ML - Random Forest Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Random Forest is a type of ensemble learning(as is gradient boosting etc.)\n",
    "\n",
    "ensemble learning: taking a multiple algo's and putting thm together or running an algo multiple times and comibining it's results.\n",
    "\n",
    "Here, we will use an RFR approach to predict the 6.5 level salary. A random forest is essentially a collection of DT that have been averaged together.\n",
    "\n",
    "Let us assume that our target for the 6.5 level is 160,000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Position_Salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, 1:2].values\n",
    "y = dataset.iloc[:, 2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=1, oob_score=False, random_state=0,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = RandomForestRegressor(random_state = 0) #default is 10 tree's and criterion is MSE.\n",
    "regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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JqgX4ZETsSON8ArgROAi4Lz2gmAjxPUnLgPUUs+6IiPWSLgMeS8d9OSLWd8HnMTPrErVY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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114819d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_grid = np.arange(min(X), max(X)+0.01, 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color='red')\n",
    "plt.plot(X_grid, regressor.predict(X_grid), color='blue')\n",
    "plt.title(\"Salary vs. Level\")\n",
    "plt.xlabel(\"Levels\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the RF approach leads to more steps in the DT visualisation. Let's see how we did with 10 tree's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(6.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 167000.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor2 = RandomForestRegressor(n_estimators=100, random_state = 0) #default is 10 tree's and criterion is MSE.\n",
    "regressor2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PU4TiPEmXAquBCwAi4nlJ8yjCqhG4Is2YA7icvdO3H04PgO8Bd6WJDZspZt0REZslXQ88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\nF+zEo10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1151f80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_grid = np.arange(min(X), max(X)+0.01, 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color='red')\n",
    "plt.plot(X_grid, regressor2.predict(X_grid), color='blue')\n",
    "plt.title(\"Salary vs. Level\")\n",
    "plt.xlabel(\"Levels\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred2 = regressor2.predict(6.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 158300.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now lets try with 300 tree's!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=300, n_jobs=1, oob_score=False, random_state=0,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor3 = RandomForestRegressor(n_estimators=300, random_state = 0) #default is 10 tree's and criterion is MSE.\n",
    "regressor3.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NDQ1tH1iqkwIA4OWXYdu2Mk6cPx8uuYQXXjqMG7mAP/KKYqryqafC8cd3qKuH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\nT0UEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115492be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_grid = np.arange(min(X), max(X)+0.01, 0.01)\n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(X, y, color='red')\n",
    "plt.plot(X_grid, regressor3.predict(X_grid), color='blue')\n",
    "plt.title(\"Salary vs. Level\")\n",
    "plt.xlabel(\"Levels\")\n",
    "plt.ylabel(\"Salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred3 = regressor3.predict(6.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 160333.33333333])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that the RFR beats our polynomial regression! Yay!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}