simple_decision_tree_regressor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train model on data\n",
    "dt = DecisionTreeRegressor()\n",
    "dt.fit(x_values, y_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.41421356]\n"
     ]
    }
   ],
   "source": [
    "#predict one values\n",
    "y_predicted = dt.predict(2)\n",
    "print y_predicted\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAFkCAYAAACemWn9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxRAhSZLKYoiQJEllMURIkqSy/H+vDRo8F5BzKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ec7f50>"
      ]
     },
     "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhEAAAFkCAYAAACemWn9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxRAhSZLKYoiQJEllMURIkqSy/H+vDRo8F5BzKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f4c5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize results\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.plot(x_values, dt.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
}