Bagging_Random_Forest_Boosting_Stacking.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Bady9898/R_Python_libraries/blob/main/Bagging_Random_Forest_Boosting_Stacking.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVYmfSmHR-CX",
        "outputId": "96ce3132-97b5-4a55-b5cc-0f8e780fbbe2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.770745044429255\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",
        "\n",
        "filename = '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 = 90\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": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arIaOxw4R-Ca",
        "outputId": "6dc5fc69-fb73-4710-a814-5462b44fc0f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7668831168831168\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",
        "\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": 3,
      "metadata": {
        "id": "djWo2CBWR-Cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3852a52e-518e-4bbb-8f39-eab0778965c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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 = '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": 4,
      "metadata": {
        "id": "VDQ40n4DR-Cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ff58389-3ff6-4a48-953c-a64ed0936a87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7630382775119617\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 = '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": {
        "id": "d7amvFOER-Cc"
      },
      "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": "3.7.4"
    },
    "colab": {
      "name": "Bagging_Random_Forest_Boosting_Stacking.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}