simple_random_forest_regressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [1]\n",
      " [2]\n",
      " [3]\n",
      " [4]\n",
      " [5]\n",
      " [6]\n",
      " [7]\n",
      " [8]\n",
      " [9]]\n",
      "[ 0.          1.          1.41421356  1.73205081  2.          2.23606798\n",
      "  2.44948974  2.64575131  2.82842712  3.        ]\n"
     ]
    }
   ],
   "source": [
    "#Creatig dataset\n",
    "x_values = np.array(range(10)).reshape(-1, 1)\n",
    "y_values = np.array([k**.5 for k in range(10)])\n",
    "print x_values\n",
    "print y_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train model on data\n",
    "#train model on data\n",
    "rf = RandomForestRegressor()\n",
    "rf.fit(x_values, y_values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.22173322]\n"
     ]
    }
   ],
   "source": [
    "#predict one values\n",
    "y_predicted = dt.predict(2)\n",
    "print y_predicted\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Kd911sNdesMkm2QRKA4QkVQZDhCpWSjB1anYL5yc/CXfeCdtsU3RVkqRVcgkR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\nxRAhSZLKYoiQJEllMURIkqSy/H+vDRo8F5BzKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112f05610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize actual data\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.plot(x_values, y_values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CHHkkHHIIDBkCDzwAJ59sgJCkjrAnQn1KSnD55XDaadnzyy7LwoSbZ0lSx/l7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130139d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize results\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.plot(x_values, rf.predict(x_values))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}