exploratory-data-analysis\">Exploratory Data Analysis</a> <br>\n",
    "<a href=\"#Decision-Tree\">Decision Tree</a> <br>\n",
    "<a href=\"#Bagging\">Bagging</a> <br>\n",
    "<a href=\"#Random-Forest\">Random Forest</a> <br>\n",
    "<a href=\"#Extremely-Randomised-Trees\">Extremely Randomised Trees</a> <br>\n",
    "<a href=\"#Model-Selection\">Model Selection</a> <br>\n",
    "<a href=\"#Model-Evaluation\">Model Evaluation</a> <br>\n",
    "\n",
    "This notebook relies on the following libraries and settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Packages\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plot settings\n",
    "sns.set_context('notebook') \n",
    "sns.set_style('ticks') \n",
    "colours = ['#1F77B4', '#FF7F0E', '#2CA02C', '#DB2728', '#9467BD', '#8C564B', '#E377C2','#7F7F7F', '#BCBD22', '#17BECF']\n",
    "crayon = ['#4E79A7','#F28E2C','#E15759','#76B7B2','#59A14F', '#EDC949','#AF7AA1','#FF9DA7','#9C755F','#BAB0AB']\n",
    "sns.set_palette(colours)\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (9, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Methods\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Model selection and evaluation tools\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_val_predict\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score\n",
    "from sklearn.metrics import precision_score, average_precision_score, log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customer Retention Data\n",
    "\n",
    "This dataset is taken from [Statistical Methods in Customer Relationship Management](http://onlinelibrary.wiley.com/book/10.1002/9781118349212), authored by  V. Kumar and J. Andrew Petersen.\n",
    "\n",
    "**Business objective**: to predict which customers will end the relationship with the business.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Censor</th>\n",
       "      <th>Avg_Ret_Exp</th>\n",
       "      <th>Avg_Ret_Exp_SQ</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Total_Crossbuy</th>\n",
       "      <th>Total_Freq</th>\n",
       "      <th>Total_Freq_SQ</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>89.61</td>\n",
       "      <td>8029.9521</td>\n",
       "      <td>1</td>\n",
       "      <td>30.16</td>\n",
       "      <td>1240</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "      <td>49.89</td>\n",
       "      <td>2489.0121</td>\n",
       "      <td>0</td>\n",
       "      <td>39.80</td>\n",
       "      <td>166</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "      <td>40.70</td>\n",
       "      <td>1656.4900</td>\n",
       "      <td>0</td>\n",
       "      <td>54.93</td>\n",
       "      <td>1016</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>85.76</td>\n",
       "      <td>7354.7776</td>\n",
       "      <td>0</td>\n",
       "      <td>45.83</td>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "      <td>31.90</td>\n",
       "      <td>1017.6100</td>\n",
       "      <td>0</td>\n",
       "      <td>69.03</td>\n",
       "      <td>313</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Duration  Censor  Avg_Ret_Exp  Avg_Ret_Exp_SQ  Industry  Revenue  \\\n",
       "Customer                                                                     \n",
       "1              500       0        89.61       8029.9521         1    30.16   \n",
       "2              730       1        49.89       2489.0121         0    39.80   \n",
       "3              730       1        40.70       1656.4900         0    54.93   \n",
       "4              340       0        85.76       7354.7776         0    45.83   \n",
       "5              730       1        31.90       1017.6100         0    69.03   \n",
       "\n",
       "          Employees  Total_Crossbuy  Total_Freq  Total_Freq_SQ  Churn  \n",
       "Customer                                                               \n",
       "1              1240               6          16            256      1  \n",
       "2               166               6          10            100      0  \n",
       "3              1016               2          14            196      0  \n",
       "4               122               2          15            225      1  \n",
       "5               313               1           9             81      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('Datasets/CustomerChurn.xls', index_col=[0])\n",
    "data['Churn'] = (data['Censor']==0).astype(int)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response='Churn'\n",
    "predictors=['Avg_Ret_Exp', 'Revenue', 'Employees', 'Total_Crossbuy', 'Total_Freq', 'Industry']\n",
    "data = data[[response]+predictors] # discarding variables that we will not use\n",
    "\n",
    "index_train, index_test  = train_test_split(np.array(data.index), stratify=data[response], train_size=0.8, random_state=5)\n",
    "\n",
    "train = data.loc[index_train,].copy()\n",
    "test =  data.loc[index_test,:].copy()\n",
    "\n",
    "y_train = train[response]\n",
    "y_test = test[response]\n",
    "\n",
    "X_train = train[predictors]\n",
    "X_test = test[predictors]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>Avg_Ret_Exp</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Employees</th>\n",
       "      <th>Total_Crossbuy</th>\n",
       "      <th>Total_Freq</th>\n",
       "      <th>Industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.46</td>\n",
       "      <td>36.10</td>\n",
       "      <td>39.74</td>\n",
       "      <td>671.75</td>\n",
       "      <td>3.40</td>\n",
       "      <td>11.47</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.50</td>\n",
       "      <td>33.01</td>\n",
       "      <td>16.33</td>\n",
       "      <td>468.54</td>\n",
       "      <td>1.54</td>\n",
       "      <td>5.85</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.35</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10.08</td>\n",
       "      <td>27.86</td>\n",
       "      <td>286.25</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>24.74</td>\n",
       "      <td>39.94</td>\n",
       "      <td>588.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>57.24</td>\n",
       "      <td>51.69</td>\n",
       "      <td>1025.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>145.16</td>\n",
       "      <td>74.97</td>\n",
       "      <td>1950.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Churn  Avg_Ret_Exp  Revenue  Employees  Total_Crossbuy  Total_Freq  \\\n",
       "count  400.00       400.00   400.00     400.00          400.00      400.00   \n",
       "mean     0.46        36.10    39.74     671.75            3.40       11.47   \n",
       "std      0.50        33.01    16.33     468.54            1.54        5.85   \n",
       "min      0.00         0.04     2.35       4.00            1.00        1.00   \n",
       "25%      0.00        10.08    27.86     286.25            2.00        6.00   \n",
       "50%      0.00        24.74    39.94     588.50            3.00       12.00   \n",
       "75%      1.00        57.24    51.69    1025.50            5.00       17.00   \n",
       "max      1.00       145.16    74.97    1950.00            6.00       21.00   \n",
       "\n",
       "       Industry  \n",
       "count    400.00  \n",
       "mean       0.60  \n",
       "std        0.49  \n",
       "min        0.00  \n",
       "25%        0.00  \n",
       "50%        1.00  \n",
       "75%        1.00  \n",
       "max        1.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tG5GRkVRVVXH8+PHLrwVyE/FZs2Zx5MgR1q5dywMPPBCw64hYKaQLrOrCM9RX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1942bdf6048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_histograms\n",
    "\n",
    "plot_histograms(train[predictors[:-1]]) # excludes the last variable, since it is binary\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kj3wmQa3ht4XlLRQqZFI2tmni+QFeEJJLJ2h4AauVOp6nRGkzV+3Q/h2XVSPc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\n3rhl7SdXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1942c767fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_logistic_regressions\n",
    "\n",
    "with sns.color_palette(crayon):\n",
    "    plot_logistic_regressions(train[predictors], train[response])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6NCEaZ+vAZK8RCK9OENJXJRBms33OKYQTSQKBfaYwAQT5eTE0OpCdaQUUnZR2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\nKawJQgjVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1942cc4d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_conditional_distributions\n",
    "\n",
    "plot_conditional_distributions(train[predictors[:-1]], y_train, labels=['Retention', 'Churn'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Industry</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.597</td>\n",
       "      <td>0.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.403</td>\n",
       "      <td>0.506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Industry      0      1\n",
       "Churn                 \n",
       "0         0.597  0.494\n",
       "1         0.403  0.506"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table=pd.crosstab(train[response], train['Industry'])\n",
    "table = (table/table.sum()).round(3)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1942c9f0978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "(table.T).plot(kind='bar', alpha=0.8, ax=ax)\n",
    "ax.set_xlabel('Industry')\n",
    "ax.set_ylabel('Proportions')\n",
    "ax.legend_.set_title('Churn')\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "tree = DecisionTreeClassifier(criterion='entropy', max_depth=2, min_samples_leaf=5)\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\r\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n",
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       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\r\n",
       " -->\r\n",
       "<!-- Title: Tree Pages: 1 -->\r\n",
       "<svg width=\"577pt\" height=\"269pt\"\r\n",
       " viewBox=\"0.00 0.00 577.00 269.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 265)\">\r\n",
       "<title>Tree</title>\r\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-265 573,-265 573,4 -4,4\"/>\r\n",
       "<!-- 0 -->\r\n",
       "<g id=\"node1\" class=\"node\"><title>0</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"290\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Avg_Ret_Exp &lt;= 66.77</text>\r\n",
       "<text text-anchor=\"middle\" x=\"290\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 400</text>\r\n",
       "<text text-anchor=\"middle\" x=\"290\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [214, 186]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"290\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = not acquired</text>\r\n",
       "</g>\r\n",
       "<!-- 1 -->\r\n",
       "<g id=\"node2\" class=\"node\"><title>1</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M266.5,-157C266.5,-157 141.5,-157 141.5,-157 135.5,-157 129.5,-151 129.5,-145 129.5,-145 129.5,-101 129.5,-101 129.5,-95 135.5,-89 141.5,-89 141.5,-89 266.5,-89 266.5,-89 272.5,-89 278.5,-95 278.5,-101 278.5,-101 278.5,-145 278.5,-145 278.5,-151 272.5,-157 266.5,-157\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"204\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Employees &lt;= 395.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 323</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [209, 114]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"204\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = not acquired</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;1 -->\r\n",
       "<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M262.078,-192.884C254.568,-183.976 246.355,-174.235 238.532,-164.957\"/>\r\n",
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       "<g id=\"node5\" class=\"node\"><title>4</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M443,-157C443,-157 309,-157 309,-157 303,-157 297,-151 297,-145 297,-145 297,-101 297,-101 297,-95 303,-89 309,-89 309,-89 443,-89 443,-89 449,-89 455,-95 455,-101 455,-101 455,-145 455,-145 455,-151 449,-157 443,-157\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Employees &lt;= 1225.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 77</text>\r\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 72]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"376\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = acquired</text>\r\n",
       "</g>\r\n",
       "<!-- 0&#45;&gt;4 -->\r\n",
       "<g id=\"edge4\" class=\"edge\"><title>0&#45;&gt;4</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M317.922,-192.884C325.432,-183.976 333.645,-174.235 341.468,-164.957\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"350.044\" y=\"-178.509\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"60\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 132</text>\r\n",
       "<text text-anchor=\"middle\" x=\"60\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [51, 81]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"60\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = acquired</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;2 -->\r\n",
       "<g id=\"edge2\" class=\"edge\"><title>1&#45;&gt;2</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"210\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 191</text>\r\n",
       "<text text-anchor=\"middle\" x=\"210\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [158, 33]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"210\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = not acquired</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;3 -->\r\n",
       "<g id=\"edge3\" class=\"edge\"><title>1&#45;&gt;3</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"371\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 64</text>\r\n",
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       "</g>\r\n",
       "<!-- 4&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\"><title>4&#45;&gt;5</title>\r\n",
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       "</g>\r\n",
       "<!-- 6 -->\r\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M557,-53C557,-53 461,-53 461,-53 455,-53 449,-47 449,-41 449,-41 449,-12 449,-12 449,-6 455,-0 461,-0 461,-0 557,-0 557,-0 563,-0 569,-6 569,-12 569,-12 569,-41 569,-41 569,-47 563,-53 557,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"509\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13</text>\r\n",
       "<text text-anchor=\"middle\" x=\"509\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [5, 8]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"509\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = acquired</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\"><title>4&#45;&gt;6</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M422.517,-88.9485C436.164,-79.2516 451.05,-68.6752 464.564,-59.073\"/>\r\n",
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       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1942c2d8be0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "dot_data = export_graphviz(tree, out_file=None, feature_names=predictors, impurity=False,\n",
    "                           class_names=['not acquired','acquired'], rounded=True) \n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render('tree01') # saves tree to a file\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found by grid search: {'min_samples_leaf': 40} \n",
      "\n",
      "Wall time: 176 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model = DecisionTreeClassifier(criterion='entropy')\n",
    "\n",
    "tuning_parameters = {\n",
    "    'min_samples_leaf': [1,5,10,20,30,40,50],\n",
    "}\n",
    "\n",
    "tree_search = GridSearchCV(model, tuning_parameters, cv= 5 , return_train_score=False)\n",
    "tree_search.fit(X_train, y_train)\n",
    "\n",
    "tree = tree_search.best_estimator_\n",
    "\n",
    "print('Best parameters found by grid search:', tree_search.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "<title>Tree</title>\r\n",
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       "<!-- 0 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"341.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Avg_Ret_Exp &lt;= 66.77</text>\r\n",
       "<text text-anchor=\"middle\" x=\"341.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 400</text>\r\n",
       "<text text-anchor=\"middle\" x=\"341.5\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [214, 186]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"341.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = retention</text>\r\n",
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       "<!-- 1 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"269.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 323</text>\r\n",
       "<text text-anchor=\"middle\" x=\"269.5\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [209, 114]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"269.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = retention</text>\r\n",
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       "<!-- 0&#45;&gt;1 -->\r\n",
       "<g id=\"edge1\" class=\"edge\"><title>0&#45;&gt;1</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"414.5\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = churn</text>\r\n",
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       "<!-- 0&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>0&#45;&gt;10</title>\r\n",
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       "<!-- 2 -->\r\n",
       "<g id=\"node3\" class=\"node\"><title>2</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"180.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Total_Crossbuy &lt;= 3.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"180.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 132</text>\r\n",
       "<text text-anchor=\"middle\" x=\"180.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [51, 81]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"180.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = churn</text>\r\n",
       "</g>\r\n",
       "<!-- 1&#45;&gt;2 -->\r\n",
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       "<!-- 5 -->\r\n",
       "<g id=\"node6\" class=\"node\"><title>5</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M428,-261C428,-261 291,-261 291,-261 285,-261 279,-255 279,-249 279,-249 279,-205 279,-205 279,-199 285,-193 291,-193 291,-193 428,-193 428,-193 434,-193 440,-199 440,-205 440,-205 440,-249 440,-249 440,-255 434,-261 428,-261\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"359.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Total_Crossbuy &lt;= 2.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"359.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [158, 33]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"359.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = retention</text>\r\n",
       "</g>\r\n",
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       "<!-- 3 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"56.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 70</text>\r\n",
       "<text text-anchor=\"middle\" x=\"56.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [11, 59]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"56.5\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = churn</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"191.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 62</text>\r\n",
       "<text text-anchor=\"middle\" x=\"191.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [40, 22]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"349.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 64</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"502.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Employees &lt;= 752.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [41, 7]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"433.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = retention</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>7&#45;&gt;8</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M478.367,-88.9485C471.888,-80.0749 464.871,-70.4648 458.354,-61.5388\"/>\r\n",
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       "</g>\r\n",
       "<!-- 9 -->\r\n",
       "<g id=\"node10\" class=\"node\"><title>9</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M619.5,-53C619.5,-53 523.5,-53 523.5,-53 517.5,-53 511.5,-47 511.5,-41 511.5,-41 511.5,-12 511.5,-12 511.5,-6 517.5,-0 523.5,-0 523.5,-0 619.5,-0 619.5,-0 625.5,-0 631.5,-6 631.5,-12 631.5,-12 631.5,-41 631.5,-41 631.5,-47 625.5,-53 619.5,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"571.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 79</text>\r\n",
       "<text text-anchor=\"middle\" x=\"571.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [77, 2]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"571.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = retention</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>7&#45;&gt;9</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M526.633,-88.9485C533.112,-80.0749 540.129,-70.4648 546.646,-61.5388\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"549.638,-63.3769 552.708,-53.2367 543.984,-59.249 549.638,-63.3769\"/>\r\n",
       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1942c716d68>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = export_graphviz(tree, out_file=None, feature_names=predictors, impurity=False,\n",
    "                           class_names=['retention','churn'], rounded=True) \n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render('tree02') # saves tree to a file\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=1000, n_jobs=1, oob_score=False,\n",
       "         random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "bag = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'), n_estimators=1000, random_state=1)\n",
    "bag.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "\n",
    "The syntax to fit a [random forest classifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) is the following. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features=2, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=1,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import  RandomForestClassifier\n",
    "rf = RandomForestClassifier(criterion='entropy', max_features= 2, min_samples_leaf=5, n_estimators=1000, random_state=1)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To tune the random forest, we should select a parameter that controls the size of the trees (such as the minimum number of observations in a terminal node) and the number of predictors that are sampled as candidate split variables at each node of a tree.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found by randomised search: {'min_samples_leaf': 1, 'max_features': 6} \n",
      "\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model = RandomForestClassifier(criterion = 'entropy',  n_estimators=1000)\n",
    "\n",
    "tuning_parameters = {\n",
    "    'min_samples_leaf': [1, 5, 10, 20, 50],\n",
    "    'max_features': np.arange(1, len(predictors)+1),\n",
    "}\n",
    "\n",
    "rf_search = RandomizedSearchCV(model, tuning_parameters, cv = 5, n_iter= 16, return_train_score=False, n_jobs=4)\n",
    "rf_search.fit(X_train, y_train)\n",
    "\n",
    "rf = rf_search.best_estimator_\n",
    "\n",
    "print('Best parameters found by randomised search:', rf_search.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After tuning the random forest, we may want to increase the number of trees to improve accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features=6, 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=10000, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.n_estimators = 10000\n",
    "rf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiQVZsCSJEkqzIAlSZJUmAFLkiSpMAOWJElSYQYsSZKkwgxYkiRJhY2qzyIcTRZcNJNW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\nmAFLkiSpsP8D1hp1jKiZfk0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19432645048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_feature_importance\n",
    "\n",
    "plot_feature_importance(rf, predictors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extremely Randomised Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters found by randomised search: {'min_samples_leaf': 1, 'max_features': 4} \n",
      "\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model = ExtraTreesClassifier(criterion = 'entropy',  n_estimators=1000)\n",
    "\n",
    "tuning_parameters = {\n",
    "    'min_samples_leaf': [1, 5, 10, 20, 50],\n",
    "    'max_features': np.arange(1, len(predictors)+1),\n",
    "}\n",
    "\n",
    "xtrees_search = RandomizedSearchCV(model, tuning_parameters, cv = 5, n_iter= 16, \n",
    "                                 return_train_score=False, n_jobs=4)\n",
    "xtrees_search.fit(X_train, y_train)\n",
    "\n",
    "xtrees = xtrees_search.best_estimator_\n",
    "\n",
    "print('Best parameters found by randomised search:', xtrees_search.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='entropy',\n",
       "           max_depth=None, max_features=4, 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=10000, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrees.n_estimators = 10000\n",
    "xtrees.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000.0, class_weight=None, dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit = LogisticRegression(C=1e3)\n",
    "logit.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Error rate</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic</th>\n",
       "      <td>0.175</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.225</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.822</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagged trees</th>\n",
       "      <td>0.162</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random forest</th>\n",
       "      <td>0.157</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Trees</th>\n",
       "      <td>0.155</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Error rate  Sensitivity  Specificity    AUC  Precision\n",
       "Logistic            0.175        0.796        0.850  0.916      0.822\n",
       "Decision Tree       0.225        0.720        0.822  0.847      0.779\n",
       "Bagged trees        0.162        0.812        0.860  0.926      0.834\n",
       "Random forest       0.157        0.812        0.869  0.925      0.844\n",
       "Extra Trees         0.155        0.801        0.883  0.934      0.856"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns=['Error rate', 'Sensitivity', 'Specificity', 'AUC', 'Precision']\n",
    "rows=['Logistic', 'Decision Tree', 'Bagged trees', 'Random forest', 'Extra Trees']\n",
    "results=pd.DataFrame(0.0, columns=columns, index=rows) \n",
    "\n",
    "methods=[logit, tree, bag, rf, xtrees]\n",
    "\n",
    "for i, method in enumerate(methods):\n",
    "    \n",
    "    y_prob = cross_val_predict(method, X_train, y_train, cv=10, method='predict_proba')\n",
    "    y_pred = (y_prob[:,1] > 0.5).astype(int)\n",
    "\n",
    "    confusion  = confusion_matrix(y_train, y_pred) \n",
    "\n",
    "    results.iloc[i,0]=  1 - accuracy_score(y_train, y_pred)\n",
    "    results.iloc[i,1]=  confusion[1,1]/np.sum(confusion[1,:])\n",
    "    results.iloc[i,2]=  confusion[0,0]/np.sum(confusion[0,:])\n",
    "    results.iloc[i,3]=  roc_auc_score(y_train, y_prob[:,1])\n",
    "    results.iloc[i,4]=  precision_score(y_train, y_pred)\n",
    "\n",
    "results.round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Error rate</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Logistic</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagged trees</th>\n",
       "      <td>0.21</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random forest</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extra Trees</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Error rate  Sensitivity  Specificity    AUC  Precision\n",
       "Logistic             0.24        0.783        0.741  0.841      0.720\n",
       "Decision Tree        0.26        0.609        0.852  0.815      0.778\n",
       "Bagged trees         0.21        0.848        0.741  0.880      0.736\n",
       "Random forest        0.22        0.848        0.722  0.882      0.722\n",
       "Extra Trees          0.22        0.848        0.722  0.849      0.722"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns=['Error rate', 'Sensitivity', 'Specificity', 'AUC', 'Precision']\n",
    "rows=['Logistic', 'Decision Tree', 'Bagged trees', 'Random forest', 'Extra Trees']\n",
    "results=pd.DataFrame(0.0, columns=columns, index=rows) \n",
    "\n",
    "methods=[logit, tree, bag, rf, xtrees]\n",
    "\n",
    "y_prob = np.zeros((len(test), len(rows)))\n",
    "\n",
    "for i, method in enumerate(methods):\n",
    "    \n",
    "    y_pred = method.predict(X_test)\n",
    "    y_prob[:, i] = method.predict_proba(X_test)[:,1]\n",
    "\n",
    "    confusion  = confusion_matrix(y_test, y_pred) \n",
    "\n",
    "    results.iloc[i,0]=  1 - accuracy_score(y_test, y_pred)\n",
    "    results.iloc[i,1]=  confusion[1,1]/np.sum(confusion[1,:])\n",
    "    results.iloc[i,2]=  confusion[0,0]/np.sum(confusion[0,:])\n",
    "    results.iloc[i,3]=  roc_auc_score(y_test, y_prob[:,i])\n",
    "    results.iloc[i,4]=  precision_score(y_test, y_pred)\n",
    "\n",
    "results.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DihkTGIbB3//+dzZt2sTs2bOZN28e3377LUOGDAnLicyiYjKsdqxVm2Ct2gS4NvfxgNeN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\ngxkzMzOrNQczZmZmVmsOZszMzKzW/gGr1KqIWJbDZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1943d815b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statlearning import plot_roc_curves\n",
    "\n",
    "with sns.color_palette(crayon):\n",
    "    fig, ax = plot_roc_curves(y_test, y_prob[:,[0,1,3,4]], labels=pd.Series(rows).iloc[[0,1,3,4]])\n",
    "    plt.show()"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}