ML-Decision Tree .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#regressor used for bottom up approach\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create random dataset\n",
    "rng=np.random.RandomState(1)\n",
    "X=np.sort(5*rng.rand(80,1),axis=0)\n",
    "y=np.sin(X).ravel()\n",
    "y[::5]+=3*(0.5-rng.rand(16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit regression model\n",
    "regr_1=DecisionTreeRegressor(max_depth=5)\n",
    "regr_2=DecisionTreeRegressor(max_depth=2)\n",
    "regr_1.fit(X,y)\n",
    "regr_2.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict\n",
    "X_test=np.arange(0.0,5.0,0.01)[:,np.newaxis]\n",
    "y_1=regr_1.predict(X_test)\n",
    "y_2=regr_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 57,
     "metadata": {},
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   ],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_2"
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  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting\n",
    "plt.figure()\n",
    "plt.scatter(X,y,s=30,edgecolor=\"black\",\n",
    "           c=\"darkorange\",label=\"Data\")\n",
    "plt.plot(X_test,y_1,color=\"blue\",\n",
    "        label=\"max_depth=5\",linewidth=2)\n",
    "plt.plot(X_test,y_2,color=\"yellowgreen\",label=\"max_depth=2\",linewidth=2)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"decision tree regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}