churn_feature_ranking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Ranking\n",
    "\n",
    "Feature selection is a very important part of churn analysis because management need to identify pain points, and come up with strategy for customer retension. However, as we can see in this notebook, there is no one size fit all method for feature ranking. Algorithm like XGBoostClassifier has a feature importance method, but its ranking can often be skewed by features that have been used with high frequency. Here, I will compare the result of linear method (Logistic Regression), stability method (Randomized Logistic Regression), recursive feature elimination (with SVM and Logistic Regression), and tree method (Decision Tree and Random Forest).\n",
    "\n",
    "One major caveat here is that our data contain a categorical entry---state and area code, that can drastically increase the dimension of our data, once we apply One-Hot-Encoding, in order to feed the data into linear model. Such encoding also skews the result (to almost zero importance) for tree method because it reduces the usage of each resulting new features from One-Hot-Encoding. Luckily, in this dataset, the feature \"state\" and \"area code\" do not have a big enough contribution to the classification task. Thus, for the sake of simplicity, I will drop them in the subsequent discussion when making the feature ranking matrix. \n",
    "\n",
    "**Disclaimer**: For the feature ranking matrix, I use with modification some of the codes from fellow Kaggler Anisotropic's [kernel](https://www.kaggle.com/arthurtok/feature-ranking-rfe-random-forest-linear-models). However, his discussion is on regression model that does not contain any categorical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "color = sns.color_palette()\n",
    "pd.set_option('display.max_columns',100)\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, scale, StandardScaler, LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.feature_selection import RFE, f_classif\n",
    "from sklearn.linear_model import RandomizedLogisticRegression\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function\n",
    "from sklearn.pipeline import TransformerMixin, make_pipeline\n",
    "\n",
    "class Drop_columns(TransformerMixin):\n",
    "    \n",
    "    def __init__(self, columns=None):\n",
    "        self.columns = columns\n",
    "        \n",
    "    def transform(self, X, y=None, **kwargs):\n",
    "        try:\n",
    "            return X.drop(self.columns, axis=1)\n",
    "        except ValueError:\n",
    "            print ('No such columns!')\n",
    "            \n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        return self\n",
    "\n",
    "class DummyEncoder(TransformerMixin):\n",
    "\n",
    "    def __init__(self, columns=None):\n",
    "        self.columns = columns\n",
    "\n",
    "    def transform(self, X, y=None, **kwargs):\n",
    "        return pd.get_dummies(X, columns=self.columns, drop_first=True)\n",
    "\n",
    "    def fit(self, X, y=None, **kwargs):\n",
    "        return self\n",
    "    \n",
    "class Encoder(TransformerMixin):\n",
    "    \n",
    "    def __init__(self, columns=None):        \n",
    "        self.columns = columns\n",
    "    \n",
    "    def transform(self, X, y=None, **kwargs):       \n",
    "        le = LabelEncoder()\n",
    "        for col in self.columns:\n",
    "            X[col] = le.fit_transform(X[col])\n",
    "        return X\n",
    "    \n",
    "    def fit(self, X, y=None, **kwargs):        \n",
    "        return self\n",
    "\n",
    "def display_result(X_train, X_test, y_train, y_test, clf, display_roc=True):\n",
    "    \n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    \n",
    "    print (classification_report(y_test, y_pred))\n",
    "\n",
    "\n",
    "    matrix = pd.DataFrame(\n",
    "           confusion_matrix(y_test,y_pred),\n",
    "            columns=['Predicted False', 'Predict True'],\n",
    "            index = ['Actual False', 'Actual True'])\n",
    "    display(matrix)\n",
    "    \n",
    "    fpr, tpr, thresholds = roc_curve(y_test, clf.predict_proba(X_test)[:,1])\n",
    "    if display_roc:\n",
    "        \n",
    "        plt.plot(fpr, tpr,label='%s (area = %0.2f)' % ('LogisticRegression', roc_auc_score(y_test, y_pred)))\n",
    "        # Plot Base Rate ROC\n",
    "        plt.plot([0,1], [0,1],label='Base Rate' 'k--')\n",
    "\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.05])\n",
    "        plt.xlabel('False Positive Rate')\n",
    "        plt.ylabel('True Positive Rate')\n",
    "        plt.title('ROC Graph')\n",
    "        plt.legend(loc=\"lower right\")\n",
    "        plt.show()\n",
    "        return\n",
    "    return fpr, tpr, thresholds, y_pred\n",
    "\n",
    "\n",
    "def read_data(onehot=False, label=False, return_cat_columns=False):\n",
    "    df = pd.read_csv('churn.csv')\n",
    "    df.columns = df.columns.str.lower().str.replace(' ','_')\n",
    "    df.rename(columns={'churn?':'churn', \"int'l_plan\":'intl_plan'}, inplace=True)\n",
    "    df = (df.drop(['phone','day_charge','eve_charge','night_charge','intl_charge'], axis=1)\n",
    "            .replace(['True.','False.'],[1,0]))\n",
    "    df.area_code = df.area_code.astype('object')\n",
    "    if label:\n",
    "        df = df.replace(['yes','no'],[1,0])\n",
    "    if onehot:\n",
    "        df = pd.get_dummies(df, columns=['state','area_code'], drop_first=True)\n",
    "    if return_cat_columns:\n",
    "        return df, df.dtypes[df.dtypes==object].index.values.tolist()\n",
    "    else:\n",
    "        return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Analyzing the State Feature\n",
    "\n",
    "Since the feature \"state\" presents an unnecessary complication to our feature ranking task, let's first check just how much influence it has on our binary classification task. For simplicity, we can see how much does our prediction result differs with or without such feature. Here, we also use the sklearn's pipeline tools to help streamline our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, classification_report, confusion_matrix\n",
    "\n",
    "df = read_data()\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop('churn', axis=1), df.churn.values, test_size=0.2,\n",
    "                                                   random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.96      0.60      0.74       579\n",
      "          1       0.24      0.83      0.37        88\n",
      "\n",
      "avg / total       0.86      0.63      0.69       667\n",
      "\n"
     ]
    },
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       "              Predicted False  Predict True\n",
       "Actual False              350           229\n",
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     "name": "stdout",
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     "text": [
      "Linear Regression with State Column Dropped\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.63      0.76       579\n",
      "          1       0.27      0.89      0.41        88\n",
      "\n",
      "avg / total       0.88      0.66      0.72       667\n",
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       "              Predicted False  Predict True\n",
       "Actual False              364           215\n",
       "Actual True                10            78"
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Rl/AuTkfzWRQkAAAAoIEpLS/V6l2rlZSWpKP5RzW83XDNGTxH0a2jnY7m8yhIANAQeXKQ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\n7ViaOod11nPXPacbO9+oABP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uCAC/t+vkLiWkJOjTjE/VKqSV/ueq/9Fdl9+l4MBgp6MB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\nSZIkqTMgSZIkSVL3C7N5PcYx5ZNkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d272de518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "\n",
    "lr = make_pipeline(DummyEncoder(), StandardScaler(), LogisticRegression(fit_intercept=False))\n",
    "lrd = make_pipeline(Drop_columns(columns=['state','area_code']),\n",
    "                    Encoder(columns=['intl_plan', 'vmail_plan']),\n",
    "                    StandardScaler(),\n",
    "                    LogisticRegression(fit_intercept=False))\n",
    "model = [('Linear Regression', lr), ('Linear Regression with State Column Dropped', lrd)]\n",
    "\n",
    "for model_name, clf in model:\n",
    "    print (model_name)\n",
    "    print ()\n",
    "    fpr, tpr, thresholds, y_pred = display_result(X_train, X_test, y_train, y_test, clf, display_roc=False)\n",
    "    plt.plot(fpr, tpr,label='%s (area = %0.2f)' % (model_name, roc_auc_score(y_test, y_pred)))\n",
    "    \n",
    "\n",
    "# Plot Base Rate ROC\n",
    "plt.plot([0,1], [0,1],label='Base Rate' 'k--')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Graph')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is interesting, dropping the columns \"state\" does make a difference to our prediction result, but for the better! We can speculate that such feature may contain mostly noise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_data(label=True)\n",
    "y = df.churn.values\n",
    "X = df.drop(['churn','state','area_code'], axis=1)\n",
    "colnames = X.columns\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dictionary to store our rankings\n",
    "ranks = {}\n",
    "\n",
    "# Create our function which stores the feature rankings to the ranks dictionary\n",
    "def ranking(ranks, names, order=1):\n",
    "    minmax = MinMaxScaler()\n",
    "    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]\n",
    "    ranks = map(lambda x: round(x,2), ranks)\n",
    "    return list(zip(names, ranks))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression(fit_intercept=False)\n",
    "lr.fit(X, y)\n",
    "ranks['Logistic'] = ranking(lr.coef_[0], colnames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stability Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished!\n"
     ]
    }
   ],
   "source": [
    "rlogit = RandomizedLogisticRegression()\n",
    "rlogit.fit(X, y)\n",
    "ranks[\"rlogit/stability\"] = ranking(np.abs(rlogit.scores_), colnames)\n",
    "print('finished!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Feature Elimination Method\n",
    "\n",
    "Here we use both Support Vector Machine (SVM) and Logistic Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished!\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(kernel='linear')\n",
    "rfec = RFE(svc, n_features_to_select=1)\n",
    "rfec.fit(X, y)\n",
    "ranks['RFE_SVC'] = ranking(list(map(float, rfec.ranking_)), colnames, order=-1)\n",
    "print ('finished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished!\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(fit_intercept=False)\n",
    "rfec = RFE(lr, n_features_to_select=1)\n",
    "rfec.fit(X, y)\n",
    "ranks['RFE_Logit'] = ranking(list(map(float, rfec.ranking_)), colnames, order=-1)\n",
    "print ('finished!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tree Method\n",
    "\n",
    "We use both Decision Tree Classifier and Random Forest Classifier here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X,y)\n",
    "ranks['DTree'] = ranking(dt.feature_importances_, colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X, y)\n",
    "ranks['RForest'] = ranking(rf.feature_importances_, colnames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Logistic</th>\n",
       "      <th>rlogit/stability</th>\n",
       "      <th>RFE_SVC</th>\n",
       "      <th>RFE_Logit</th>\n",
       "      <th>DTree</th>\n",
       "      <th>RForest</th>\n",
       "      <th>Mean Ranking</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</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>account_length</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intl_plan</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vmail_plan</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vmail_message</th>\n",
       "      <td>0.54</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_mins</th>\n",
       "      <td>0.83</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_calls</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eve_mins</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eve_calls</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>night_mins</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>night_calls</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intl_mins</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intl_calls</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>custserv_calls</th>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Logistic  rlogit/stability  RFE_SVC  RFE_Logit  DTree  \\\n",
       "feature                                                                 \n",
       "account_length      0.41              0.00     0.75       0.17   0.07   \n",
       "intl_plan           1.00              1.00     0.42       1.00   0.22   \n",
       "vmail_plan          0.00              0.46     1.00       0.75   0.17   \n",
       "vmail_message       0.54              0.24     0.92       0.58   0.00   \n",
       "day_mins            0.83              1.00     0.83       0.83   1.00   \n",
       "day_calls           0.42              0.00     0.00       0.25   0.03   \n",
       "eve_mins            0.60              0.55     0.17       0.67   0.49   \n",
       "eve_calls           0.39              0.00     0.33       0.00   0.05   \n",
       "night_mins          0.48              0.00     0.08       0.33   0.13   \n",
       "night_calls         0.39              0.00     0.25       0.08   0.04   \n",
       "intl_mins           0.53              0.18     0.50       0.50   0.35   \n",
       "intl_calls          0.25              0.05     0.58       0.42   0.21   \n",
       "custserv_calls      0.88              1.00     0.67       0.92   0.39   \n",
       "\n",
       "                RForest  Mean Ranking  \n",
       "feature                                \n",
       "account_length     0.13          0.25  \n",
       "intl_plan          0.27          0.65  \n",
       "vmail_plan         0.00          0.40  \n",
       "vmail_message      0.05          0.39  \n",
       "day_mins           1.00          0.92  \n",
       "day_calls          0.13          0.14  \n",
       "eve_mins           0.40          0.48  \n",
       "eve_calls          0.10          0.14  \n",
       "night_mins         0.17          0.20  \n",
       "night_calls        0.17          0.16  \n",
       "intl_mins          0.27          0.39  \n",
       "intl_calls         0.19          0.28  \n",
       "custserv_calls     0.41          0.71  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = pd.DataFrame(colnames, columns=['feature'])\n",
    "for name in ranks.keys():\n",
    "    tmp = pd.DataFrame(ranks[name], columns=['feature',name])\n",
    "    r = pd.merge(r,tmp, how='inner', on='feature')\n",
    "    \n",
    "r['Mean Ranking'] = round(r.mean(axis=1),2)\n",
    "\n",
    "r.set_index('feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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060kOSXJF26sX75OVq6b+fdvLk5yQld9+uKfH\nHkl+I8kLxhifTfKqJFdlJaJdshv7+ViSpyb587bHr3Oz30ly57YfWcxwdZLP7cb4AMAG15V/qwAA\nwHK1vV2SQ8YYX13Er3cluc8Y45+XPBoAcJDwPAIAAA4Wd0jy120PycrzwJ4jYgEAq7kiCwAAAIAp\neEYWAAAAAFMQsgAAAACYgpAFAAAAwBSELAAAAACmIGQBAAAAMIX/H3ufdWtSgdM6AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d1e82c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the ranking of the features\n",
    "sns.factorplot(x=\"Mean Ranking\", y=\"feature\", data = r.sort_values(by='Mean Ranking',ascending=False), kind=\"bar\", \n",
    "               size=14, aspect=1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall from the previous notebook that we notice four features standout: vmail_plan, intl_plan, day_mins, custserv_call. This conclusion almost match what we have found here, with the exception of eve_mins. Thus, with some degree of confident, we can say that day_mins, custserv_calls, intl_plan are the top three reason customer leave.\n",
    "\n",
    "It worths pointing out the result of tree method differ a bit with the linear method, while intl_plan ranks on the top for Logistic Regression, but it ranks low for both Decision Tree and Random Forest.\n",
    "\n",
    "In fact, such disparity is also true for XGBoost Classifier. It has to with how feature importance is calculated for the tree method. Even though one features play important role at splitting the tree early on, but it may not be used as much on other nodes, so it has less of a aggregate effect, hence, lower feature importance.\n",
    "\n",
    "We can demonstrate this effect with the following chart, using XGBoost's plot_tree method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yGq0VrVQnGsabHnNWa/rlVYPajNe4I+sbeoZ0LuqtNQ+VnldPXhNL5cx+fZvZ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\nXi21olnVtta6pl+r1KBm9Rq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JSUlIRrxxxzDJCsQQE6d+6cxohS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\nhg0bhiz+/zN7784BBxwAwK9+9asKj5c2y9y5Sd+tW1SPOCKqDz+c/jySJ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TJ+662obtiw+Z9Xo0ZU9947yRYvjmrjxpv/uZL0Q+syizTje7cAN9xwQ5Hsmmuu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\nKldyCgsLy7qHjSn3DUqSB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SJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJEmSJElKG1R2AyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkqS0wWU3QJLU25w5c+L10KFDS2xJ93vnnXfi9fnnnw/AM888E8v+/ve/AzBy\n5MhYdt555wGwwQYbxLKbbroJgIMPPjiWzZgxA4ATTjghlk2cOLGiDZdccgkA3/zmN3v9P8BBBx2U\nazuL8u677wJw8803x7Lbb78dgOeffz6WHXnkkQAcdthhsWzllVcG4Ic//GEsmzVrFgBHH310LHvo\noYcAWGuttQC49tpr42vrrrtuRZvmzZsHwM9//vOKNj377LOx7N577wXglltuqfi6X//617HsT3/6\nE9D7d3zXXXcBsM466wDwgx/8IL5W7b4/+uijAHzrW9+KZZ/4xCeA3n/355xzDgAzZ86MZUsuuWRF\nfUH6e2+44QYAzjzzzFgWPiv77LMPAFdddVV8bY011qiob8stt+z3Zy1MaMsnP/nJhusI3nrrrXj9\nuc99Dkg+6wBTpkwBkt8DJO/7+OOPj2UnnXRSRd2Nvsf0va72HqdPn95v2QorrJBZ95gxY4Def++v\nvvpqr+9Nf+6DFVdcMV5vuummQPJZA1h//fUBOP3002PZNttsU1FPVt2h3nTdtdarfA0bNgyA2bNn\nl9yS9mP81FrhWdUu8VO6zPip//gJkud9u8RPkMRQnRw/tYs84jjIN84JMQ4kf5chxkl/b95xzuTJ\nkyvqW2mllfptZ1r4G0v761//CmTfB6jtXmTFe8qf8VP36xuXQPIcT48hQ5+f1d8/+eST8fqYY44B\nevdZL774Yq//Alx44YUAbLjhhrHstttu6/VfgPfeew+Af/3rX7Hs61//eq+ff+yxx8br999/v6J9\nzRg+fDgAq666aixr9FmZ9ZxsJ0XOK9TrjjvuAHrf33HjxjVc3/z584HebQvxUIiHmxHaC0mba23v\nRRddFK933313AEaMGNF0m5SPEPM5bi5GGFNBMq4KYyVIxlVhTAXJuCr9dWFcFcZUkIyr0s+srHnp\na665BugdP4e/xXT/c+mll9b47lrH9Yb2XW9Iz6t02ni5qDEiJHFjvRY2Dg+xTIhjIIllQhwDyWcn\nPa8RYplqcUxR9VZjXNDeHC9L7c+1r9ZZsGBBvC4qRq81przvvvv6bV+Y1wL4wx/+APTuW6+++mqg\n92enmvT77SvMex1wwAGxLMQIa6+9diwL7/FjH/tYLGuXcUgYU0Ayrkj/PsO4IowpIBlXpGO/MK7I\ncw0O8h0v5rEGB8m4IowpIPkchTEFJOOKPNfgIBlXtPOYAvIdVzQzn9rM3F0r6w1zmJCMIdKfp6x5\nzEZzmCBZNzSHqRyOMzqXc8flSvefIU8qnYcQ1jnTX5eOefqT19rnddddB/R+docY6Xvf+14smzBh\nAgCDBydb1H784x8DSd9/xRVXxNf23nvvhb6HRtQbD7YyJwtqiwdbmZMFtcWDReVkQXHxYJHzwkXL\ne325VfWm686j3kaEsfNjjz0Wy4444gig97MtjKHDs6vavHceeWKQnTOn1knHOcarxah2X40vW6eo\nmBKSuLJdYkpI4sp2iSmhco6x7JgSKucYy44poZw5xnaMC/PIm693b2Ct9abrrjcHs5n8grLUGiO3\nMtdCzasWgxgD5iv9rA7zwspXuMfpfZUhvkvvhQxfV29sB5XxXTqmC9chtoMkvguxHSTxXYh3IInv\nQmwHSXwXYjto7ZxhEPZ0QLIX9R//+EcsC+vb6Wf7l770JSCJ5QYNGhRf6xsrQ/33s5WKit/S31v2\nnGBRijzfIQj5liHXElqTb9k3HsprXq/T5jiVrVp/b3wlSRqoXA9pHcfG+cpzz2b6vYbxclj3gHL2\nbDaT559W9J7NMG6G5vZsFnV+YlGycoqr5RN34l7QoqX3koScmJAPA8nzKeTDQHZOjPKVtS7RzXGC\nucetk3WWYta5SpD0d+G8R0j6ynDeIyRnPmad9wi1nZld7/lP6XYWpai9ZtB4PNROZ5MErVxHgNri\noU5ZR8hLHmsFRZ2J3cqzF1t5RkUz+9+UL3M92pvnPNam2jmPjWqnvMd2akstijx7sm9/WOT+kqJy\nCMrITWinPUdqb/Y3tfFc4Xyf7X1zxMs+rxiyn5WtyGmv5ayPZvLkNDDlMceZlkf+bvrZHq5recZD\n8pwv6hkP3TeuKEpRz/F22UNUa/8wEH7X6k55/3tiRfcPkPQRWf0D5NtHdHP/0MqxzEBcgyny3y6r\npa9sZixlnozqNWjhXyJJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiS1zuCyGyBJ6m327NnxeujQoSW2pHstWLAAgEMPPTSWHXfccQCss846FV//H//xH/H6M5/5\nDABPPfVULNt1110B+Ne//hVyH+FYAAAgAElEQVTLDj/8cAA23XTTzLZsv/32AOy+++4AHHTQQYW1\nc6mllspsS6OGDx8O9H6ve+65JwAjR46s+Pn33HNPLBs3bhwAO+ywQyw78MADAbjpppti2XPPPQfA\nRhttBMARRxwRX7vjjjsq2rTIIosAyX2A5B6vvfbaFV+/ySabxOs99tgDgHfeeSeWXXbZZQCcf/75\nsWzy5Mm9vv7rX/96fO3++++v+BnVPieh7T09PbEs/M7ef//9WLbkkksC8Pbbb8eyyy+/HIDzzjsv\nloU2H3LIIbFs/PjxACy33HIVbcpD+r3OmjULgFNPPbXpetOfnXPPPbfi9ZkzZwJw0UUXxbJTTjkF\ngJNPPjmWLb/88kDyuWpEeI/h/UH197juuusC8PDDD8eyO++8E4C99tor82cMGTKkomzevHlV25GW\nfhaENj3//POxLHzutttuu1j2l7/8BYANNtigprrT7zXUXWu9yleIC9Kxgj5k/FS8EJdAEpu0S/wE\nSQxl/NR//ARJDNUu8RMkMdRAip/aSdFxTi0xTrodac3EOa+88kpFfaNGjeq3nWlLL710Rdmzzz4L\nJPcBknsR7gPkcy+UL+On7pQVl0B2n5/V36f71vD3efPNN8eyuXPnAjB27NhY9pWvfAWARx99NJaF\netL1hX4x3bdceumlvdp49tlnx+sJEyZUvIdmbLHFFkDSF0Pjz8qs52Q7aad5hRtvvBFI+q5GpD+L\nIZaZNGlSLFt11VWB5O/j4IMPjq+l46ZahPZC7W2eMmUKAB988EEsW1hcr9YL/eGiiy5acku6UxhT\nQdLfpMetWeOq9BgpjFFC3wHJuCr9HM+alz7hhBMAOPHEE+NroQ/q2/+0G9cb2ne9IYyVofPGy0WN\nERtR6zg8fN6z4hhIYpkQx0ASy1SLY/Kst2/dgXFBZwjj5Tlz5pTcEkl9hTmJ9Nyha1+tU1SMXm9M\nmXbhhRcCveeLXn/9dQBGjx4dy1ZffXUAjj766FgWYt1qfX814T2m3+sjjzwC9I6zv/GNbwC95yba\nZRwSxhSQxCBhTAFJPJReAwzjijCmgOT95rkGB/mOF/NYg4NkXJFuexhXpOdRw7gijCkgGVcMhDW4\nPMcVzYwpGpm7a2W9YR4z/VkIz4owhwnZ85iN5jBBsm4Y1gzBPKZWcpzRucLcsXFna82fPx9I8qAA\nVlppJQCuvvrqWBaej88880wsSz9n+5PX2meIpUJcCHDGGWcAsNNOO8WywYMrt6ZtvvnmAHz+858H\nYO+9915ou5tVbzzYypwsqC0ebGVOFtQWD3ZiTlY7zQvXK4/15TLqLaLueq211loAPPjgg7Hsi1/8\nIgBbb711LAv5pmeeeWa/dZkn1l2GDRsWr41Xi1HtvhpfFq/omBKS2KhdYkooPq7MY46x7JgSKucY\ny44poXKOsRUxZTvGhXnkzVeb/ypy32EtOZiN5BeUrdYYuagcDhUjHfsF7iXIl/s4ixFiO0jiuxDb\nQRLfpfMDQ3xXb2wHlfFdiO0gie9CbAdJfJeOi0J8F2I7SOK7rNgOWjtnGPLYwhwBwLLLLgvAtdde\nG8sGDRoEwBVXXBHLwhxCiPO+8IUvxNf6xspQ//1spaLWiCH5DJY9J1iUouKskGsJyee01bmWnToX\nWfY85EBTLb5yjk2SNFC5HlIsx8bFyXPPZnqOM6x9hHUPKGfPZjN5/ml57tnMGjdDa/ds1rqfMW+1\n5BSnzw0JOcXttLZT1r3rK+TDQJITk57rCjkx6TNss3JilK+sdYn0vFo3SM+FhmtjoeI0epZium8N\nfVa1+eFw3iNkz0umc1JqOTO73vOf0u0s+rxHyHevGTQeD7XT2SRBK9cRoDvOsMhD3msFRZ2J3Yqc\n2laeUZHH/jflKz2GDs9Icz3K5TmP9at2zmOj2mls3E5tqUWRZ0/27WeL6gsh3xyCsnMT2mnPkdqP\n/U39PFc432d7LTnirTyvGLKfla04P7CWsz4a2Y+ggSnPOc703F0e+bvpZ3u4ruUZD8lzvqhnPHTf\nuKIoRT3H22UPUa39w0D4Xau7FPXviRXdP0D2mYpFjQO6uX8oeizjGkz98jwzvZmxlHkyqtegshsg\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkpQ0quwGSJEmS\nJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElSWs+CBQvKbsPCtH0D\nJSlPn/zkJ+P1pptuCsB5551XVnO60uTJkwHYaqutGq7j1ltvjdc77LADAHPmzIlla6yxBgAf+9jH\nYtltt91WUc8JJ5wAwM477wzAxhtvXHg7i5KOKQYNGgTA2muvHcsef/zxiu9ZeeWVAXjppZeq1tPX\ncsstB/S+19OnT8+1TeH1J598MrNNoWz55ZcHYMaMGfG12bNnV3z92LFjAZg6dWosC3/bhx9+eCx7\n5JFHAFh11VVj2d133w3APvvsE8sWX3xxAL71rW/FsoMPPhiApZZaquLn5+2DDz4AYJtttollBx54\nIAB777134T+/mksvvRSAQw45JJZttNFGAPz5z3+uq67w/iB5j+H9QfX3+Le//Q3o/Xe87LLLAnDT\nTTfFsvXXXx+AO++8M5Z94xvfAHp/PmbNmgXA4MGDARg+fHh8beTIkQC8+uqrme/j+uuvB2DPPfeM\nZfvuuy8AV111VSwLdYd6F1Z3rfUqXyuuuCIAEyZMiGXpZ8BAZvxUvBCXQOOxSVHxEyTPXuOnSiF+\nguR+t0v8BEkMNRDip56eHmDh97NoRcU5IcaBJM4JMQ4kv8cQ40Bxcc7TTz8NwKRJk+Jr77//PgCL\nLrpoZr3h6xZbbLFY9vGPfxyAq6++OpaFexHuA9R2L7LiPeXP+Klx4Zl14403xrLddtut5T+/2jOz\nqLjksssui2WhD0w/H+fPnw/AmmuuGcteeOEFAObOnZv589qlD0j71Kc+BdT/rAxfA8mzMjwnAf74\nxz/m2s6gFfcwz3mF9957L16HvuKhhx6KZeuuu25d9aXnnUIfGeIdgKOPPhpIfj9XXnllfG3//fev\nq83pvi20uVp7p02bVvHzr7jiilgW/o6CddZZJ14/8cQTQHYc2wrhcwXJ866Vz7pWC8+70aNHx7Jr\nr722rOZ0tfDZTv8dhOdX1pgKknFV1pgKapuXTn/Gs35+Nc18bx5cbxhY6w1F9/Mh7oEk9knHNEXH\nPo2Mw+sV4hhIYplG45ha603X3Q1xwUATPnfp3121uVhJrVdtfjLMYxS9ZqFEUTF6IzHlTjvtBPR+\nToe55aFDh1Z877hx42LZZpttBsCUKVMq6g39ceiLAebNm9erbZDMiYUYFJLPaXr+Iyh7LJFW79gs\njCkgGVfkuQbXSJvq/SzWuwYHybgivb4exhVhTAHJuCI9JxXGFe0ypoDiPneNzqemv66Z+dR65+5q\nlXe94XOXXucLn5kwVoDsecxGc5ggWTcMa4ZgHlMrOc7oXCussAIA3/72t2NZeo5JxbjmmmuA3s+s\nv/71r0Dv3Klq1lprLQCeeuqpWNY3Rsh77fO1116L16utthoAX/va12JZ+pkeTJw4EUjyK1o9pmk0\nHiwqJ6uRNhWdkwW1xYPtPsdcTZ7xGyQxXFHr4XmvLxddb7ruvGPUPNYMnnvuuXgdcrbCcw/gN7/5\nDQD//d//DcCZZ54ZXwt/nyFfDhrPE4PsnDm1zoUXXhiv/+d//geAV155pazmdKX08zY8g9NjuXS/\nqfwUHVNCEle2S0wJrY0rjSmLnWNsRUyZR75A3nFhHnnz1ea/itx3WIta8wtqVdS8b5ExclBUDodq\nk/7ch3mv++67L5Y1s99MH0o/O0P/vccee5TVnK4RYjtInsUhtoPs+C7EdpDEd1mxHVTGd+lxc4jv\nQmwH2fFdiO0gie+yYjto7ZzhueeeCyTzAJD0Ken5hyD9XkNf+cUvfhGAUaNGxdf6xsrQ3P0M6u0D\na/36otaIIYnhum2NOMg7zjrrrLOA3uunId+yb64l5J9vmRUP5TFfWK3eZupuRfymbGFuM/238Ktf\n/QpI8pokSRooXn75ZQBWWmmlWHb//fcDvc89UmMcGxcvjz2bCxuPlLFnM488f8h3z2bWuBnKOSMy\n7/2MC1NLTnF6zSb8zaT3vXfbXtA8c2LSuSnh+RXyYSA7J0b5euCBB+L1FltsASTPzHDmUbeo9jfW\nyrP6Bpo8z1JM/25CHx3Oe4Qkzso67xGKPzO7FZ+hoveaQePxUNlnk7RyHQHKyy+uRSvWIMK+m7zX\nCoo6E/vRRx+NZbXk1NZa78yZM2NZLWdUQPV9sfWum+Sx/03FCXHGJZdcApR3xn1RfvrTn8br3Xff\nHSjvrBXPeWxfZZ+TVK0tZe3NaaezJ/v2s3ntLwn9Yd5xQVHxRq2K2nPUjGrnS6afy93Kc4W7v7/x\nXOFKeZ4rXGS9RT8rG8nv79R/r2ignSsc+q8wpoByxhULm0Mpau6w6PzddnrGN6PsWL5erZiTK7p/\nSNfdyv6h7L1z7XTeXDUDaRzQLu+1lf0DVI4Dyt7f0e6ynhmt7h+KGsv83//9H9C9azDpuvP4969a\neWZ6I31wp+TJlD3/0oXCH1VNN9GMKEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJEmSJEmSJEmSJLWVwWU3QJLUW09PT9lN6Hp/+MMfAFh33XVj2SOPPNJ0vUOHDo3X48ePB2DC\nhAmx7OmnnwZglVVWiWVPPPEEABtvvHHL2lmURj67iy++eF1fP3LkSCC5b0W0qdbvCV8X2vTaa69l\nfv0ll1wCwD777BPLjjjiCACuv/76WHbxxRcDsNRSS8Wy119/HYCZM2fGso022qjXfwGWXHLJmtqe\nh5NOOgmArbfeOpbtvffeLfv51Rx44IFAcl8BnnrqqYbqCu8Pkve4sPf3sY99DIA777wzlh177LEA\nfO5zn4tlH/3oRyvauWDBAgA+/elPx7LBg3uH6qNGjer3tf6k6wuqPUdC3XnXq3zNnz8fgEGDBpXc\nkvZj/FS8EJdAEpsYPzWvlfET1BZDtTJ+guwYqtvip3ZRVJwTYhxIfk8hxoEkfqgWb+Qd52y44YYA\nTJo0Kb725ptvArDccstl1jtjxoyKshVWWAFI7gMk9yLcB6jtXmTFe8qf8VN3KiouOfjgg+N16D8u\nuOCCWBaeI7Nnz45lH3zwQdM/tyzrrLMOUP+zMus52enynFf49a9/Ha9DLJuOUeuV7ivDdbq+ESNG\nALDXXnsBcN1118XX9t9//5p+RmhzOvbOavMhhxxScf3kk0/2+/Xpv53g8ccfj9dDhgwBYPXVV6+p\nvaqfY+fWqfde1zumgtrnpTuV6w0Da72haCHugST2CXEPFB/7NDIOr1eIYyD5LDQaxzRSr3FB53G8\nLLWvavFDep5VrVFUjN5ITPnZz34WgFtvvTWW3X777QB86Utfyqw7fG+tqvULoWzs2LGxrNYYumz1\n3u+i1+AaaVPRa3CQjCvSc2JhXBHGFJCMK8KYApJxRbePKaDx+VSoHFc0Mp9a79xdWfWGucv0fGao\nL8xhQvY8pjlMnctxRucz7myt2267raJsjTXWqOl7a/k7y3vtc9lll43XBxxwAACXXXZZLPvOd74D\n9O7n7rnnHqB3XkUrGQ/mFw+2wxxzep63Fp/61KeA/HOHipL3+nLR9abrzjtGbcaDDz4IwA477BDL\nwud9p512imXbbrstAOeccw4Aw4YNi6+ddtppQD55YpCdM6fWCbEquG5eFOc1y1F0TAlJXGlMWRtj\nyvrmGFsRU5adL1BNHnnzWXsDm6m3v7prUVTeQt6KjJGDTrkX3apaDOKcZXGM+fLTytgOKuO7dP8Y\n4ruF7ZUI8V2I7SCJ70JsB0n/GWI7aG18d/fdd1eUrbTSSv1+fchdA9h33337/bq+sTI0dz+LVtQa\ncfq629aIg7zjrJBjmc67bGW+ZafNRbYiflM24ytJkhKuhxTLsXHxBtKezUb24ua5Z7Mdxs19z4hs\n9fmQteQUh3xiSHKKy17bacVe0HqFfBhIcmLScz0hJybkw0B2Tozylc5JCQbSuNlYqDhFn5kdznuE\n5MzHcN4jJHNx6T69ljMf230vR9F5IFB8PFTUvsdOXEeoN7c4PbdetjzXCtJrW0WdiV1vTm2t9dZ7\nRgXUfi+y1k3y2P+m4rg/rT14zmO+Gt0PA+XnPea5t7rZthSl1rMn+/azee0vKSouCO0rOzehnfYc\nqf3Y3+TDc4Ur5XmucJH1Fv2sbCSn3bM+lKei5jg7LX83D/WOKaDz9tm3QtH9Q7ruVvYPZa+vSvWy\nf8hXnvNOre4fihrLFLVfo13WYNJ15/HvX7XyzPRa+2DzZFQvV5QkSZIkSZIkSZIkSZIkSZIkSZIk\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZLUVgaV3QBJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiQpbXDZDZAk9TZs2LB4PWfOnBJb0r3eeecdAJ599tlY\n9u677wKw+OKL11THvHnz4vUiiyxS8foBBxwAwCmnnBLLLr74YgA233zzWLbrrruW2k61zi677ALA\nhhtuGMsOOeQQAO66665YtuWWWwJw+eWXx7KDDjoIgC222CKWnXXWWQBsv/32sWyDDTYA4Jhjjoll\nO++8MwCDBzcf9v3yl7+M14suuigAJ554YtP15iV8xpdeeulYtswyy9RVR3iP4f1B/e/x05/+dLx+\n8MEH+/26W265JV6//vrrAOyzzz79fv1aa61VUe+CBQtiWU9PT8X3jBkzpqJsiSWW6LfudHtD3c3U\nq3yFuCAdK+hDxk/FC3EJJLGJ8ZOK1g3xUzspI84JMQ60Ns5Zb731Kl575ZVXAFhuueX6bUf669LC\nZywt3Ius+wCV9yLrPih/xk/dKSsugdr6/Gr9/aRJk2LZV77yFaB33xL6jxtuuKGRZi/UtGnT4vUb\nb7yRa93Dhw8HYNVVV41ljT4ra31OdqI85hWCG2+8MV6HmKZIX/jCF3r9f62xb1poc63tTc9r3HTT\nTXX/PIBx48bF69VXXx2Ap59+uqG6tHChP3TcLHW+MsbLYawMnTdezop7oLjYJ49xeK3Sc1ghlmk0\njmmkXuOCzuN4WWpfQ4cOrSgzhh/YvvnNbwLJ/A7A/vvvD8ADDzwQy0K/efrpp8e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Ljz76aMiOOOIIAG677baQNW3aFIDMzMwkWpQkaZfxww/x+IQTUvXAA+Ns+vRULVJk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\nJEmSJEmSJEmSJEmSJEm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d1f573e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_tree\n",
    "import xgboost as xgb\n",
    "X_df = pd.DataFrame(X, columns=colnames)\n",
    "clf = XGBClassifier(max_depth=5).fit(X_df, y)\n",
    "xgb.plot_tree(clf, num_trees=2)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(150, 100)\n",
    "#fig.savefig('tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0d1f4194a8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zvS8R/CVVdYMkJ2zh0v26+/zVrgcAAJgdAtYCQ4jab9p1AAAAs8cSQQAAgJEIWAAAACMR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\nlDJeabgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d272dee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance = (pd.DataFrame(list(zip(colnames,clf.feature_importances_)),\n",
    "                          columns=['Feature Ranking','Scores'])\n",
    "              .sort_values(by='Scores',ascending=False))\n",
    "sns.factorplot(y='Feature Ranking',x='Scores', data=importance,kind='bar',size=10, aspect=1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, although custserv_call is used as criterion for splitting the tree on the second level. However, it ranks low in the overall feature importance ranking. \n",
    "\n",
    "This is something important to watch out for in practice because important business decisions are being made with such analysis. Give the difference in ranking for various methods, it is both important and worthwhile to try multiple methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}