14-ENSEMBLE LEARNING.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about ensemble learning.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y =make_moons(n_samples=100, noise=0.25, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "log=LogisticRegression(solver='lbfgs').fit(X_train,y_train)\n",
    "rnd=RandomForestClassifier(n_estimators=10).fit(X_train,y_train)\n",
    "svm=SVC(gamma='auto').fit(X_train,y_train)\n",
    "voting=VotingClassifier(estimators=[('lr',log),('rf',rnd),('svc',svm)],\n",
    "                       voting='hard').fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.76\n",
      "0.84\n",
      "0.88\n",
      "0.88\n"
     ]
    }
   ],
   "source": [
    "print(log.score(X_test,y_test))\n",
    "print(rnd.score(X_test,y_test))\n",
    "print(svm.score(X_test,y_test))\n",
    "print(voting.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging and Pasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "X,y=make_blobs(n_samples=300,centers=4,random_state=0, cluster_std=1)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree=DecisionTreeClassifier().fit(X_train,y_train)\n",
    "bag=BaggingClassifier(tree,\n",
    "                     n_estimators=100,\n",
    "                     max_samples=0.8,\n",
    "                     n_jobs=-1,\n",
    "                     random_state=1).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9066666666666666\n",
      "0.9466666666666667\n"
     ]
    }
   ],
   "source": [
    "print(tree.score(X_test,y_test))\n",
    "print(bag.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y =make_moons(n_samples=100, noise=0.25, random_state=3)\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest=RandomForestClassifier(n_estimators=5).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "kanser=load_breast_cancer()\n",
    "X_train,X_test,y_train,y_test=train_test_split(kanser.data,kanser.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.972027972027972\n"
     ]
    }
   ],
   "source": [
    "forest=RandomForestClassifier(n_estimators=100).fit(X_train,y_train)\n",
    "print(forest.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbrt=GradientBoostingClassifier(learning_rate=0.01,random_state=0).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9882629107981221\n",
      "0.965034965034965\n"
     ]
    }
   ],
   "source": [
    "print(gbrt.score(X_train,y_train))\n",
    "print(gbrt.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
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