Ensemble_Technique_Bagging-Boosting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T16:47:47.075447Z",
     "start_time": "2021-08-30T16:47:40.216709Z"
    },
    "execution": {
     "iopub.execute_input": "2021-08-30T06:35:17.393880Z",
     "iopub.status.busy": "2021-08-30T06:35:17.393039Z",
     "iopub.status.idle": "2021-08-30T06:35:23.150608Z",
     "shell.execute_reply": "2021-08-30T06:35:23.149229Z",
     "shell.execute_reply.started": "2021-08-30T06:35:17.393827Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7720437457279563\n"
     ]
    }
   ],
   "source": [
    "# Bagged Decision Trees for Classification\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "filename = r'C:\\Users\\Hp\\OneDrive\\Documents\\ExcelR\\Python Intro\\Ensemble Techniques\\pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "seed = 7\n",
    "\n",
    "kfold = KFold(n_splits=10)\n",
    "cart = DecisionTreeClassifier()\n",
    "num_trees = 100\n",
    "model = BaggingClassifier(base_estimator=cart, n_estimators=num_trees, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T16:48:05.351083Z",
     "start_time": "2021-08-30T16:47:59.603477Z"
    },
    "execution": {
     "iopub.execute_input": "2021-08-30T06:35:23.152604Z",
     "iopub.status.busy": "2021-08-30T06:35:23.152327Z",
     "iopub.status.idle": "2021-08-30T06:35:26.096555Z",
     "shell.execute_reply": "2021-08-30T06:35:26.095067Z",
     "shell.execute_reply.started": "2021-08-30T06:35:23.152577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7681647300068353\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classification\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "num_trees = 100\n",
    "max_features = 3\n",
    "kfold = KFold(n_splits=10)\n",
    "model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T16:48:21.406107Z",
     "start_time": "2021-08-30T16:48:20.888925Z"
    },
    "execution": {
     "iopub.execute_input": "2021-08-30T06:35:26.098518Z",
     "iopub.status.busy": "2021-08-30T06:35:26.098152Z",
     "iopub.status.idle": "2021-08-30T06:35:26.388776Z",
     "shell.execute_reply": "2021-08-30T06:35:26.387722Z",
     "shell.execute_reply.started": "2021-08-30T06:35:26.098480Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7525974025974026\n"
     ]
    }
   ],
   "source": [
    "# AdaBoost Classification\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "filename = r'C:\\Users\\Hp\\OneDrive\\Documents\\ExcelR\\Python Intro\\Ensemble Techniques\\pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "\n",
    "num_trees = 10\n",
    "seed=7\n",
    "kfold = KFold(n_splits=10)\n",
    "model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-30T16:48:34.608089Z",
     "start_time": "2021-08-30T16:48:32.038558Z"
    },
    "execution": {
     "iopub.execute_input": "2021-08-30T06:35:26.390417Z",
     "iopub.status.busy": "2021-08-30T06:35:26.389846Z",
     "iopub.status.idle": "2021-08-30T06:35:27.137869Z",
     "shell.execute_reply": "2021-08-30T06:35:27.137031Z",
     "shell.execute_reply.started": "2021-08-30T06:35:26.390375Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7682330827067669\n"
     ]
    }
   ],
   "source": [
    "# Stacking Ensemble for Classification\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "filename = r'C:\\Users\\Hp\\OneDrive\\Documents\\ExcelR\\Python Intro\\Ensemble Techniques\\pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "kfold = KFold(n_splits=10)\n",
    "\n",
    "# create the sub models\n",
    "estimators = []\n",
    "model1 = LogisticRegression(max_iter=500)\n",
    "estimators.append(('logistic', model1))\n",
    "model2 = DecisionTreeClassifier()\n",
    "estimators.append(('cart', model2))\n",
    "model3 = SVC()\n",
    "estimators.append(('svm', model3))\n",
    "\n",
    "# create the ensemble model\n",
    "ensemble = VotingClassifier(estimators)\n",
    "results = cross_val_score(ensemble, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}