CART Decision Tree and Parametres.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "X, y = datasets.make_moons(noise=0.25, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfxGRBvp/RNEY8SiTfQkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt_clf = DecisionTreeClassifier()\n",
    "dt_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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])*100)).reshape(-1, 1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(dt_clf, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 显然上述发生了过拟合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 通过 调整参数来改善\n",
    "##### 调整最大深度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_clf2 = DecisionTreeClassifier(max_depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', 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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf2.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRAAFBBERiSggiIgIoIAgIiKR/w/xxuvtHQHv8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(dt_clf2, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 调整最小叶子结点包含的样本数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREQiCggiIgIoIIiISOT/AyV89dS0yQOQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_clf4 = DecisionTreeClassifier(min_samples_leaf=6)\n",
    "dt_clf4.fit(X, y)\n",
    "\n",
    "plot_decision_boundary(dt_clf4, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 调整拥有最大的叶子结点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\matplotlib\\contour.py:960: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRAAFBBERiSggiIgIoIAgIiKR/w/xxuvtHQHv8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dt_clf5 = DecisionTreeClassifier(max_leaf_nodes=4)\n",
    "dt_clf5.fit(X, y)\n",
    "\n",
    "plot_decision_boundary(dt_clf5, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}