Assign2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Author: Rudra Panda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1) Load the dataset into a pandas dataframe and display the first 5 lines of the dataset along with the column headings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  custcat  \n",
       "0       2        1  \n",
       "1       6        4  \n",
       "2       2        3  \n",
       "3       1        1  \n",
       "4       4        3  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "#loading the dataset\n",
    "dataset = pd.read_csv(\"data.csv\")\n",
    "\n",
    "#display the first 5 lines of the dataset along with the column headings\n",
    "dataset.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2) Display the number of instances for each class. That is, you should write code to find out how many instances you have for class 1, class 2, class 3, and class 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    281\n",
       "1    266\n",
       "4    236\n",
       "2    217\n",
       "Name: custcat, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.custcat.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q3) Create histograms of columns age and income to visually explore their distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 140.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age = dataset.iloc[:,2].values\n",
    "income = dataset.iloc[:,5].values\n",
    "\n",
    "n, bins, patches = plt.hist(x=age, bins='auto', color='#0504aa',\n",
    "                            alpha=0.7, rwidth=0.85)\n",
    "plt.grid(axis='both', alpha=0.75)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Age')\n",
    "maxfreq = n.max()\n",
    "# Set a clean upper y-axis limit.\n",
    "plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 200.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n, bins, patches = plt.hist(x=income, bins='auto', color='#0504aa',\n",
    "                            alpha=0.7, rwidth=0.85)\n",
    "plt.grid(axis='both', alpha=0.75)\n",
    "plt.xlabel('Income')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Income')\n",
    "maxfreq = n.max()\n",
    "# Set a clean upper y-axis limit.\n",
    "plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function that returns testing accuracy and misclassification rate for a given confusion matrix\n",
    "def model_quality(confusion_matrix):\n",
    "    #Testing accuracy and misclassification rate\n",
    "    TP = np.diag(confusion_matrix)\n",
    "    FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix)\n",
    "    FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)\n",
    "    TN = confusion_matrix.sum() - (TP+FP+FN)\n",
    "\n",
    "    accuracy = (TP+TN) / (TP+FP+FN+TN)\n",
    "    misclassification = (FN+FP) / (TP+FP+FN+TN)\n",
    "    \n",
    "    return accuracy, misclassification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q4) Perform normalization of the data using standardization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q5) Split the dataset into training (80%) and testing set (20%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,:-1].values\n",
    "y = dataset.iloc[:,11].values\n",
    "\n",
    "#Splitting the data into Training Set and Test Set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "\n",
    "#Normalizing the features\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X= StandardScaler()\n",
    "X_train= sc_X.fit_transform(X_train)\n",
    "X_test= sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q6) Model 1: Fit a logistic regression model. What is the testing misclassification rate you get?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassification Rate:  [0.3   0.31  0.325 0.275] \n",
      " Average Misclassification Rate:  0.3025\n"
     ]
    }
   ],
   "source": [
    "#Fitting Logistic Regression to Training Set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifierObj= LogisticRegression(random_state=0)\n",
    "classifierObj.fit(X_train, y_train)\n",
    "\n",
    "#Making predictions on the Test Set\n",
    "y_pred= classifierObj.predict(X_test)\n",
    "\n",
    "#Evaluating the predictions using a Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "#Testing accuracy and misclassification rate\n",
    "accuracy, misclassification = model_quality(cm)\n",
    "\n",
    "mean_mc = misclassification.mean()\n",
    "print(\"Misclassification Rate: \", misclassification, \"\\n\", \"Average Misclassification Rate: \", mean_mc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q7) Model 2: We will now fit k-NN. However for k-NN you need to specify the value for k. In order to figure that out, run k-NN with different values of k and compute the testing misclassification rate. Plot a chart with k on X-axis and testing error on the Y-axis. What is the lowest value of testing error and corresponding value of k?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lowest value of testing error and corresponding value of k:  0.29 \t 109 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Fitting KNN Classifier to Training Set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "#function that returns predicted values and confusion matrix \n",
    "#for given training and test data along with k (number of neighbors)\n",
    "def KNeighbors_Classifier(Xtrain,yTrain,Xtest,yTest,neighbors):\n",
    "    classifierObj= KNeighborsClassifier(n_neighbors=neighbors, p=2, metric='minkowski')\n",
    "    classifierObj.fit(Xtrain, yTrain)\n",
    "\n",
    "    #Making predictions on the Test Set\n",
    "    yPred= classifierObj.predict(Xtest)\n",
    "\n",
    "    #Evaluating the predictions using a Confusion Matrix\n",
    "    cm = confusion_matrix(yTest, yPred)\n",
    "    \n",
    "    return yPred, cm\n",
    "\n",
    "\n",
    "k_error = {} #dictionary that stores k and corresponding misclassification rate\n",
    "for nbr in range(1, (len(X_train)+1)):\n",
    "    y_pred, cm = KNeighbors_Classifier(X_train,y_train,X_test,y_test,nbr)\n",
    "\n",
    "    #Testing accuracy and misclassification rate\n",
    "    accuracy, misclassification = model_quality(cm)\n",
    "    #error for the model is average of misclassification rates of class labels\n",
    "    mean_mc = misclassification.mean() \n",
    "    k_error[nbr]=mean_mc\n",
    "\n",
    "#Plot a chart with k on X-axis and testing error on the Y-axis.\n",
    "k_error_list = sorted(k_error.items()) # sorted by key, return a list of tuples\n",
    "k, mc = zip(*k_error_list) # unpack a list of pairs into two tuples\n",
    "plt.plot(k, mc, color='brown')\n",
    "plt.title(\"K values vs. Testing Error\")\n",
    "plt.xlabel(\"K - number of neighbors\")\n",
    "plt.ylabel(\"Misclassification Rate\")\n",
    "plt.show()\n",
    "\n",
    "k_error_min = min(k_error.keys(), key=(lambda k: k_error[k]))\n",
    "print(\"lowest value of testing error and corresponding value of k: \", k_error.get(k_error_min), \"\\t\", k_error_min, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q8) Model 3: Fit SVM model with different kernels. Which kernel gives the least testing error?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lowest value of testing error and corresponding value of kernel:  0.305 \t linear \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Fitting SVM Classifier to Training Set.\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def SVM_Classifier(Xtrain,yTrain,Xtest,yTest,kernel):\n",
    "    classifierObj= SVC(kernel=kernel)\n",
    "    classifierObj.fit(Xtrain, yTrain)\n",
    "    \n",
    "    #Making predictions on the Test Set\n",
    "    yPred= classifierObj.predict(Xtest)\n",
    "\n",
    "    #Evaluating the predictions using a Confusion Matrix\n",
    "    cm = confusion_matrix(yTest, yPred)\n",
    "    \n",
    "    return yPred, cm\n",
    "\n",
    "model_error = {} #dictionary that stores kernel and corresponding misclassification rate\n",
    "kernel_options = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "for kernel in kernel_options:\n",
    "    y_pred, cm = SVM_Classifier(X_train,y_train,X_test,y_test,kernel)\n",
    "\n",
    "    #Testing accuracy and misclassification rate\n",
    "    accuracy, misclassification = model_quality(cm)\n",
    "    #error for the model is average of misclassification rates of class labels\n",
    "    mean_mc = misclassification.mean() \n",
    "    model_error[kernel]=mean_mc\n",
    "\n",
    "kernel_with_least_error = min(model_error.keys(), key=(lambda kernel: model_error[kernel]))\n",
    "\n",
    "print(\"lowest value of testing error and corresponding value of kernel: \", model_error.get(kernel_with_least_error), \"\\t\", kernel_with_least_error, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q9) Model 4: Fit Naïve Bayes model. What is the testing error you get?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassification Rate:  [0.44  0.265 0.285 0.21 ] \n",
      " Average Misclassification Rate:  0.3\n"
     ]
    }
   ],
   "source": [
    "#Fitting NaiveBayes to Training Set.\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifierObj= GaussianNB()\n",
    "classifierObj.fit(X_train, y_train)\n",
    "\n",
    "#Making predictions on the Test Set\n",
    "y_pred= classifierObj.predict(X_test)\n",
    "\n",
    "#Evaluating the predictions using a Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "#Testing accuracy and misclassification rate\n",
    "accuracy, misclassification = model_quality(cm)\n",
    "\n",
    "mean_mc = misclassification.mean()\n",
    "print(\"Misclassification Rate: \", misclassification, \"\\n\", \"Average Misclassification Rate: \", mean_mc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q10) Model 5: Fit Random Forest model. For Random Forest, you need to specify the number of trees (n_estimators). In order to figure that out, run Random Forest with different values of n_estimators and compute the testing misclassification rate. Plot a chart with n_estimators on X-axis and testing error on the Y-axis. What is the lowest value of testing error and corresponding value of n_estimators?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lowest value of testing error and corresponding value of n_estimators:  0.335 \t 15 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Fitting Random Forest Classifier to Training Set.\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "def Random_Forest_Classifier(Xtrain,yTrain,Xtest,yTest,n_trees):\n",
    "    classifierObj= RandomForestClassifier(n_estimators=n_trees, criterion='entropy')\n",
    "    classifierObj.fit(Xtrain, yTrain)\n",
    "    \n",
    "    #Making predictions on the Test Set\n",
    "    yPred= classifierObj.predict(Xtest)\n",
    "\n",
    "    #Evaluating the predictions using a Confusion Matrix\n",
    "    cm = confusion_matrix(yTest, yPred)\n",
    "    \n",
    "    return yPred, cm\n",
    "\n",
    "model_error = {} #dictionary that stores number of trees and corresponding misclassification rate\n",
    "\n",
    "for n_trees in range(1, (len(X_train)+1)):\n",
    "    y_pred, cm = Random_Forest_Classifier(X_train,y_train,X_test,y_test,n_trees)\n",
    "\n",
    "    #Testing accuracy and misclassification rate\n",
    "    accuracy, misclassification = model_quality(cm)\n",
    "    #error for the model is average of misclassification rates of class labels\n",
    "    mean_mc = misclassification.mean() \n",
    "    model_error[n_trees]=mean_mc\n",
    "\n",
    "\n",
    "#Plot a chart with number of trees on X-axis and testing error on the Y-axis.\n",
    "model_error_list = sorted(model_error.items()) # sorted by key, return a list of tuples\n",
    "ne, mc = zip(*model_error_list) # unpack a list of pairs into two tuples\n",
    "plt.plot(ne, mc, color='brown')\n",
    "plt.title(\"Number of Trees vs. Testing Error\")\n",
    "plt.xlabel(\"Number of Trees\")\n",
    "plt.ylabel(\"Misclassification Rate\")\n",
    "plt.show()\n",
    "    \n",
    "n_trees_with_least_error = min(model_error.keys(), key=(lambda ne: model_error[ne]))\n",
    "print(\"lowest value of testing error and corresponding value of n_estimators: \", k_error.get(n_trees_with_least_error), \"\\t\", n_trees_with_least_error, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q11) Predicting with Ensemble: Now that you have built 5 models. Loop over the testing set. For every test instance, have each of the models predict the class label. Eventual class predicted will be based on a majority vote of the 5 models. What is the testing misclassification rate you get with the ensemble model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble average misclassification rate:  0.3025\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression prediction\n",
    "lrObj= LogisticRegression(random_state=0)\n",
    "lrObj.fit(X_train, y_train)\n",
    "y_pred_lr= lrObj.predict(X_test)\n",
    "\n",
    "#k-nn prediction\n",
    "knnObj= KNeighborsClassifier(n_neighbors=k_error_min, p=2, metric='minkowski')\n",
    "knnObj.fit(X_train, y_train)\n",
    "y_pred_knn= knnObj.predict(X_test)\n",
    "\n",
    "#SVM prediction\n",
    "svcObj= SVC(kernel=kernel_with_least_error)\n",
    "svcObj.fit(X_train, y_train)\n",
    "y_pred_svc= svcObj.predict(X_test)\n",
    "\n",
    "#NaiveBayes prediction\n",
    "gnbObj= GaussianNB()\n",
    "gnbObj.fit(X_train, y_train)\n",
    "y_pred_gnb= gnbObj.predict(X_test)\n",
    "\n",
    "#Random Forestr prediction\n",
    "rfObj= RandomForestClassifier(n_estimators=n_trees_with_least_error, criterion='entropy')\n",
    "rfObj.fit(X_train, y_train)\n",
    "y_pred_rf= rfObj.predict(X_test)\n",
    "\n",
    "y_pred_ensemble = []\n",
    "\n",
    "for t in range(0, len(X_test)):\n",
    "    #majority vote\n",
    "    class_vote_count = {}\n",
    "    class_vote_count[1] = 0\n",
    "    class_vote_count[2] = 0\n",
    "    class_vote_count[3] = 0\n",
    "    class_vote_count[4] = 0\n",
    "    \n",
    "    class_vote_count[y_pred_lr[t]] = class_vote_count.get(y_pred_lr[t]) + 1\n",
    "    class_vote_count[y_pred_knn[t]] = class_vote_count.get(y_pred_knn[t]) + 1\n",
    "    class_vote_count[y_pred_svc[t]] = class_vote_count.get(y_pred_svc[t]) + 1\n",
    "    class_vote_count[y_pred_gnb[t]] = class_vote_count.get(y_pred_gnb[t]) + 1\n",
    "    class_vote_count[y_pred_rf[t]] = class_vote_count.get(y_pred_rf[t]) + 1\n",
    "    \n",
    "    class_label_with_max_vote = max(class_vote_count.keys(), key=(lambda cl: class_vote_count[cl]))\n",
    "    y_pred_ensemble.append(class_label_with_max_vote)\n",
    "\n",
    "cm_en = confusion_matrix(y_test, y_pred_ensemble)\n",
    "accuracy, misclassification = model_quality(cm_en)\n",
    "#error for the model is average of misclassification rates of class labels\n",
    "mean_mc = misclassification.mean() \n",
    "print(\"Ensemble average misclassification rate: \", mean_mc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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