12-Decision-Tree/07-Problems-of-Decision-Tree/07-Problems-of-Decision-Tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:,2:]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\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": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, criterion=\"entropy\")\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*200)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*200)).reshape(-1, 1),\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l8t6dauDz8W+6lV440giVyx9xz8LrCUUVp6y8fLohR9VgXbNulkaOp1lrQuHVHqFy2Uc/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\nC3FmEqD9n0wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f92a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(tree_clf, axis=[0.5, 7.5, 0, 3])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.scatter(X[y==2,0], X[y==2,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.delete(X, 138, axis=0)\n",
    "y_new = np.delete(y, 138)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf2 = DecisionTreeClassifier(max_depth=2, criterion=\"entropy\")\n",
    "tree_clf2.fit(X_new, y_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nESkhNXcRkRJScxcRKSE1dxGREvr/buc29RUzAfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ea8b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(tree_clf2, axis=[0.5, 7.5, 0, 3])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.scatter(X[y==2,0], X[y==2,1])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}