DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 调用sklearn中决策树算法，调节参数观察决策边界的变化。\n",
    "note:sklearn中的决策树算法使用CART。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_moons\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 样本数据的生成\n",
    "X,y=make_moons(noise=0.23,random_state=888)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIhFS8BcRiZCCv4hIhP4f41tt3U4cxlkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8026a0>"
      ]
     },
     "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": 19,
   "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])*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": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRASA/w8CvTJTd7mmRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf9eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_classifier=DecisionTreeClassifier()\n",
    "tree_classifier.fit(X,y)\n",
    "plot_decision_boundary(tree_classifier,axis=[-1.6,2.5,-1.2,1.7])\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": [
    "### 接口参数：\n",
    ">class sklearn.tree.DecisionTreeClassifier(criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb925518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###控制树的深度\n",
    "tree_classifier=DecisionTreeClassifier(max_depth=2)\n",
    "tree_classifier.fit(X,y)\n",
    "plot_decision_boundary(tree_classifier,axis=[-1.6,2.5,-1.2,1.7])\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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc63e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#控制决策树的叶子结点的个数\n",
    "tree_classifier=DecisionTreeClassifier(max_leaf_nodes=7)\n",
    "tree_classifier.fit(X,y)\n",
    "plot_decision_boundary(tree_classifier,axis=[-1.6,2.5,-1.2,1.7])\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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3X+QXWWd5/H3t39AWugQQsjQJITI\nBNhgWA1SOE6mFJWdJVkXnB2swh+1aGmlnFlQ16oVZlMFVVZRo06VU4ruuBlHxXVKdHDFOIZhyaCL\nZlfHyA+TEMGGHaBJawgZOg12IN393T/uueH27XN/nueeX/fzqurK7XvPvefJ6XPP9zzP832ex9wd\nERGRgawLICIi+aCAICIigAKCiIhEFBBERARQQBARkYgCgoiIAAoIIiISUUAQERFAAUFERCJDWReg\nkTNGR33NGWdkXQwR6bHB5XBoRt/1UJ7+5QOH3f3Mbt6b24Cw5owz+MG2bVkXQ0R6bNl7Bvns/vdm\nXYzS+OhlJz/Z7XvVZCQiIoACgoiIRBQQREQEUEAQEZGIAoKIiAAKCCIiElFAEBERQAFBREQiCggi\nIgIoIIiISCRIQDCzL5vZITPb1+D1y81syswein5uDrFfEREJJ9RcRl8FPg98rck2P3L3twfan4iI\nBBakhuDu9wNHQnyWiIhkI80+hDea2cNmdreZvSZuAzPbamZ7zGzP4enpFIsmIiJpBYQHgHPd/bXA\nbcBdcRu5+3Z3v9TdL10xOppS0aRsdk6PsPnJlWx8fIzNT65k5/RI1kUSKYRUAoK7H3X3F6LHO4Fh\nM1uRxr6lv+ycHuETz57G5OwQjjE5O8Qnnj2tEEFBgUyylkpAMLOzzMyix5dF+30ujX1Lf7ntyCjH\nfOFpfcwHuO1IvmucRQ5kUh5BsozM7BvA5cAKM5sAbgGGAdz9i8A1wJ+Y2SwwA1zr7h5i3yK1fj07\n2NHzedEskG0ZncmoVNJvggQEd39Xi9c/TyUtVXJg5/QItx0Z5dezg5w1NMcNy6dLc9E5a2iOydnF\np/VZQ3MZlKZ9RQ1kUi4aqdxnyt40ccPyaZbY/ILnltg8NyzPd9Zao4CV90Am5aKA0GeK2sberi2j\nM9x85hRjQ7MYztjQLDefOZX7GlBRA5mUS6iRylIQ/dA0sWV0JvcBoF61vGVtypNiUEDoM0VtY+8H\nRQxkUi5qMuozapoQkUZUQ+gz/dQ0UeZsKpFeUEDoQ/3QNFHNpqp2oFezqYDS/99FuqUmIymlsmdT\nifSCaghSSp1mU6l5SUQ1BCmpTgZ6lX2wnki7FBCklDrJplLzkkiFmoxyRk0XYXSSTdUPg/VE2qGA\nkCPKjAmr3WwqDdYTqVCTUY6o6SIbGqwnUqEaQo6o6SIb/TRYT6QZBYQcUdNFdvphsJ5IK2oyyhE1\nXYhIllRDyBE1XYhIlhQQckZNFyKSFQWEADR2IN/09xFpjwJCQho7kA+NLvr6+4i0T53KCWnsQPaa\nzUWkv49I+xQQEtLYgew1u+jr7yPSPgWEhDqZVVN6o9lFX38fkfYpICSksQPZa3bRv2H5NMP4gueH\ncf19RGIoICS0ZXSGm8+cYmxoFsMZG5rl5jOn1GGZolZB2esCQv3vIlKhLKMAijh2oEypmM0G9G1+\nciWzdfc9s1T6F4r6/xXpFQWEggh5AS9jKmajoKxOZZH2BWkyMrMvm9khM9vX4HUzs8+Z2biZ/cLM\nLgmx334ReonHfkrFVKeySPtC9SF8FbiyyeubgfOjn63AXwXab18IfQHvp7tmdfqLtC9IQHD3+4Ej\nTTa5GviaV/wEWGZmYyH23Q9CX8D76a5Znf4i7UurD2EV8HTN7xPRc5Mp7b/QQq+TcMPy6QV9CND5\nXXOROqWL2OkvkoW0AoLFPLco98/MtlJpUmL18uW9LlNhhLiA1+pmmu3aALDU5vmtD3A8+rOWoVNa\nRNILCBPAOTW/rwYO1m/k7tuB7QAb165VsnikF+skdHLXXJ+VNOWLm6qqfRoKCCLFlVZA2AFcb2Z3\nAG8AptxdzUUdyLLZI65TO04ZO6VF+kmQgGBm3wAuB1aY2QRwCzAM4O5fBHYCW4Bx4LfA+0PsV9LR\n7oW+jJ3SIv0kSEBw93e1eN2B/xRiX5K+Rp3atZTKKVJ8mstIWorL5R9inmUDcxjOsoE5TsbZdmgZ\nm59c2fWAOWls5/QIm59cycbHx3SMpWc0dYW01KxTu4zTYOTNzukRbjm0bEFW1y2HlgE6xhKWAkKf\na3c8QaNO7WajqMt4scpi/MWnDy89EQyqjmN8+vDSUh5jyY4CQh8LcXcfehR1nge8ZVUben4+vmW3\n0fMi3dIZ1cca3d1/6tmlbX9GyGkwQk/iF1o/TQoo/UkBoU/EdUo2uouf8oG2L8IhJ4/L+wU3q0kB\nT6s7vq2eF+mWAkIfaHTnvbThBcXavgiHnDwu1AW3Vxk5WU0KeOOZRxlicZbXjWce7el+pf+oD6EP\nNLrzXjIwB+7ETTXVyUU41CjqEJP49bKd/w9GjvF306ew8Hg5k7ODbH5yZc/6O3oxdYlIHAWEPtCw\naWh+gGUD8zw/X3n9qoEf8/Ghb3G2HeY3nMHwi3/I8VNen1o5Q0zi16usp53TI3zvhVdRHwxIaYI/\nzdgqaVCTUR9o1tTx8RVHWWLzXDXwYz45/CVWDxxmwGDMnuNVU3cy/OLPUytniOanXrXzx8/ntLBm\nlaf+DpFuqIaQsTTSLJvdeVf39Zapb/Iqe3nB+8yPM/fSj7h90+eClqeVa2sePwvc3sF7Tz34I6Zn\nji1+fmSE2193Xddlmnz83va2mx1asJ+PvObrPP+3muNJikE1hAyllWbZ6s57y+gMZ9lzse8dnXkm\naFl6bdOGdQwNLjythwYH2LRhXaLPHR1ZEnQ7kTxSDSFDaY7ybdUGPT+4jMG55xc9Pz2yKmg5em39\nmsrKrLv3jTM9c4zRkSVs2rDuxPPd2rRhHbseeITZucapniECj0iWFBAylKfF7o+Nbuak6bsYnnsl\naBwfHGH3hdtSL0tS69eMJQ4AcZ8JCwPNq89awf/79eGggUckSwoIGQq9VnISx095PfevfRObHr2V\n0ZlnmB5Zxe4Lt/HYqmtSL0te9SLQiOSJAkKGQq+VnNRjq65RABDpYwoIGcrLgKNqptPk4/eq6UOk\njykgZCzEgKMkqav1I3unZ46x64FHABQURPqM0k4LLmnqalym0+zcPLv3jfeiuCKSYwoIBZd0htBG\nGU1xg7tEpNwUEAouaepqo4wmDbDKhtZOliwpIBRc0imZ49Yz0ACrbOR9gSApPwWEJopwt5Z0gZra\naS2gUjO44pKL1KHcI83OqbwvECTlpyyjBrJaP7dTIVJXq5lOSSZ/S+KCZ+7siwFxrc6pPI1cl2SK\nek4rIDSQ5jxDSRV5rvw37/04r33qqxgOwNKZCa7Y+zGAQnyBOtHqnMrTyHXp3gXP3MkVez92YhqY\nIp3TajJqQHdr7Tvw1CRf2vkj/vLb9/KlnT/iwFOTbb3vgmfuXBAMqobnZtj06K29KGqmWp1TIden\nluxsevTWBXOCQXHOadUQGtDdWnsOPDW5YBbQTga2bXr01kXBoKpo0263o9U5lZeR65JMo3O3COe0\nAkIDeZtnKK927xtfNCV0dWBbq4DQ7AtStGm3mzkxNcjsILXLbsLic6rIzX9SMT2yiqUzE7HP552a\njBoIsZxjP2g0gK2dgW2NviCOFXLa7Th37V1yIpW0EgiMSlDQOVVWuy/cxvHBhRmJRZlKPkhAMLMr\nzexRMxs3s5tiXn+fmT1rZg9FPx8Msd9e2zI6w93nHuLB353k7nMP6Ysbo9EAtnYGtsV9cRzj4TXv\ny33nW7v+4r5TY9diHhua0zlVUo+tuoZdF3+GoyOrcYyjI6vZdfFnCnFOJ24yMrNB4AvAvwEmgJ+Z\n2Q53f6Ru02+6+/VJ9yf5EreSWLsD26pfkCKm57Xr4FT8PZeSE8qtqFPJh+hDuAwYd/cnAMzsDuBq\noD4gSAklXbKyqF+cdp192jzPTC2++Cs5QfIoREBYBTxd8/sE8IaY7f7YzN4EPAb8Z3d/un4DM9sK\nbAVYvXx5gKJJGrSSWGNvOf8lvr5nhGYdySJ5EaIPwWKeq88l/B6w1t3/NbALuD3ug9x9u7tf6u6X\nrhjVcH0ptgNPTfLth5ew8Cvi/PtTf6u+A8mlEAFhAjin5vfVwMHaDdz9OXd/Kfr1r4HXB9ivSK7t\n3jfOzPHFHco/ntFMspJPIQLCz4DzzezVZnYScC2wo3YDM6ttT7gKOBBgv4VWhInzJJlGqbe96FDW\n+SQhJO5DcPdZM7seuAcYBL7s7vvN7BPAHnffAXzYzK4CZoEjwPuS7rfIijJxniQzOrIkNiiE7lDW\n+SShBBmH4O473f0Cd/9dd781eu7mKBjg7n/m7q9x99e6+1vc/Zch9ltUmua4P2zasI6R4d7PTaTz\nSULRSOUMaOK8/rB+zRh//vbpno921/kkoWguowxo4rz+8Y6Lj3H5L17s6T50PvVOknUNirgmgmoI\nGdA0xxKSzqfeqK5rsHRmAsNPrGtwwTN39vS9WVJAyMjJ0QRn4CwbmNMkZ9I1TcTYG0nWNSjqmghq\nMkpZfUYIwDGPG9snZVedFjvE2gdx02aH/Px+lGRdg6KuiaAaQsq6zQhRnnm5VG8MJmeHcOxEqmio\nv2uvP78fNJqevZ11DZK8N0sKCCnrJiNEX+7y6XWqqFJRk0uyrkFR10RQQEhZo8yPZhkh+nKXT69T\nRZWKmlySdQ2KuiaC+hBS1s3SnPpyl0+vU0WVihpGkunZizi1uwJCyrpZSF1f7uKozz3H3gxsXLRd\nr9fsvmH5NLccWsbxmplWh3GlogZUxHEGrSggZKDThdR7ffGQMKq559V0w6UzE/AP32L41HmOn7Jw\ngt9ubgw65Ti1U2/7olnppVtxf+sr9n4MoNBBQQGhANK4eEhycbnnzB5nyfTdiwICdH5j0Inbjowy\nW9dFOEul30nnTXLNxhkoIEjP9fLiIWE0yjEfmHs+5ZKo36nXijrOoBVlGYkE0ijHfH5wWcol6S6b\nTdpX1HEGrSggiAQSl3vO0DDHRjenXpak8xtpIGRzRR1n0IqajEQCqbYd12aeLH3bmzn+4OIso15L\n0u9U1AV30sz6iftbK8tIRBaozz3/yEVfhwezaabptt+p2UDIvAaELLJ+ijjOoBU1GYmURKhmniJ2\nSBd1dtG8UQ1BpA15H4QUspmniAMhy5r1kzbVELqkTrf+UYTFTkLOd9WqQzqP535Zs37SpoDQhSSz\nj+bxy1RUFzxzJ++/byMf/v5K3n/fxp5doIvQHBGymafZgjt5nXm3rFk/aVOTURe67XSLq9b/10PL\n+NSzS7nxzKO57bDLozQ7EYvQHBG6madRh3ReO5zLmvWTNgWELnR7Nxb3ZQJjygcLkdaXJ2lOHTA9\nsqoyL1HM83mR1nxXee5wLmPWT9rUZNSFbkeBNvvSaH2DzqR5196qOSKtpqtm0lpXWSOgy001hC50\nezfWqFpfleZd1rL3xOxrfzr7DpGxk+Zd+8LmiAncBhmKaiNjR/6J1zxzR8Omq8/ufy8fec/Xm37+\nXXuX8Bf3ncrBqQHOPm2e//LWF3jHxcc6Lue7eZl381zds2HPqRv3vsif/f0oM8dfOfdHhue58e0v\nsuzi7vb12f3vDVU8Scjc8zkl7sa1a/0H2/LbIdTNAub1fQj1xoZmufvcQ70o7gLL3jOY2Zewvu0f\nKnfbna4mFepzOhG3T8ewmGmlj46s5itvfbDlZx54apJdDzzC7NwrWT1DgwNccclFrF8zFqbggR14\napLd+8aZnjnG6MgSNm1Yl9uy9qOPXnbyz9390m7eqxpCl7oZBVrd/tOHl/L8/AC1c9X3y/oGodr+\ns+hEjCt7XDCA9puudu8bXxAMAGbn5tm9bzy3F9n1a8ZyW7ZO5H1sSRYUEFJUrVVMzQ9wms1jBlPz\nA321vkHItv+0OxE7KWO7TVfTM/FNQ42elzDKusBNUkE6lc3sSjN71MzGzeymmNdPNrNvRq//1MzW\nhthvkdTnb0/5IMfcuHXl89x97qG+CAZQ7AFEjcpYX0foJP99dGRJR89LGEUYW5KFxAHBzAaBLwCb\ngYuAd5nZRXWbfQD4F3dfB/wl8Kmk+y2akCNJi6zIA4hip7fmlYY/B347fHpH/RibNqxjaHDheTE0\nOMCmDesSllaaKcLYkiyEqCFcBoy7+xPu/jJwB3B13TZXA7dHj+8E3mZmRh/Jc/52mh5bdQ27Lv4M\nR0dW4xhHR1b3tCM4pIVlX8yA2aFTOvq/rF8zxhWXXHSiRjA6siTXHcpFV00RXlyvqyhCTbWXQvQh\nrAKervl9AnhDo23cfdbMpoAzgMMB9l8IRZwwrFeKPICoWvYPf38lcReVbu4wy9JJm3dxWWK1ilJT\n7aUQNYS4O/36b0o722BmW81sj5ntOTxdroybpCtYSb4UuS+kX8X1G0DlQlSkmmovhaghTADn1Py+\nGjjYYJsJMxsCTgOO1H+Qu28HtkNlHEKAsuVGkhWspLks0gd3X7gtdhxEv99h5lnj2pudGDPS76mo\nIQLCz4DzzezVwDPAtcC767bZAVwH/F/gGuA+z+uIuB7qdgUraSyr9EFNplY8rUa3KxU1QECI+gSu\nB+6hMk7+y+6+38w+Aexx9x3A3wD/w8zGqdQMrk26XxFId5K7ekXuC+lHrWp1WZ5LeRFkYJq77wR2\n1j13c83jY8A7Q+xLpJbSB6VdrWp1Opc0UlkKrghTU0t+NKvV6VzS9NeSc62mli7yQDfJF51LqiFI\njrXTyafOXQlF55ICguRYu5186tyVULo5l8qUqqqAILlV5E6+Ml0kpLGypaqqD0Fyq6ijgasXiaUz\nExh+4iKRxdKa/SKrZUzLNmuqAoLkVq86+Xp98SjbRSLvsgzARa7FxlFAkNzqxcyoaVw8ynaRCC10\nQM4yABe1FtuI+hAk10J3GKcxGlX57I31os09ywBctjmtVEOQvpLGxUP57I314m4+y7v0Iq/vEUc1\nBOkrady9K5+9sV4E5Kzv0suU9qyAIH0lrYtHmS4SIfUiICsAh6OAIH1FF49s9SogKwCHoYAgfUcX\nj+z0c0AuwmBFBQSRHCjCxSKUfgzIRRnRrIAgkrGiXCySahX0yhwUi7L4jtJORTLWDyObWw0ILPt0\nH0UZrKgagkiXQt3RFuVikUSjoPdvH76eKx/6U9wGGPC5Ra/n7Q66W0UZrKgagkgXQt7Rlm36gziN\ngtuAz2H4omDQ6n1FU5TBigoIIl0I2cwTd7FwjNGZiVRn7uylboNbWYJiUUY0q8lIpA31zUOjMdV/\n6O6OdmEq5gRgGA6Up4M5bvxBK9U76LJ0Nhchu0o1BJEW4pqHwGK37faO9rFV1/CVtz7I9MjqE8Gg\nqgwdzPV3yPM2GLvdvA0uuIMGSt3ZnDcKCCItxDUPGY7XBYUQbcJl7mCuBr3P/btD3PPazy9qJpu1\nYV4aOm3Bc/2QgZUnajISaaHxxdg5OrI6aFNGL7JRkja59KLJpn7E8szwMk6afZGR40eAV5rKhho0\nMZUhQOaRAoJIC40v0qv5ylsfDLqvuLb22g7mbi7mSQa99XLQXG2b+vvv28irjv/LgteH52aYt0Es\nJgOpLJ3NeaMmI5EW0kwZXNjWXgkGFjVOddN+nrTJJa0mm0Z3/OZzPWmak3gKCFIavVorOe2UwZAd\nzEn7JNLq02h8x28LjoFj7F91be6zdYpKTUZSCr2eDyiLlMEQF+NGzV0zw8sSvT90k02jprL6gGg4\n5z17L/876N6lSjUEKYUyZqOEGMG8+8JtzNrwoudPmn2xrRpUWs1lcbUw6oJBlTqUeydRQDCz5WZ2\nr5n9Kvr39AbbzZnZQ9HPjiT7FIlTxnTNEBfjx1Zdw/Gh0UXPD/nLXPnQn7RsWkuzuaw2LbXaZBZH\nHcq9k7TJ6CbgH939k2Z2U/T7jTHbzbj76xLuS6Shokwe1olOFpM58NQku/eNMz1zjNGRJWzasI71\na8YAWFKXvVNV21Fdu7+4cmTRZp/1Wsn9KGlAuBq4PHp8O/BD4gOCSE+V9eLRzsX4wFOT7HrgEWbn\n5gGYnjnGrgceAWD9mrGGwbIqr7OK9vPqallJGhB+x90nAdx90sxWNthuiZntAWaBT7r7XXEbmdlW\nYCvA6uXLExZN+kk/Xzx27xs/EQyqZufm2b1vnPVrxtqaRyivTWud1k7KMu9RVloGBDPbBZwV81In\nt15r3P2gmZ0H3Gdme9398fqN3H07sB1g49q18T1KIg0UYfKwXpieOdb0+frJ8+JmYSpy01pVv6w8\n10stO5Xd/Qp33xDz813gN2Y2BhD9e6jBZxyM/n2CSrPSxmD/A5E+NzqypOXz1Q7bf3jdXxViXv5u\nlDHTLG1J0053ANdFj68Dvlu/gZmdbmYnR49XAJuARxLuV0QimzasY2hw4Vd5aHCATRvWLdq2KPPy\nd6OMmWZpS9qH8EngW2b2AeAp4J0AZnYp8CF3/yCwHvjvZjZPJQB90t0VEEQiSdu9q9lEjbKM6pW1\naa2MmWZpSxQQ3P054G0xz+8BPhg9/j/AxUn2I1JWodq9168ZaxgA+kVZM83SpJHKIhlSu3c4ZW4O\nS4vmMhLJkNq9wyprc1haVEMQyVCI+YpEQlFAEMlQmmstiLSiJiORDPXzCGvJHwUEkRpZTH2gdm/J\nCwUEkYimPpB+pz4EkUgeUkB7tQyoSDtUQxCJZJ0CqhqKZE01BJFI1imgWdRQVCORWgoIIpGsU0DT\nrqFUayRLZyYw/ESNREGhfykgiESynvogdA2l1d1/HvpMJF/UhyBSI8sU0JCTs7XTH5F1n4nkj2oI\nIjkRsobSzt1/1n0mkj+qIYjkSLs1lFYD6Nq5+9d00VJPAUGkYNppDmpnsRhNmyH1FBBECqZZc1D1\nYt7u3b+mzZBaCggiBdNOc1CIu/8s5nWSbCkgiBRMu2sHJ7n716jp/qQsI5GCSWMAncYo9CfVEEQK\nJo3OYI1R6E8KCCIF1OvO4HabpaRc1GQkIotkPa+TZEM1BBFZRGMU+pMCgojE0hiF/qMmIxERARQQ\nREQkooAgIiJAwoBgZu80s/1mNm9mlzbZ7koze9TMxs3spiT7FBGR3khaQ9gH/Afg/kYbmNkg8AVg\nM3AR8C4zuyjhfkVEJLBEWUbufgDAzJptdhkw7u5PRNveAVwNPJJk3yIiElYafQirgKdrfp+InhMR\nkRxpWUMws13AWTEvbXP377axj7jqgzfY11ZgK8Dq5cvb+GgREQmlZUBw9ysS7mMCOKfm99XAwQb7\n2g5sB9i4dm1s0BARkd5Io8noZ8D5ZvZqMzsJuBbYkcJ+RUSkA0nTTv/IzCaANwLfN7N7oufPNrOd\nAO4+C1wP3AMcAL7l7vuTFVtEREJLmmX0HeA7Mc8fBLbU/L4T2JlkXyIi0lsaqSwiIoACgoiIRBQQ\nREQEUEAQEZGIAoKIiAAKCCIiElFAEBERQAFBREQiCggiIgIoIIiISMTc8zmpqJk9CzzZYrMVwOEU\nitOtvJcPVMZQVMYw8l7GvJcP4EJ3H+3mjYnmMuoldz+z1TZmtsfdG67lnLW8lw9UxlBUxjDyXsa8\nlw8qZez2vWoyEhERQAFBREQiRQ8I27MuQAt5Lx+ojKGojGHkvYx5Lx8kKGNuO5VFRCRdRa8hiIhI\nIIUKCGb2TjPbb2bzZtawp9/M/tnM9prZQ0l63HtYvivN7FEzGzezm9IqX7Tv5WZ2r5n9Kvr39Abb\nzUXH7yEzS2UN7FbHxcxONrNvRq//1MzWplGuDsv4PjN7tubYfTDl8n3ZzA6Z2b4Gr5uZfS4q/y/M\n7JI0y9dmGS83s6maY3hzyuU7x8x+YGYHou/zR2K2yfQ4tlnGzo+juxfmB1gPXAj8ELi0yXb/DKzI\nY/mAQeBx4DzgJOBh4KIUy/hp4Kbo8U3Apxps90LKx67lcQH+FPhi9Pha4Js5LOP7gM+nfe7V7P9N\nwCXAvgavbwHuBgz4PeCnOSzj5cDfZ3gMx4BLosejwGMxf+dMj2ObZez4OBaqhuDuB9z90azL0Uib\n5bsMGHf3J9z9ZeAO4Orel+6Eq4Hbo8e3A+9Icd/NtHNcast+J/A2M7OclTFT7n4/cKTJJlcDX/OK\nnwDLzGwsndJVtFHGTLn7pLs/ED2eBg4Aq+o2y/Q4tlnGjhUqIHTAgf9lZj83s61ZF6bOKuDpmt8n\nCPCH7MDvuPskVE4qYGWD7ZaY2R4z+4mZpRE02jkuJ7Zx91lgCjgjhbIt2n+k0d/uj6NmhDvN7Jx0\nita2rM+/dr3RzB42s7vN7DVZFSJqltwI/LTupdwcxyZlhA6PY+5GKpvZLuCsmJe2uft32/yYTe5+\n0MxWAvea2S+ju5I8lC/ujjZoqlezMnbwMWuiY3gecJ+Z7XX3x8OUMFY7x6Xnx66Fdvb/PeAb7v6S\nmX2ISo3mrT0vWfuyPobteAA4191fMLMtwF3A+WkXwsxOBb4NfNTdj9a/HPOW1I9jizJ2fBxzFxDc\n/YoAn3Ew+veQmX2HSlU/SEAIUL4JoPaucTVwMOFnLtCsjGb2GzMbc/fJqIp7qMFnVI/hE2b2Qyp3\nIL0MCO0cl+o2E2Y2BJxGuk0PLcvo7s/V/PrXwKdSKFcnen7+JVV7YXP3nWb238xshbunNoeQmQ1T\nudD+rbv/z5hNMj+OrcrYzXEsXZORmZ1iZqPVx8AfArHZDBn5GXC+mb3azE6i0jmaShZPZAdwXfT4\nOmBRrcbMTjezk6PHK4BNwCM9Llc7x6W27NcA93nUe5aSlmWsa0e+ikrbbp7sAP5jlCXze8BUtQkx\nL8zsrGrfkJldRuU69VzzdwXdvwF/Axxw98802CzT49hOGbs6jmn2jAfoWf8jKpH5JeA3wD3R82cD\nO6PH51HJ/ngY2E+lKSc35fNXMhQeo3LHnVr5on2fAfwj8Kvo3+XR85cCX4oe/z6wNzqGe4EPpFS2\nRccF+ARwVfR4CfB3wDjwT8B5GZyDrcr459F59zDwA+BfpVy+bwCTwPHoXPwA8CHgQ9HrBnwhKv9e\nmmTrZVjG62uO4U+A30+5fH+pUwqbAAAAS0lEQVRApfnnF8BD0c+WPB3HNsvY8XHUSGUREQFK2GQk\nIiLdUUAQERFAAUFERCIKCCIiAiggiIhIRAFBREQABQQREYkoIIiICAD/H/x8L9bCXqfxAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbee2588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#控制决策树:一个节点最少有10个样本才可能继续往下分\n",
    "tree_classifier=DecisionTreeClassifier(min_samples_split=10)\n",
    "tree_classifier.fit(X,y)\n",
    "plot_decision_boundary(tree_classifier,axis=[-1.6,2.5,-1.2,1.7])\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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3X2QXXWd5/H3tx+gW+gQQsikSYjI\nBNhAWOVhcZzMKCo7SzIuODtYhQ+1aEmlnFlQ160VZqnCKqusUafKKZ9m3Iyj4jolODhiHMOwZNBV\ns6Nj5MEkRLBhB+ikNUCGpMEO9MN3/7jnxtu3z308v3ue7udV1ZXb9557zy+nzz3f8/v+nszdERER\nGci6ACIikg8KCCIiAiggiIhIRAFBREQABQQREYkoIIiICKCAICIiEQUEEREBFBBERCQylHUBGjlt\nbMzXnXZa1sUQkR4bXAGHZvRdD+Wpn93/jLuf3s17cxsQ1p12Gt+55ZasiyEiPbb87YN8ct87si5G\nabz/shOf6Pa9ShmJiAiggCAiIhEFBBERARQQREQkooAgIiKAAoKIiEQUEEREBFBAEBGRiAKCiIgA\nCggiIhIJEhDM7AtmdsjM9jZ4/XIzO2JmD0Y/t4bYr4iIhBNqLqMvAZ8Bvtxkm++7+5sC7U9ERAIL\nUkNw9+8Bh0N8loiIZCPNNoTXmNlDZna3mV0Qt4GZbTWz3Wa2+5np6RSLJiIiaQWE+4GXu/srgU8D\nd8Vt5O7b3P1Sd7905dhYSkWTstkxPcrmJ1Zx0WPjbH5iFTumR7MukkghpBIQ3P2ouz8fPd4BDJvZ\nyjT2Lf1lx/QoH376FKbmhnCMqbkhPvz0KYUICgpkkrVUAoKZrTYzix5fFu332TT2Lf3l04fHOOaL\nT+tjPsCnD+e7xlnkQCblEaSXkZl9FbgcWGlmk8CHgGEAd/8ccA3wR2Y2B8wA17q7h9i3SK1fzA12\n9HxeNAtkW8ZmMiqV9JsgAcHd39ri9c9Q6ZYqObBjepRPHx7jF3ODrB6a58YV06W56Kwemmdqbulp\nvXpoPoPStK+ogUzKRSOV+0zZUxM3rphmxBYWPTdiC9y4It+91hoFrLwHMikXBYQ+U9Qce7u2jM1w\n6+lHGB+aw3DGh+a49fQjua8BFTWQSbmEGqksBdEPqYktYzO5DwD1quUtaypPikEBoc8UNcfeD4oY\nyKRclDLqM0pNiEgjqiH0mX5KTZS5N5VILygg9KF+SE1Ue1NVG9CrvamA0v/fRbqllJGUUtl7U4n0\ngmoIUkqd9qZSeklENQQpqU4GepV9sJ5IuxQQpJQ66U2l9JJIhVJGOaPURRid9Kbqh8F6Iu1QQMgR\n9YwJq93eVBqsJ1KhlFGOKHWRDQ3WE6lQDSFHlLrIRj8N1hNpRgEhR5S6yE4/DNYTaUUpoxxR6kJE\nsqQaQo4odSEiWVJAyBmlLkQkKwoIAWjsQL7p7yPSHgWEhDR2IB8aXfT19xFpnxqVE9LYgew1m4tI\nfx+R9ikgJKSxA9lrdtHX30ekfQoICXUyq6b0RrOLvv4+Iu1TQEhIYwey1+yif+OKaYbxRc8P4/r7\niMRQQEhoy9gMt55+hPGhOQxnfGiOW08/ogbLFLUKyl4XEOp/F5EK9TIKoIhjB8rUFbPZgL7NT6xi\nru6+Z45K+0JR/78ivaKAUBAhL+Bl7IrZKCirUVmkfUFSRmb2BTM7ZGZ7G7xuZvYpM5sws5+a2cUh\n9tsvQi/x2E9dMdWoLNK+UG0IXwKubPL6ZuCc6Gcr8JeB9tsXQl/A++muWY3+Iu0LEhDc/XvA4Sab\nXA182St+CCw3s/EQ++4HoS/g/XTXrEZ/kfal1YawBniq5vfJ6LmplPZfaKHXSbhxxfSiNgTo/K65\nSI3SRWz0F8lCWgHBYp5b0vfPzLZSSSmxdsWKXpepMEJcwGt1M812bQBYZgv8ygeYjf6sZWiUFpH0\nAsIkcGbN72uBg/Ubufs2YBvARWedpc7ikV6sk9DJXXN9r6QjvjRVVW3TUEAQKa60AsJ24AYzux14\nNXDE3ZUu6kCWaY+4Ru04ZWyUFuknQQKCmX0VuBxYaWaTwIeAYQB3/xywA9gCTAC/At4VYr+SjnYv\n9GVslBbpJ0ECgru/tcXrDvyXEPuS9DVq1K6lrpwixae5jKSluL78QyywfGAew1k+MM+JOLccWs7m\nJ1Z1PWBOGtsxPcrmJ1Zx0WPjOsbSM5q6Qlpq1qhdxmkw8mbH9CgfOrR8Ua+uDx1aDugYS1gKCH2u\n3fEEjRq1m42iLuPFKovxFx9/ZtnxYFA1i/HxZ5aV8hhLdhQQ+liIu/vQo6jzPOAtq9rQcwvxmd1G\nz4t0S2dUH2t0d/+xp5e1/Rkhp8EIPYlfaP00KaD0JwWEPhHXKNnoLv6ID7R9EQ45eVzeL7hZTQp4\nSt3xbfW8SLcUEPpAozvvZQ0vKNb2RTjk5HGhLri96pGT1aSAN51+lCGW9vK66fSjPd2v9B+1IfSB\nRnfeIwPz4E7cVFOdXIRDjaIOMYlfL/P8vzN6jL+dPonFx8uZmhtk8xOretbe0YupS0TiKCD0gYap\noYUBlg8s8NxC5fWrBn7AB4e+xhn2DL/kNIZf+D1mT7oktXKGmMSvV72edkyP8q3nX0Z9MCClCf40\nY6ukQSmjPtAs1fHBlUcZsQWuGvgBHx3+PGsHnmHAYNye5WVH7mT4hZ+kVs4Q6ade5fnj53NaXLPK\nU3uHSDdUQ8hYGt0sm915V/f1+iN38DJ7adH7zGeZf/H73LbpU0HL08q1NY+fBm7r4L0nH/w+0zPH\nlj4/Osptr7qu6zJNPXZve9vNDS3az/su+ArP/Y3meJJiUA0hQ2l1s2x1571lbIbV9mzse8dmDgQt\nS69t2rieocHFp/XQ4ACbNq5P9LljoyNBtxPJI9UQMpTmKN9WOeiFweUMzj+35Pnp0TVBy9FrG9ZV\nVmbdtXeC6ZljjI2OsGnj+uPPd2vTxvXsvP9h5uYbd/UMEXhEsqSAkKE8LXZ/bGwzJ0zfxfD8r4PG\n7OAou867JfWyJLVh3XjiABD3mbA40Lxi9Ur+3y+eCRp4RLKkgJCh0GslJzF70iV876zXsumRjzA2\nc4Dp0TXsOu8WHl1zTeplyateBBqRPFFAyFDotZKTenTNNQoAIn1MASFDeRlwVO3pNPXYvUp9iPQx\nBYSMhRhwlKTrav3I3umZY+y8/2EABQWRPqNupwWXtOtqXE+nufkFdu2d6EVxRSTHFBAKLukMoY16\nNMUN7hKRclNAKLikXVcb9WjSAKtsaO1kyZICQsElnZI5bj0DDbDKRt4XCJLyU0Boogh3a0kXqKmd\n1gIqNYMrLj5fDco90uycyvsCQVJ+6mXUQFbr53YqRNfVak+nJJO/JXHugTv7YkBcq3MqTyPXJZmi\nntMKCA2kOc9QUkWeK/91ez7IK5/8EoYDsGxmkiv2fACgEF+gTrQ6p/I0cl26d+6BO7lizweOTwNT\npHNaKaMGdLfWvv1PTvH5Hd/nz79+L5/f8X32PznV1vvOPXDnomBQNTw/w6ZHPtKLomaq1TkVcn1q\nyc6mRz6yaE4wKM45rRpCA7pba8/+J6cWzQLaycC2TY98ZEkwqCratNvtaHVO5WXkuiTT6Nwtwjmt\ngNBA3uYZyqtdeyeWTAldHdjWKiA0+4IUbdrtZo5PDTI3SO2ym7D0nCpy+k8qpkfXsGxmMvb5vFPK\nqIEQyzn2g0YD2NoZ2NboC+JYIafdjnPXnpHjXUkrgcCoBAWdU2W167xbmB1c3COxKFPJBwkIZnal\nmT1iZhNmdnPM6+80s6fN7MHo5/oQ++21LWMz3P3yQzzwm1Pc/fJD+uLGaDSArZ2BbXFfHMd4aN07\nc9/41q4/u+/k2LWYx4fmdU6V1KNrrmHnhZ/g6OhaHOPo6Fp2XviJQpzTiVNGZjYIfBb498Ak8GMz\n2+7uD9dteoe735B0f5IvcSuJtTuwrfoFKWL3vHYdPBJ/z6XOCeVW1KnkQ7QhXAZMuPvjAGZ2O3A1\nUB8QpISSLllZ1C9Ou844ZYEDR5Ze/NU5QfIoREBYAzxV8/sk8OqY7f7QzF4LPAr8V3d/qn4DM9sK\nbAVYu2JFgKJJGrSSWGOvP+dFvrJ7lGYNySJ5EaINwWKeq+9L+C3gLHf/t8BO4La4D3L3be5+qbtf\nunJMw/Wl2PY/OcXXHxph8VfE+Y8n/0ptB5JLIQLCJHBmze9rgYO1G7j7s+7+YvTrXwGXBNivSK7t\n2jvBzOzSBuUfzGgmWcmnEAHhx8A5ZvYKMzsBuBbYXruBmdXmE64C9gfYb6EVYeI8SaZR19teNCjr\nfJIQErchuPucmd0A3AMMAl9w931m9mFgt7tvB95rZlcBc8Bh4J1J91tkRZk4T5IZGx2JDQqhG5R1\nPkkoQcYhuPsOdz/X3X/T3T8SPXdrFAxw9z9x9wvc/ZXu/np3/1mI/RaVpjnuD5s2rmd0uPdzE+l8\nklA0UjkDmjivP2xYN86fvmm656PddT5JKJrLKAOaOK9/vPnCY1z+0xd6ug+dT72TZF2DIq6JoBpC\nBjTNsYSk86k3qusaLJuZxPDj6xqce+DOnr43SwoIGTkxmuAMnOUD85rkTLqmiRh7I8m6BkVdE0Ep\no5TV9wgBOOZxY/uk7KrTYodY+yBu2uyQn9+PkqxrUNQ1EVRDSFm3PULUz7xcqjcGU3NDOHa8q2io\nv2uvP78fNJqevZ11DZK8N0sKCCnrpkeIvtzl0+uuouqKmlySdQ2KuiaCAkLKGvX8aNYjRF/u8ul1\nV1F1RU0uyboGRV0TQW0IKetmaU59ucun111F1RU1jCTTsxdxancFhJR1s5C6vtzFUd/3HHsdcNGS\n7Xq9ZveNK6b50KHlzNbMtDqMqytqQEUcZ9CKAkIGOl1IvdcXDwmj2ve82t1w2cwk/MPXGD55gdmT\nFk/w282NQaccp3bqbV8yK710K+5vfcWeDwAUOigoIBRAGhcPSS6u7zlzs4xM370kIEDnNwad+PTh\nMebqmgjnqLQ76bxJrtk4AwUE6bleXjwkjEZ9zAfmn0u5JGp36rWijjNoRb2MRAJp1Md8YXB5yiXp\nrjebtK+o4wxaUUAQCSSu7zlDwxwb25x6WZLOb6SBkM0VdZxBK0oZiQRSzR3X9jxZ9sbXMfvA0l5G\nvZak3amoC+6k2esn7m+tXkYiskh93/P3nf8VeCCbNE237U7NBkLmNSBk0euniOMMWlHKSKQkQqV5\nitggXdTZRfNGNQSRNuR9EFLINE8RB0KWtddP2lRD6JIa3fpHERY7CTnfVasG6Tye+2Xt9ZM2BYQu\nJJl9NI9fpqI698CdvOu+i3jvt1fxrvsu6tkFugjpiJBpnmYL7uR15t2y9vpJm1JGXei20S2uWv8/\nDi3nY08v46bTj+a2wS6P0mxELEI6InSap1GDdF4bnMva6ydtCghd6PZuLO7LBMYRHyxEt748SXPq\ngOnRNZV5iWKez4u05rvKc4NzGXv9pE0poy50Owq02ZdG6xt0Js279lbpiLRSV82kta6yRkCXW25r\nCIMrYPnbs7/riHPTnhf4k78fY2b21/F0dHiBm970AssvbFzmMz65wIEjjV//xdxgtv/nfensJkSP\nnTTv2henIyZxG2Qoqo2MH/5nLjhwe8PU1Sf3vYP3vf0rTT//rj0j/Nl9J3PwyABnnLLAf3/D87z5\nwmMdl/NtvMTbeLbu2bDnU7fnfjOf3PeOUMWThMw9n1Pirttwif+3L/9T1sVoaP+TU+zaO8H0zDHG\nRkfYtHE9G9aNt3zPzvsfZm5+Ifb1sdERrt/yu70obm7U5/6hcrfd6WpSoT6nE3H7dAyLmVb66Oha\nvviGB1p+Ztw5MTQ4wBUXn9/yfMpKN+e+pOf9l534E3e/tJv35raGkHcb1o13/CWobv/dhx7h2Euz\ni14bGhxg08b1wcqXV6Fy/1k0IsaVPS4YQPupq117J5bcIMzNL7Br70RuL7LdnPt5lPexJVlQQEhR\n9c7q2EuznDg8hJlx7KXZvrrLCpn7T7sRsZMytpu6mp6JTw01el7CKOsCN0kFaVQ2syvN7BEzmzCz\nm2NeP9HM7ohe/5GZnRViv0VSTQ1Uv+gvzs4xNz/Plf9uI9dv+d2+CAZQ7AFEjcpYX0fopP/72OhI\nR89LGEUYW5KFxAHBzAaBzwKbgfOBt5rZ+XWbvRv4V3dfD/w58LGk+y2aZqmBflLkAUSx01vz60Uq\nHfjV8KkdtWNs2rieocHFX8N+SR9mqQhjS7IQooZwGTDh7o+7+0vA7cDVddtcDdwWPb4TeKOZGX1E\nqYGKR9dcw84LP8HR0bU4xtHRtT1tCA5pcdmXMmBu6KSO/i8b1o1zxcXnH68RjI2O5LpBueiqXYSX\n1usqilBT7aUQbQhrgKdqfp8EXt1oG3efM7MjwGnAMwH2XwhjoyOxF/9+TA0UeQBRtezv/fYq4i4q\n3dxhlqWRNu/ieonVKkpNtZdC1BDi7vTrvyntbIOZbTWz3Wa2+/nnyhUrlBoolyK3hfSruHYDqFyI\nilRT7aUQNYRJ4Mya39cCBxtsM2lmQ8ApwOH6D3L3bcA2qIxDCFC23KjeAar/dnhZdB/cdd4tseMg\n+v0OM88a197s+JiRfu+KGiIg/Bg4x8xeARwArgXeVrfNduA64J+Aa4D7PK8j4npIqYHwsuo+qMnU\niqfV6HZ1RQ0QEKI2gRuAe6iMk/+Cu+8zsw8Du919O/DXwP8yswkqNYNrk+5XBNKd5K5ekdtC+lGr\nWl2W51JeBBmY5u47gB11z91a8/gY8JYQ+xKppe6D0q5WtTqdSxqpLAVXhKmpJT+a1ep0Lmn6a8m5\nVlNLF3mgm+SLziXVECTH2mnkU+OuhKJzSdNfS469676LYqvw7U4tLZKGvHVV1fTXUkpFbuTL20VC\neqNsXVXVhiC5VdTRwNWLxLKZSQw/fpHIYmnNfpHVMqZlmzVVAUFyq1eNfL2+eJTtIpF3WQbgItdi\n4yggSG71YmbUNC4eZbtIhBY6IGcZgItai21EbQiSa6FHA6cxGlX92RvrRc49ywBctjmtVEOQvpLG\nxUP92Rvrxd18lnfpRV7fI45qCNJX0rh7V3/2xnoRkLO+Sy/TnFYKCNJX0rp4lOkiEVIvArICcDgK\nCNJXdPHIVq8CsgJwGAoI0nd08chOPwfkIgxWVEAQyYEiXCxC6ceAXJQRzQoIIhkrysUiqVZBr8xB\nsSiL76jbqUjG+mFkc6sBgWWf7qMogxVVQxDpUqg72qJcLJJoFPT+w0M3cOWDf4zbAAM+v+T1vN1B\nd6sogxVVQxDpQsg72rJNfxCnUXAb8HkMXxIMWr2vaIoyWFEBQaQLIdM8cRcLxxibmUx15s5e6ja4\nlSUoFmVEs1JGIm2oTw+NxVT/obs72sVdMScBw6gsXFWWBua48QetVO+gy9LYXITeVaohiLQQlx4C\ni9222zvaR9dcwxff8ADTo2uPB4OqMjQw198hL9hg7HYLNrjoDhoodWNz3iggiLQQlx4yHK8LCiFy\nwmVuYK4GvU/9/iHueeVnlqTJ5myYF4dOWfRcP/TAyhOljERaaHwxdo6Org2ayuhFb5SkKZdepGzq\nRyzPDC/nhLkXGJ09DPw6VTbUIMVUhgCZRwoIIi00vkiv5YtveCDovuJy7bUNzN1czJMMeuvloLna\nnPq77ruIl83+66LXh+dnWLBBLKYHUlkam/NGKSORFtLsMrg4114JBhYlp7rJnydNuaSVsml0x28+\n35PUnMRTQJDS6NVayWl3GQzZwJy0TSKtNo3Gd/y26Bg4xr411+a+t05RKWUkpdDr+YCy6DIY4mLc\nKN01M7w80ftDp2wapcrqA6LhnP30vfyfoHuXKtUQpBTK2BslxAjmXefdwpwNL3n+hLkX2qpBpZUu\ni6uFURcMqtSg3DuJAoKZrTCze83s59G/pzbYbt7MHox+tifZp0icMnbXDHExfnTNNcwOjS15fshf\n4soH/6hlai3NdFltt9RqyiyOGpR7J2nK6GbgH939o2Z2c/T7TTHbzbj7qxLuS6Shokwe1olOFpPZ\n/+QUu/ZOMD1zjLHRETZtXM+GdeMAjNT13qmqbaiu3V9cObLI2We9VnI/ShoQrgYujx7fBnyX+IAg\n0lNlvXi0czHe/+QUO+9/mLn5BQCmZ46x8/6HAdiwbrxhsKzK66yi/by6WlaSBoTfcPcpAHefMrNV\nDbYbMbPdwBzwUXe/K24jM9sKbAU4dfW6hEWTftLPF49deyeOB4OqufkFdu2dYMO68bbmEcpraq3T\n2klZ5j3KSsuAYGY7gdUxL3Vy67XO3Q+a2dnAfWa2x90fq9/I3bcB2wDWbbgkvkVJpIEiTB7WC9Mz\nx5o+Xz95XtwsTEVOrVX1y8pzvdSyUdndr3D3jTE/3wR+aWbjANG/hxp8xsHo38eppJUuCvY/EOlz\nY6MjLZ+vNtj+w6v+shDz8nejjD3N0pa02+l24Lro8XXAN+s3MLNTzezE6PFKYBPwcML9ikhk08b1\nDA0u/ioPDQ6waeP6JdsWZV7+bpSxp1nakrYhfBT4mpm9G3gSeAuAmV0KvMfdrwc2AP/TzBaoBKCP\nursCgkgkad672puoUS+jemVNrZWxp1naEgUEd38WeGPM87uB66PH/xe4MMl+RMoqVN57w7rxhgGg\nX5S1p1maNFJZJEPKe4dT5nRYWjSXkUiGlPcOq6zpsLSohiCSoRDzFYmEooAgkqE011oQaUUpI5EM\n9fMIa8kfBQSRGllMfaC8t+SFAoJIRFMfSL9TG4JIJA9dQHu1DKhIO1RDEIlk3QVUNRTJmmoIIpGs\nu4BmUUNRjURqKSCIRLLuApp2DaVaI1k2M4nhx2skCgr9SwFBJJL11Aehayit7v7z0GYi+aI2BJEa\nWXYBDTk5WzvtEVm3mUj+qIYgkhMhayjt3P1n3WYi+aMagkiOtFtDaTWArp27f00XLfUUEEQKpp10\nUDuLxWjaDKmngCBSMM3SQdWLebt3/5o2Q2opIIgUTDvpoBB3/1nM6yTZUkAQKZh21w5OcvevUdP9\nSb2MRAomjQF0GqPQn1RDECmYNBqDNUahPykgiBRQrxuD201LSbkoZSQiS2Q9r5NkQzUEEVlCYxT6\nkwKCiMTSGIX+o5SRiIgACggiIhJRQBARESBhQDCzt5jZPjNbMLNLm2x3pZk9YmYTZnZzkn2KiEhv\nJK0h7AX+E/C9RhuY2SDwWWAzcD7wVjM7P+F+RUQksES9jNx9P4CZNdvsMmDC3R+Ptr0duBp4OMm+\nRUQkrDTaENYAT9X8Phk9JyIiOdKyhmBmO4HVMS/d4u7fbGMfcdUHb7CvrcBWgFNXr2vjo0VEJJSW\nAcHdr0i4j0ngzJrf1wIHG+xrG7ANYN2GS2KDhoiI9EYaKaMfA+eY2SvM7ATgWmB7CvsVEZEOJO12\n+gdmNgm8Bvi2md0TPX+Gme0AcPc54AbgHmA/8DV335es2CIiElrSXkbfAL4R8/xBYEvN7zuAHUn2\nJSIivaWRyiIiAiggiIhIRAFBREQABQQREYkoIIiICKCAICIiEQUEEREBFBBERCSigCAiIoACgoiI\nRMw9n5OKmtnTwBMtNlsJPJNCcbqV9/KByhiKyhhG3suY9/IBnOfuY928MdFcRr3k7qe32sbMdrt7\nw7Wcs5b38oHKGIrKGEbey5j38kGljN2+VykjEREBFBBERCRS9ICwLesCtJD38oHKGIrKGEbey5j3\n8kGCMua2UVlERNJV9BqCiIgEUqiAYGZvMbN9ZrZgZg1b+s3sX8xsj5k9mKTFvYflu9LMHjGzCTO7\nOa3yRfteYWb3mtnPo39PbbDdfHT8HjSzVNbAbnVczOxEM7sjev1HZnZWGuXqsIzvNLOna47d9SmX\n7wtmdsjM9jZ43czsU1H5f2pmF6dZvjbLeLmZHak5hremXL4zzew7ZrY/+j6/L2abTI9jm2Xs/Di6\ne2F+gA3AecB3gUubbPcvwMo8lg8YBB4DzgZOAB4Czk+xjB8Hbo4e3wx8rMF2z6d87FoeF+CPgc9F\nj68F7shhGd8JfCbtc69m/68FLgb2Nnh9C3A3YMBvAT/KYRkvB/4+w2M4DlwcPR4DHo35O2d6HNss\nY8fHsVA1BHff7+6PZF2ORtos32XAhLs/7u4vAbcDV/e+dMddDdwWPb4NeHOK+26mneNSW/Y7gTea\nmeWsjJly9+8Bh5tscjXwZa/4IbDczMbTKV1FG2XMlLtPufv90eNpYD+wpm6zTI9jm2XsWKECQgcc\n+N9m9hMz25p1YeqsAZ6q+X2SAH/IDvyGu09B5aQCVjXYbsTMdpvZD80sjaDRznE5vo27zwFHgNNS\nKNuS/Uca/e3+MEoj3GlmZ6ZTtLZlff616zVm9pCZ3W1mF2RViCgteRHwo7qXcnMcm5QROjyOuRup\nbGY7gdUxL93i7t9s82M2uftBM1sF3GtmP4vuSvJQvrg72qBdvZqVsYOPWRcdw7OB+8xsj7s/FqaE\nsdo5Lj0/di20s/9vAV919xfN7D1UajRv6HnJ2pf1MWzH/cDL3f15M9sC3AWck3YhzOxk4OvA+939\naP3LMW9J/Ti2KGPHxzF3AcHdrwjwGQejfw+Z2TeoVPWDBIQA5ZsEau8a1wIHE37mIs3KaGa/NLNx\nd5+KqriHGnxG9Rg+bmbfpXIH0suA0M5xqW4zaWZDwCmkm3poWUZ3f7bm178CPpZCuTrR8/MvqdoL\nm7vvMLO/MLOV7p7aHEJmNkzlQvs37v53MZtkfhxblbGb41i6lJGZnWRmY9XHwO8Bsb0ZMvJj4Bwz\ne4WZnUClcTSVXjyR7cB10ePrgCW1GjM71cxOjB6vBDYBD/e4XO0cl9qyXwPc51HrWUpalrEuj3wV\nldxunmwH/nPUS+a3gCPVFGJemNnqatuQmV1G5Tr1bPN3Bd2/AX8N7Hf3TzTYLNPj2E4ZuzqOabaM\nB2hZ/wMqkflF4JfAPdHzZwA7osdnU+n98RCwj0oqJzfl81/3UHiUyh13auWL9n0a8I/Az6N/V0TP\nXwp8Pnr828Ce6BjuAd6dUtmWHBfgw8BV0eMR4G+BCeCfgbMzOAdblfFPo/PuIeA7wL9JuXxfBaaA\n2ehcfDfwHuA90esGfDYq/x5P93h+AAAAXElEQVSa9NbLsIw31BzDHwK/nXL5fodK+uenwIPRz5Y8\nHcc2y9jxcdRIZRERAUqYMhIRke4oIIiICKCAICIiEQUEEREBFBBERCSigCAiIoACgoiIRBQQREQE\ngP8PYpYwFZJPuyUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbbd6780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 叶子结点至少要有多少个样本\n",
    "tree_classifier=DecisionTreeClassifier(min_samples_leaf=5)\n",
    "tree_classifier.fit(X,y)\n",
    "plot_decision_boundary(tree_classifier,axis=[-1.6,2.5,-1.2,1.7])\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": [
    "#### 决策树的局限性：\n",
    "- 高度依赖样本数据（没有对样本数据进行任何假设，属于非参数学习），容易形成高方差。\n",
    "- 决策边界是横平竖直的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn中决策树算法解决回归问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "original_data=load_boston()\n",
    "X,y=original_data.data,original_data.target\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(379L, 13L)\n",
      "(379L,)\n"
     ]
    }
   ],
   "source": [
    "print X_train.shape\n",
    "print y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 在不对决策树进行任何限制的情况下，决策树是过拟合的\n",
    "tree_reg=DecisionTreeRegressor()\n",
    "tree_reg.fit(X_train,y_train)\n",
    "tree_reg.score(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78690410950286782"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 绘制决策树算法的学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6a67b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(algo, X_train, X_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1, len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "    \n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(mean_squared_error(y_train[:i], y_train_predict))\n",
    "    \n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(mean_squared_error(y_test, y_test_predict))\n",
    "        \n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               np.sqrt(train_score), label=\"train\")\n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               np.sqrt(test_score), label=\"test\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_learning_curve(DecisionTreeRegressor(), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJztvXmcXFWZ//8+tfa+d/alAwlZCCGQ\nEHYBZUkQQQURhBFm0CCCG8IIzoDo7zcj4ziAC6DRQRSURRQIGiREYBBIIAESsiedvdNZOkvvXfv5\n/nHu7Vq6qru6091VXf28X6963e3UvU/frvrUc5/znOcorTWCIAhCbuHItAGCIAhC/yPiLgiCkIOI\nuAuCIOQgIu6CIAg5iIi7IAhCDiLiLgiCkIOIuAuCIOQgIu6CIAg5iIi7IAhCDuLK1IWrqqp0TU1N\npi4vCIIwJHn//fcPaa2re2qXMXGvqalh1apVmbq8IAjCkEQptSuddhKWEQRByEFE3AVBEHIQEXdB\nEIQcJGMxd0EQhL4QDAapq6vD5/Nl2pQBJS8vj3HjxuF2u/v0fhF3QRCGFHV1dRQXF1NTU4NSKtPm\nDAhaaw4fPkxdXR2TJk3q0zkkLCMIwpDC5/NRWVmZs8IOoJSisrLymJ5ORNwFQRhy5LKw2xzr3yji\nLgiCkIOIuAuCIPSCxsZGHnnkkV6/79JLL6WxsXEALEqOiLsgCEIvSCXu4XC42/ctWbKEsrKygTKr\nC5ItIwiC0Avuuusutm3bxuzZs3G73RQVFTF69GhWr17Nhg0b+PSnP82ePXvw+Xx84xvfYOHChUC0\n5EpraysLFizgnHPO4Z133mHs2LG8+OKL5Ofn96udIu6CIAxZvv/SejbUN/frOWeMKeF7nzox5fH7\n77+fdevWsXr1at544w0++clPsm7dus6Uxccee4yKigo6Ojo47bTTuPLKK6msrIw7x9atW3nqqaf4\n1a9+xdVXX82f/vQnrr/++n79O0TcBUEQjoF58+bF5aL/9Kc/5fnnnwdgz549bN26tYu4T5o0idmz\nZwMwZ84cdu7c2e929SjuSqnHgMuAg1rrmUmOK+AnwKVAO3Cj1vqD/jZUEAQhke487MGisLCwc/2N\nN95g2bJlLF++nIKCAs4///ykueper7dz3el00tHR0e92pdOh+jgwv5vjC4Ap1msh8OixmyUIgpCd\nFBcX09LSkvRYU1MT5eXlFBQUsGnTJlasWDHI1kXp0XPXWr+plKrppskVwO+01hpYoZQqU0qN1lrv\n6+687YEwb209hMMBTqVwOhQOh+pcH19RQGl+32oqCIIgDBSVlZWcffbZzJw5k/z8fEaOHNl5bP78\n+fziF79g1qxZTJ06lTPOOCNjdvZHzH0ssCdmu87a10XclVILMd49haOP5/r/fTflSYu8LuZMLOeW\n84/njOMqU7YTBEEYbP7whz8k3e/1enn55ZeTHrPj6lVVVaxbt65z/x133NHv9kH/iHuyMbI6WUOt\n9SJgEcDMk0/Rj918JuGIRmtNWGvCEU1EawIhzcvr9vHi6nraAyH++JWz+sFMQRCE4UN/iHsdMD5m\nexxQ39Ob8txO5k2qSHl8/sxRzBxTyn8s2cjiNfWU5rv5zds7uPOSqZw4pvTYrRYEQchh+kPcFwO3\nKaWeBk4HmnqKt6fLVXPG8fg7O/n6Ux/idCjCEc2724/wxE3zmFuT+odBEARhuNNjtoxS6ilgOTBV\nKVWnlLpJKfUVpdRXrCZLgO1ALfAr4Kv9ZVx5oYc37jyfm86ZhAIeue5URpXm8Y2nVxMKR/rrMoIg\nCDlHOtky1/ZwXAO39ptFCbidDu65bAZf//gUSgvcOB2Km594n1c3HGDBSaMH6rKCIAhDmiFTOKy0\nwKRFXjh9JOMr8nns7R0ZtkgQBCF7GTLibuN0KG48axIrdx7lo7rBK58pCIIAfS/5C/DQQw/R3t7e\nzxYlZ8iJO8DVc8dR5HXx46VbMFGhY2N/k4/2QKgfLBMEIdcZKuI+JAuHFee5ufOSqXxv8XpeWb+f\n+TN7F3t//sM6PE4nk6oKefLdXfzh3d2MKc3j7bs+Piym7xIEoe/Elvy96KKLGDFiBM8++yx+v5/P\nfOYzfP/736etrY2rr76auro6wuEw99xzDwcOHKC+vp4LLriAqqoqXn/99QG1c0iKO8A/nTGRH7+y\nmbdqD/VK3OsbO/jWM2vi9nlcDuqbfNQd7WB8RQFPrtjFmcdXcnx1UX+bLQhCf/LyXbB/bf+ec9RJ\nsOD+lIdjS/4uXbqU5557jvfeew+tNZdffjlvvvkmDQ0NjBkzhr/+9a+AqTlTWlrKAw88wOuvv05V\nVVX/2pyEISvuDodi+pgS1sfUcv73F9ayaV8Lz90SP6L1+Q/r+Nlrtbxw69n89SOTgv/Dz55EeYGb\n4jw3BR4nn3nkHdbXN+FyKv79hXVUFXn541fOZFJVIYIgCMlYunQpS5cu5ZRTTgGgtbWVrVu3cu65\n53LHHXfwne98h8suu4xzzz130G0bsuIOcOKYEp5+bw/hiMbpUDy5YjcAoXAEl9NB3dF2Xll/gAeW\nbqYtEGbWfUspzXdz0thSrp03ofM8vmAYp0NZPxQmLNPiC3Ldr1bwl6+fS0Whp99t33W4jfpGH8V5\nLk4YWYzHNSS7PwQhs3TjYQ8GWmvuvvtubr755i7H3n//fZYsWcLdd9/NxRdfzL333juotg1xcS+l\nI7iTHYdaKYmpILnnaAel+W4ufvBN2gPReQ0dCs44roKvfXxK3Hny3E4mVxexbm8T4YjG5VA8/s/z\nuPZXK1i8ei83nm0K8YfCEb721IdMGVnM1z8+GZezb4L8t3X7+MqT0ZL3p04o47mvnIXDIfF+Qch2\nYkv+XnLJJdxzzz1cd911FBUVsXfvXtxuN6FQiIqKCq6//nqKiop4/PHH494rYZkeOHWCmWz29mfX\n0OKLZrtsPdCCQynaA2GqirwcavXzwq1nM7GigPIUXviZx1fyxIpdvL65gRPHlHDm8ZWcMLKI/1m6\nhWBYU5rvptkX5OV1+3l53X68Lge3XjA5pW3v7zrCfy7ZxPjyfL510QlMrIyGdx5/ZycTKgq4/7Mn\n8cHuo/x46RYeXLaFs46vQqNZs6eJjfuaafOHKMl3o7XmcFsAgKkjiyn0urj6tPGMLev9nItaa77/\n0gberj3EOVOqGFGcR7MvSIsviMvh4Jbzj6e6yCs/NIKQgtiSvwsWLOALX/gCZ555JgBFRUU8+eST\n1NbWcuedd+JwOHC73Tz6qJnmYuHChSxYsIDRo0cPeIeq6o9Uwr4wd+5cvWrVqmM+z0tr6vnvVzaz\n+0g7HqeDQDjCv86fij8Y4aevbWXN9y5me0Mbs8d3P+t4U0eQBQ+9SX2TjxvPquG+y0/koWVbeGjZ\n1rh2J4wsYlRpPuv2NvGvl0xlX5OP40cUccLIIqaNKgGgIxDmM4+8zab9LRR6nATDmlMmlOF1OwmG\nIizffpg7L5nKrRdMJhLRfPGx93ir9lDcdcaV51Oa76ahxY9SMLo0n2A4wub9LUS0xu108KVzJ3Hi\nmFLqGzv4YPdR8txOqou8HG4L8E7tIXyhCCNL8giGIwTDEUJhjT8U5lBrgBmjS6htaCUQiuByKErz\n3bT4QgTCEaqLvXx+7njcTgc7DrUSDGtK8l0cag2Q73ZS6HVR4HHS0OInFIlw6oRy1u5twutyUOR1\nM7Y8nzkTyzlxTAnuPj7dCEIqNm7cyPTp0zNtxqCQ7G9VSr2vtZ7b03uHvLjbtAdC+IMRFvzkH0ys\nLMAXDNMWCLPs9vPSPkdTR5DtDa1MH11CnttJqz/E39bt57wTqmnqCPDejqOcOrGM9kCYaxatIBCK\n1rfxOB1cNGMky7cf5ojlZT/8hVOZW1PO/yzdzLaGNkLhCB6XgxHFefzgihOpLDJTbWmtqT3YyqHW\nAOGIZtroYqqKvElt1FpT3+Tj3hfW8fdNBzv3j6/IJxKBhlY/RV4Xp0+qoNDrorE9iMelcDsduBwO\nPC7F8dVF3HTOJALhCJEI5LkdKKWoPdjCkrX7eW/HEd7ZdggNjCnNx+ty0NgRpLrIiy8Ups0fos0f\nprLIQyis2d/so6rIg8vhoMUXpM0KheW5HYwty6emspDvLJjGCSOLe/tvFYQuiLgPM3G3uffFdfxu\n+S4ALj1pFI9cN6ffrwFwpC1AY3uAyiIve46086NXNrP1QAun1VQweUQRcyeWc9bkgY2rtfpD7DzU\nRkWhhzF9CNF0R3sghEKR73F22y4UjrBpfwvTRhV39kHsb/KxatcRVu9upL6pg3e3H8EXDPPq7ef1\nu53C8EPEfZiKO8Cm/c3c+8J6bjn/eC6YNmJAriGkz54j7Vz4wP8xc2wpJ48rIxyJcMKoYjoCYZ5c\nsYs8t5M8t5MxZXmcVlPBOZOr8LgcVBd7KfAM6W4hYQDYuHEj06ZNy/kBh1prNm3aJOIuZDf/+9YO\nHli6GYf1hWzxmw7wUyaUUVnoxRcMU3e0nZ2H44dml+S5mDqqmEX/NDdlZ7gwvNixYwfFxcVUVlbm\nrMBrrTl8+DAtLS1MmjQp7piIu5C1aK3Z1mA6aqeOLI7LzFlb18SuI234ghEOtvjY1+jjmZV7qCzy\noIDjqou4+bzjOHl8GQpYt7eZQ61+3E6F0+GgvMCNUgqvyyHjB3KUYDBIXV0dPp8v06YMKHl5eYwb\nNw632x23X8RdyBn++tE+nlm1h8pCD2/XHuJgiz+t9zmsLKMr54xj79EOfMEw5YVujrQFCIQiPPj5\n2RTnuXs+kSBkEemKuwQ0haznk7NG88lZpn6QLxhm6YYDHGjyEYxEmD66hFEleYQjmmA4Qqs/RESb\nEcab9rXw9rZD/PTvW6ko9FBWYIS90ONib2MH7+04wiemj8zwXycIA4OIuzCkyHM7ufzkMWm1vWwW\n3MFU/KEwHqejMz7b5g8x875XWLu3ScRdyFlE3IWcx+uKT+cs9LqYXF3E2rqmDFkkCAOPiLswLDlp\nXCl//mAvD766Ba/bQTCkuXDGCE4cU5pp0wShXxBxF4Yl50yu4s8f7OWnr21Fa1AKHly2hWKvi5Gl\necweX8a8mgpmjCmhotBDeYGnxwFdgpBNSLaMMGzxBcP4gmEC4Qhep5PnP6xj5+F26o628/6uoxxt\nD8a1nzaqmPNOqKYjGCaiNcoqD62UKRStlMIfCnOg2U95gYfxFfkoFKNL87hg2giqi5OXlBCE3iDZ\nMoLQA/bIWBu7tDNAJGJy8bc1tHK0PUhDi59/bG1g0T+2U+x1dZZa0Fpju0dag8uhGFmSx+o9jZ01\nhgAKPE6Oqy6kJM+N1+XA43LgdTnxuhyUF3o6C8TlWzblu53MHFvKWZMrKXA7+1xe2kZrzZG2AC6H\nA5fT1BpyO1XWDwLyBcPsPNzG0bYg00YVy0C2XiDiLghJcDgUU0YWMyWm2NnXPzGFYDiSVqXLSEQT\nimg0mm0H23hixS72N3XQ7AvR4gvhD4UJhCL4ghEOt/mpLvKilOp8mjBPB9HzeZwOnA7VOblLkddF\nQ6ufYDiC1xqoFdGms/jKU8dyxeyxcfY88OoWfvZabRc7i/Nc3L1gOqdMKGPaqOJBFfvG9gChiMap\nFA6lUA46133BMPcuXs9La+o727scih9/7mQ+fcrYbs6aOYLhCKv3NNLUHsTtcuB2KPI8TmaNLcXl\ndLBxXzOvrN9PcZ6bikJThTUS0ZQVePjUyWNw9nOZbRF3QegF6ZYwdjgUHuvLOmNMCT/87Ekp22qt\nu4hqOKJ5bdNBdh1uoz0Qpj1gQkENLX52HGrjQLOP8kIPxXke/MGICQ0p2H24jdufXUOe24nLoSjy\nmq/4Y2/t4KzjK7lw+sjOEtCBsOatrQ1893kzB2me2zxNuC3P3uVUVBZ6+fHnZjF5xLFV9LRnS7N5\nYOlmfprkxyaRG8+q4aSxpYwo8fKTZVv57vNrqW/qoCzfg8flYPb4MiaP6L+5ju0f1wKPiweXbeFA\ns49iryl33dDqBw0oUzCv1R9Ca5g9voyx5fm8tukgHyXJwBpVkseo0jw27GuOqyQby5K1+5g9oYzl\n2w6zZk8jwbDG5VS4HAqnQxGKaMJhzdya8rT/Fom5C0IO0dge4MIH3uRQa/woXqXgr187lxljSuL2\n+0NhVmw/woFmH1v2txCKaALhCKFwhGBY8+aWBjqCYQo8TnzBCP5QmAkVBZwzuYpbPz6ZEcV5APxw\nyUYa24N886IpaA0dwTDvbj/CU+/tpqHFT0OrnwumVvPo9XNYtuEAt/z+Az45azRnTKogoo34R7R5\nhSPgdipOnVjOqROiYravqYObn3g/TkALPU5+dcNcZo8v61JkLhLRNHUEKfS6aPEFeXfHEZo7gtQ3\n+QiFI4QimvrGDmoPttLcEaQ9GDbetNZUFHg43BZgRLGpe1RV5KWq2ItDmfCb2+mg0OskFNas3HmE\nZl+I4jwX91w2g2mjigmGzaC6gy1+lny0j/ZgmBHFXr576XSC4Qht/hDFeW5cDsUTK3bxk79vJRzR\njCzxcsmJo/C6HIQimlBYE9Yat8OU15hUXcgXz6yR8gOCMBw52OJjfX0zpflu2vwhwhFNVZGXmWN7\nn+a5eX8Lj7+zA6UUeZZXv2l/C8u3H2ZEsZfTairwBcO8vG5/0vefNLaUaaOKcToUT6/cQ01lAUfa\nAkysLOTPXz2rT5O5NLUH6QiGaWjxc92vV9DsC1FW4GZiRQEBS1TDEfOU0+oP4bI831hcDoXLqagu\n9nLCiGIqCk02VKk1Xefexg4unjGK+TNHpWVTOKLRWve5b8QeYe22wm/d0a+1ZZRS84GfAE7g11rr\n+xOOTwB+C5RZbe7SWi/p7pwi7oIwdHl/1xG+/9IGjrYHiETg9OMquO70CWzY14LbYeYBKCvwcO7k\nqs7CcIvX1PP8B3Vo4N8/OaNfwim7D7fz4Z6j/H3jQZp9wc6OYpfDYQS/spBDrX5K8tyccVwFVUVe\nxpTl93t8ezDpN3FXSjmBLcBFQB2wErhWa70hps0i4EOt9aNKqRnAEq11TXfnFXEXBEHoPemKezrP\nEPOAWq31dq11AHgauCKhjQbsYF4pUI8gCIKQMdLJlhkL7InZrgNOT2hzH7BUKfU1oBC4sF+sEwRB\nEPpEOp57suBUYiznWuBxrfU44FLgCaVUl3MrpRYqpVYppVY1NDT03lpBEAQhLdIR9zpgfMz2OLqG\nXW4CngXQWi8H8oAus0NrrRdpredqredWV1f3zWJBEAShR9IR95XAFKXUJKWUB7gGWJzQZjfwCQCl\n1HSMuItrLgiCkCF6FHetdQi4DXgF2Ag8q7Ver5T6gVLqcqvZt4EvK6XWAE8BN+pMJdALgiAI6ZUf\nsHLWlyTsuzdmfQNwdv+aJgiCIPQVmRpeEAQhBxFxFwRByEFE3AVBEHIQEXdBEIQcRMRdEAQhBxFx\nFwRByEFE3AVBEHIQEXdBEIQcROZQFQRByDRNdbDxLxAJgY6ADptlJAJF1TByJqz9I1Qcn/YpRdwF\nQRAyzRv3w4dPpD5ePgmO7oATFqR9ShF3QRCETLNvDUz6GHz+96Ac4HCa5cGNsOg8I+yzroHP/hKu\neyatU0rMXRAEIZOEg9CwCcacAnkl4C0Cdz64vDBiBjg9pt2ok3p1WhF3QRCETHJoC4QDMGpW12Mu\nD1RPM+ujZvbqtCLugiAImWT/WrNM5Znboj+yd567xNyFgefAenjnZzDxLJhwpvFS1j8Pp1wP5TWZ\ntk4QMsvhWlDO1Jkwp/0LlE2AwspenVbEXRh4NiyGNU+Z16hZ0FwP7YfA4YLz78q0dYKQWRr3QMkY\ncKaQ47FzzKuXSFhGGHjaDkJeKUy+CI7sMMIO0Hogs3YJQjbQtAdKx/fcrpeIuAsDT+tBKB4D406D\nQEv8fkEY7jTugTIRd2Eo0noQikaYV9x+8dxznnAw0xZkN5EwNO+F0nH9fmqJuQsDT+sBGH86FI2M\n7queJuKeazRsNvnaDpd5rXoM6lbCN9eCp9C02fY6tB+G6ZebNL/hTss+U2pgAMIyIu7CwKI1tDVY\nnnuMuI86CTa+ZI4rlTn7hP7jmetNznYiB9bD+Hnw5n/Da/+/2Xf5z+DUL/a/DZGIiWGXT4zuaz8C\n3mJwuuPbbvwLbHgR5twANefEH2uuj6YoBtvB3wqeAhg3DzYvMRlf4QAEfeZ6YAYbjT4ZTrvJbGsN\nG14AT5Fxbna9DdVToeOoGZGqNexeYdoOQFhGxF3oSiQMe9414ZRAq1m2NYCvGVr3Q+NuyztzgrsA\nLnsIRs5Ifq5Aq/lyFI2AYlvcFYw80RRC8jVBftmg/WnCANJ+BGZ8Gs69HcIh8DXCk581QlY2AV7/\nIZz4Wdj6KmxdOjDivuEF+POX4VsbzOdNa3h4Hpx5G5zzzfi2y+6Dw1uN6CeK+58Xws5/dD3/yJlw\nYF38vpKxJpXR1wgf/A5OvsY8qRxYD3+80bSZeLYR9xIr/NJcF32/wxUdqNSPiLgLXXnp6/Dhk/H7\nPEUm48VbAiOmWxXrwlD7d3j/cbj0R8nPZXeaFo2EwmqzXlgV/ZC3HhRxzzTtR4wXqiNGDNHWEiN8\nRSPNsTd+CDvfhpDP1D359CPGE7UJtBkPdPTJZltryK+ALX8zgq7DcMF3jRe9/nnT/qNn4d1fpGdn\n8SiYf7/5/NnsfBvcedFUwcO1prJi424j7u1HjGOS7ImiZb+13Nf12NGdMOUSOO875vztR+C3lxlh\nn3whfO5x46k73OCwui43/RWe/gIc2ADjT4N9q6Pn2/W2Wdqifv7dMOefzbrLOyDfARH3THFwE2x6\nCc69I3VYonG3eRxM5RUPBIF2WPsnOOlzcPY3zRexsNo8kibjqS+Y2Opx50XLlUbC0eUe67GzsNr6\nEJcbsbA7V1sPQPUJg/O3DRf2fQT1H1gbyny+yibAced3bbv2OfjTTd2fz5VvlqEOE5YIdkDDRtjz\nXlTcI2Fz3FMUfZ9SRuxrl5ntcfOgagpM/xR88Fv4zzFm/8iZUNlDKduQ3/xIbF4SL+4vf8cI441/\nMdu2UKda2vhbo5lbLQl9P5GIEf6TroJxc6J/n7vAPIWOOcV8LxKxR5ju/8iI+/614C40bVv3Q+kE\naNpt2kw4I+ZJdmAQcc8Uy39mvOPpV0DHESN+JWNMTPLIDvjMo/DoOeBvgnuPGE9pwwsw5eJo59Sx\n0rLffOgdbnN+MHHwUIcZPZpOLYvpn4LNlsfSHWUTzLJ4tPUaFbVB6F9e/Go0XhzL55+EqZcaL71h\nkwmz/d9/mZDA6TfT+UMQuwx2QOMu8/4xpxjB6zgK/1UD/pi01kCbWSZ+Nuf+iwlVnPvtqEc/5SK4\n4SV49XtwZBtc9xyUjO757/rh+K5C3FJvPq+d29Zxu7O+0ztP+JzZx92FXYW//TBEguZzauNwmlBi\n3crUZQJKx0NeWfTe7/vIfIfyy80P0+xrzf2GXpcS6Asi7plAa9j2hln//ZXGQ6+cAvMWwvKfm/3b\nXjfCDlC3ynyA/3gjnPYlGDvXdFA17jJF/tsPQ9sh8+gYbDM/FEGf8WjKa4zXc2S78TrCAZOeFuyA\n7W+YD3Ei+RUmRpgOJ11lzlE11VSzUw7r5TSPq+Ggsatqimn/qZ+apwA79csWDqH/aD9qYt+X/If5\nrOkIPHMdPHeTeXryN8e3v+o3MPOz6Z/fY3mt6Yj7nBvNK5FJH4MvLTPnSDckUTTSeMA2oYD57If8\n0X0pPfcEcbf3j5ltQiYhv7k3YH4wIF7cwYh6d+KulDm25mnzVHNwvfm+2uJec445Fg70upRAXxBx\nzwSHtkZjb43WY9rhrfDynTB6tvGqtr0Wbb/mD+aDDLDy1+aViCsPCqqs+OBh8wjZdgjC1gc/r9S8\nHG4TK3S6YPYX4IT5RpztGKvDZcJAiZkFqXC6e9cxNv606HphtYj7QOBvMU9GsbnTn/89LH/YfB5K\nx0HZRGvIuxfGze3d+Z0uE6oJJBP3ouTvSYbD2btYc/Go+IFvbdZ6oNX8zd7irp66vew4kiDg1v7R\nlri3Hog+XTZbwl8yJv76sz5vzlFWk9rG8++C1X+AjkYoHQsnf8GI/o5/wJhTzQ/dIOX+pyXuSqn5\nwE8AJ/BrrfX9SdpcDdwHaGCN1rqH5/RhRCRsxNOuHfHuo8a7rTkHdrwJX/sAHj7diOyF95mUsQ9+\nZ9rmlZkOSzAee9MeOOd2I+IVx5uUr4Kq5DHxoM988B1Oc55sSzksmwhHRdz7Fa2NZ54YEy6fmLrT\nuy94ixM891az7K+QYTKKRsLe96Pbsd54ywETYukSjtkX395OkbSPj5kd3bbF3X6PHTq0mXCGeXVH\nzTldM28AbnrFLM+9vfv39yM9irtSygk8DFwE1AErlVKLtdYbYtpMAe4GztZaH1VKjUh+tgxQuwye\nvBLuqDVzEQ4WkYgZeVY2Hp75J9PJsvAN+OV5xms/41a48HvG4ymogFvetjobR8CO/4v2rt/0qvlA\nN+4ycfCSsemLtDvPvLKV8onxX1bh2Am0ATp5h19/0kXcU4Rl+pPiUUa87bERceK+z0x0ocPWtnUs\ndqBcrLi37jdPu3aHcOK5UPHjMoYg6Xju84BarfV2AKXU08AVwIaYNl8GHtZaHwXQWmdP0ZB3F5nl\n3lUwNf35B4+JIzvg7Z/A+7+Bz/zSdDgCvHirEfZJH4NP3GMeEe3HxNiUslNvgLceNOsVx+VuNknZ\nRDOIJBI2TxfCsWN70BkT916EZXpL0QhrQFGLEfLY+Pumv8Cud8x6YbWJd7/1oNlfOMKEcP54I0yd\nbzLCPnrafP6KrdDLX75lUj1DftPJm1+efmgyS0lH3McCe2K264DTE9qcAKCUehsTurlPa/23frHw\nWHFbaVzB9sG5Xv2HsOh8s55XBs/fHD22/Q2z/Pg9UbuSUTEJ7txuPpCpyoDmAuUTTfrk8p+bPOVk\nj7OpaD9ihEw5MNkdDtOh6y02X15XXjT/eDhhC65nsMV9MMIyVpik9aAR99jMGTtX3ukxsfHlPzeD\nlABmXW0y09oOmvLTrjwT/556qRlzccG/mX4wWyMmXwijk8yKNMRIRzmSxQB0kvNMAc4HxgH/UErN\n1Fo3xp1IqYXAQoAJEyb02ti/iNzuAAAaoklEQVQ+4bZi0YEBFPflj5gPzoX3mZxvMIMtikbAc/8S\nbRfymWViAa1kFFYOSo96Rhk924jyq/eajtzv7EzP4zy8DX4+12SBJGLnIjtc5rG6ZAxUnWBekZB5\nUrI/C+d+O/d+PO1MmAH33EviO8MHJSxjhUl+e5l5+q1fbWXQWCL/9Q/ND4CnwAwSAmsUdT5c+H1r\n0ukkcnbevw6czRkknU92HRBb+GAcUJ+kzQqtdRDYoZTajBH7lbGNtNaLgEUAc+fOTfyBGBg6PfeO\n7tv1Fa2Nl9B60KQyrv0TzL4ezrglPo43/oyYAT3Z0yWRUcbMhrt2m1Guf7zBpI95iswjNZh0yuLR\ngDbpYyNmmMEu214zwn7JDy0xsdL9/C3Gm8svNwLfesBkI9Uug9W/73r9KRfB2FMH8y8eeGxvesDF\nvSg+pXIwwjIjZ1o/0mFTkyXYYcZ9jJxhBjZVHBdvXyy59iOeBun8xSuBKUqpScBe4BogMRPmBeBa\n4HGlVBUmTLO9Pw3tM+6Y0XUDwaGtpuMU4PmvmDxzOzUwtrd94plG3D3FqUd7Dke8xTD5E0bIN7xg\nRkymCqEVjzbe2e7lJlZ6xi3pdy77mow3Hw7CwY3wm/lmWHquMWjiXmxGedoMRlimsApuW9lzOwFI\nQ9y11iGl1G3AK5h4+mNa6/VKqR8Aq7TWi61jFyulNgBh4E6t9eGBNDxt7LDMQHnu2183S0+RyXKp\nnmYGGNmMmmUyZcqsXvrBzNgZKniLTYzzg98Zkf/yayYrKBwwOccOlxmE9ecvwX9YP5gzr+xdamde\naXTdzl/OxclCBlXcW6KZK4E2839yShnfbCGtZxWt9RJgScK+e2PWNXC79cou7FTA/uxQjc3uqF9t\n4nyf+w2seAROvjZedG5aakRq13KzPcTTqwaMeTebTrHpl8XPF2nnHo+bY35ID28zP5bTL+/7tew+\nj7ZcFveSgb2Ot9iMywj5zXcs0Ga89mwbSzGMyf1AlLJEuL88990r4MmrTEW8GZebTqWKSTDxLPNK\nxJ1vXnaIJp3O1OHI7GvNqzs+/Uj/XMudb8JjrbkclhnA2DdEfzz8LTHiPsDXFHpF7ou7PaihL557\n8z6Tq37eXdE6Kc/eYIZdr7DE/eiu9FL47FCAdKZmB0XV3cfcD9XCuudM5+yEM0zIoW6lGVE880pY\n9RtTSKqw2tT9dlplHfLL4aIfDGzsuTv8LaakgD1+YqCwwz7+ZnMvA62Z+5uFpOS+uEesdLnEVMi2\nw/D4J02lvKrJyd/7t7tMJ9+EM83kFeufNwMnRsww2zvfNp2psbO+pKKgysTd7eHOQmaxB7bE0rIf\nHj0bPvtLWPMMrH3W7C8eber1hK36PutfME5D7atWRs8o48mGrQJtI2ZEZ+MBk5P/zPVGAN0F5oci\n2GG92qPrYb8V2rAmfnB6zJNeQRUsuD8+XJUKu8bKQGNfY/nPTahx/1qTey5kDbkv7p2ee0f8lG5H\nd5ia1Ac3RMV99wqT/TL9U6agke39NNeb0Ws2V/4aHlsAj19qtsvSEHeHA775Uf/8TcKxU1hl4vex\n1C6D9kPw4m3m83LS52DcafCylQd9w1/g7YdMu1P+yRSI0mG4/k/WBCYafnEuvLfIiJ+7wAje+ufN\n6MkpF5mxDuGQ6eAtHmXauPPN0ukyTkgkaAbAhYMmnXP3cnj6Opj2SWvClGITAvE3m07hsXPME4TD\nafolBsODrjrB2GCP61CO6OQTQlaQ++IescR9y8vwwAy49V3zhbO9MHsJZsBR814j+PN/GO38jC0+\nVHOueRy/bSX8j1UWoGyQBmQJ/UfRCCOasdjzWdr/7+mfMnnUr/+neeKadK7xrutWwsfuNJ+d1oPR\nySOUgtMXwuKvmaneYpl1jXki6Av1q+GFr8K6P5uUTtthATPaMnEmo7G9rPLYF6qmwHf3WiWF9fAc\nDZzl5L64x34RWuph2ffgsgdjxN0qv9m4O5qvbndK5ZebZcMmszz7G2bmJIifRSWdsIyQXRSOMKGW\n311hvOaqE8xgqrFzzI+6rwmO/4Txqm9aajxpMEPT77LKNH92UbRUss2pXzTTs/lbzJgHX7PxsHtT\nWiGRMbPhq1bdFK3NU4Ud43blmeyhYIcZyBVog8oUYcaBQCnJkMlScl/cI+H47U1/tcTdEnVb5O2i\nQxA9Zv8w7HnPLMedFh9XvOlV07FWElM3WxgaHHe++SwE2k3YbeurJhzysW+bCRZiiS3qlkgyYSse\nOXBTqCllBsHFDoQbc8rAXEsY0uS+uMd67mUTLA99X9ewzK53TDwzvyI6O1GnV2/V0CiNrcKAGawU\nO2BJGDpMPBNueSu6HYmYTkz7aU0Qhji5HyiL9dxPtvKo963uKu6tB434u7wxXn3CjCkSW89dHA5T\nV19CDEKOkHvi7muKL0UaK+4nfQ5QpoMqMSwT8pn4pdNtqgfGHrMRr04QhCFC7oVl7re86/usyaXt\nsMzH7jQdTeU1cGiLmSEJoiIf8htxj4Sjoh4JmZGMZ3/d7BOvThCEIULuiXsikbDJAf74v5ttT5ER\n8i6eewcUVJrt2GMub87WexYEIXcZmmGZD5+EzWlO9KTD0foyYMIuiQIOUc/dERuWCQ75qbYEQRie\nDE1xf+dn0ZFxPRGJxM/P6fKagSiJee6xMffYDlURd0EQhiBDU9xDvvQLgSX13INds2Vsz9327MGk\nRDpE3AVBGHpkt7j/6uOmgFMiIX90Wq+eiITjh0Y7PfFhmZDfLIMdxqt3JGTLyOQDgiAMQbJb3Pe+\nD88v7Lo/5Etf3Lt47l4IBZKEZfxmqLnTFROWCQ3LuRcFQRj6ZLe4pyIU6Dks42s2XnvsrEkQ06Ga\nGJbxGc/d6YkZoSqeuyAIQ5PsFffYgkxth2HZfcaThvQ89/vHw19vT+K5e6wO1RgBD4dMOztbxj4m\nMXdBEIYoQ0Pcl/4bvPUgbF4SFeJ0OlRXP2V57jGhFZcnoUM1aH4swPLcXZItIwjCkCd7xZ0YcbdL\nCARao0Ic8nWt+JiIw9VNh2pMWKZT3POtDlURd0EQhjbZK+46El1355tlsCO+3ktP3rvD1U2HaoyA\nx3nunmj4R2LugiAMUbJY3GM8d1vcQ/6oEEPXeVETcTjT61C10yFdeVZYJqa2jEOyZQRBGHpkr7jH\nhmVceWYZ6ogX92APnapOdzcdqra4++M997iwjHjugiAMTbJX3FOFZUIxYZkePXd38vIDOhIV9HAQ\ngr7odeySv1pLzF0QhCFLFot7jOdui3Mw0XNPIyyjw2ZmdhtbrO1UyrgOVW/0eCQk4i4IwpAle8U9\nNiwTm99ux8eh51x3pztJzN0Ks/hbrXPHinteNK89HJA8d0EQhizZK+6xYRk7Bu5v7aXnnixbxhL3\ngC3uwZgO1RjP3c6Fl5i7IAhDkLTEXSk1Xym1WSlVq5S6q5t2VymltFJq7jFbFhuWsTs//c3xqZA9\nxtxdqT33pGGZ/KinHglZtWXEcxcEYejRo7grpZzAw8ACYAZwrVJqRpJ2xcDXgXf7xbJYz90Oy/hb\nepctk8xzd3nNMs5zTxJz7/TcRdwFQRh6pOO5zwNqtdbbtdYB4GngiiTt/j/gR4AvybE+kMJzj4u5\n98Vzt8TaDukkxtydEnMXBGHok464jwX2xGzXWfs6UUqdAozXWv+luxMppRYqpVYppVY1NDR0f9XY\nsIwdc/c19z7PPVVYxibkj6ZC2nnuEL2OxNwFQRiCpCPuKsm+TuVVSjmAB4Fv93QirfUirfVcrfXc\n6urqnhpH123x9bf03nNPVn4g/kLRHwm7njtEY/JSz10QhCFIOuJeB4yP2R4H1MdsFwMzgTeUUjuB\nM4DFx96pGiPudnw8MSzTU7aM1qnDMrH4mq1j3qinbp9bPHdBEIYg6Yj7SmCKUmqSUsoDXAMstg9q\nrZu01lVa6xqtdQ2wArhca73qmCyL9dz9LWYZDsArd5t1T1G0+FcqIqHUqZCxbHzJhGMcjmhYxn4q\nkJi7IAhDkB7FXWsdAm4DXgE2As9qrdcrpX6glLp8wCyLzZYJtEJ+OYw/I7rPUxifFgmm1EDcdjB5\n+QEbd4FZHt0Bo2aadTsM0+m5i7gLgjD0SCugrLVeAixJ2HdvirbnH7tZEB+WaYPyGjj587Bnhdnn\n9EYnsu58S6K4h1KXHwDzAxFsN7H5L71m9jkSsmlE3AVBGIIMjRGq/lYTTimbEN1nl+5N9R4w+fHd\nZct4Cs1y1KzohB6Jg5wk5i4IwhAki8U9IebucEPZxOg+p7trzF0nzMzUU8zdjqvbIRn7vBD13KWe\nuyAIQ5DsFfe4sEyLiYWXjovuc7jTC8t057m7rTrxk86LOa8l5u/+smt7QRCEIUL2uqWJQu30ROu6\nQ/KwTOKcqpGgOU+y8gMAp98CFcfBCZfEnxegeW/8tiAIwhAii8Vdx28npiQmDcskeu5ha6q8FHnu\nVVNgykUJ503w1EXcBUEYgmSvuJMg7rbIXnif6WDdvSL9sExctkyMeI+Y3vWyiTF2u+67IAjCECJ7\nxb1LWMYS93O+ZZa/+3R05Gqq94SDpkM1VrBjyw+UjKULsZ560SgYd1rv7BYEQcgCsljcEz33JOGS\nVGEZTzF4i0w2TGKHauy6SlI2Jzb8c8fm3tstCIKQBWRvtkyXmHvC75DT0zUsY3eoXvwDmHml1eGq\n4ztUOwU9WT00JMYuCEJOkL2ee6qYu43DlXoQk3Jax60iY7HeOsDnfx+f297ddQRBEIYg2SvuxxKW\nUQ5zPHY7lumXpb6uy8p9P/na3tkrCIKQRWSxuCd0jiYLy8SKeyQSHaGqHPHtEz337nC64dtboKCy\nd/YKgiBkEdkbc+8Slknw3B2u6AxNK38NPyiHtkPWMWe8uKteiDtA8UiZpEMQhCFN9op7l7BMN4OY\nPnrWLA9uMMtj8dwFQRBygCwW916EZfLLzdL23BPFvbeeuyAIwhAne8W9N2GZ/AqzbD9slnaHamdb\nEXdBEIYX2SvuqUaoxm53eu5lZtl60CyVI2GwUvb+mYIgCANB9qqeHXO3QypdxN1jsmNip9Frtubt\nTuxQlZrsgiAMM7JX3O2wjD1bUmJVSFuwI8GoB99iibtyxLeXsIwgCMOM7BV3Oyxj13BPVt8dzCjV\nkDUStTlW3KVDVRCE4UsWi7u1tMU95Is/bodpwsFoGQK7jXLG56mL5y4IwjAje8XdVne3FZYJdsQf\n7gzLhLrWmOniuWfxnykIgjAAZK/qdZbvLTDLLp57krCMjUMGMQmCMLzJYnG3PHd7gFKi950sLGOT\n2KEqMXdBEIYZ2ZsjaHvup30JRs2Cs74Wf9wW70ioq+eemOcunrsgCMOM7BV3O+bu9MAn7ul6uNNz\nD3Qt/auc4EzYFgRBGEZkr7h3DmJKETmKC8sk8dxjp9ATz10QhGFGWjF3pdR8pdRmpVStUuquJMdv\nV0ptUEp9pJT6u1Jq4jFb1jnRRqrp8KwO1WRhmS4lf7O3a0EQBGEg6FH1lFJO4GFgATADuFYpNSOh\n2YfAXK31LOA54EfHbpqd6J5C3G3xDgd6ToWUqfMEQRhmpOPSzgNqtdbbtdYB4GngitgGWuvXtdbt\n1uYKYNwxW9arsEyiuKt4ca+cfMzmCIIgDCXSEfexwJ6Y7TprXypuAl5OdkAptVAptUoptaqhoaH7\nq6YblgkHIRToWm4gdrtkTPfXEgRByDHSEfdk6qqT7EMpdT0wF/jvZMe11ou01nO11nOrq6ujBw7V\nmuqOyS6RynOPKxzmj45ktd8joRhBEIYx6Yh7HTA+ZnscUJ/YSCl1IfBvwOVaa3/i8ZTsXws/nwNv\nPxS/v7NQWCrPPSEs44kRd4fTdLQCVE1N2xRBEIRcIR1xXwlMUUpNUkp5gGuAxbENlFKnAL/ECPvB\nXlnQuNss61bG7+/sT+0pLBMwYRlPgudeOQXm/DNc92yvzBEEQcgFesxz11qHlFK3Aa9ghgY9prVe\nr5T6AbBKa70YE4YpAv6ojBjv1lpffmympRuWCZmwTKK4O13wqYeSv1cQBCHHSWsQk9Z6CbAkYd+9\nMesX9tkCnTR8n35YJthh2saJuwxaEgRheJNFo3sSRLwzFTJFczssE2gzyzhxT/UmQRCE4UEWiXsi\nPYVlLM89mbhLuQFBEIY52SvuPYVl3Hlm2XHULBNj7oIgCMOYLFLBhNh7TyNUPYWQVwpHd5jtxDx3\nQRCEYUzmVTBVfLynEaoApRPgyHazbs/YBNKhKgjCsCfz4p6SHgqHAZSOg8PbzLqEZQRBEDrJvAqm\nTIXsISwDRtx12Ky7pUNVEATBJvPi3kliKmQ6YZmY4pPiuQuCIHSSxSqYRlimLKbkjeS5C4IgdJK9\n4p5WWCZW3Iui69KhKgjCMGcIiHs3XvjYOXDiZ6BwBFQeH90vYRlBEIY52TtBdk8jVMF0nH7ucbMe\n7IjfLwiCMIzJXhdXJ07e0QOOmMk5xHMXBGGYkwUq2FMqZJqdo7Heuoi7IAjDnOxRwS4inkZYJtX7\npUNVEIRhTvaIeyI9FQ7rDkmFFARhmJPF4t7LsEwsIu6CIAxzslfcexuWEQRBEDrJHuVMrDFzLGEZ\nQRCEYU4WiHuqkr/iuQuCIPSVLFDOHibIlvi5IAhCr8kCcbdIlQopYRlBEIRekz3inoiEZQRBEPpM\n9irnsaRCCoIgDHOyV9wlLCMIgtBnslfcxXMXBEHoM1ks7pItIwiC0FfSEnel1Hyl1GalVK1S6q4k\nx71KqWes4+8qpWrStiDVBNkyQlUQBKHP9KicSikn8DCwAJgBXKuUmpHQ7CbgqNZ6MvAg8F/HbJmM\nUBUEQegz6bjF84BarfV2rXUAeBq4IqHNFcBvrfXngE8odYzxFIm5C4Ig9Jl0xH0ssCdmu87al7SN\n1joENAGVx2aahGUEQRD6SjrKmcx1TgyUp9MGpdRCpdQqpdSqhoaG7q/al7DMqFnptxUEQchh0pkg\nuw4YH7M9DqhP0aZOKeUCSoEjiSfSWi8CFgHMnTs3VU+q3dgsexOW+fLroMPptxcEQchR0vHcVwJT\nlFKTlFIe4BpgcUKbxcAN1vpVwGtap0yDSZM+hGWcLnB5j+2ygiAIOUCPnrvWOqSUug14BXACj2mt\n1yulfgCs0lovBv4XeEIpVYvx2K9J34QeqkJKtowgCEKvSScsg9Z6CbAkYd+9Mes+4HN9sqBTxBP3\nW0vpUBUEQeg1mVfOVNEbGaEqCILQZzIv7qnCMlI4TBAEoc9kXtxTeu4yiEkQBKGvZF7cUyFhGUEQ\nhD6TeXHvtnCYCLsgCEJfyLy4p0yF1JIpIwiC0Ecyr56pwi86IiEZQRCEPpIF4t5NWEY8d0EQhD6R\nBerZXZ67eO6CIAh9IfPi3l0qpIRlBEEQ+kTmxb27QUwSlhEEQegTmVfPbgcxiecuCILQFzIv7rbn\nrjWEg9B2OLotYRlBEIQ+oY657HpfL6xUC7A5IxfvG1XAoUwb0QvE3oFF7B1YxN7UTNRaV/fUKK2S\nvwPEZq313Axev1copVaJvQOH2DuwiL0DSzbamwVhGUEQBKG/EXEXBEHIQTIp7osyeO2+IPYOLGLv\nwCL2DixZZ2/GOlQFQRCEgUPCMoIgCDlIRsRdKTVfKbVZKVWrlLorEzb0hFJqp1JqrVJqtVJqlbWv\nQin1qlJqq7Usz6B9jymlDiql1sXsS2qfMvzUut8fKaVOzRJ771NK7bXu8Wql1KUxx+627N2slLok\nA/aOV0q9rpTaqJRar5T6hrU/K+9xN/Zm5T1WSuUppd5TSq2x7P2+tX+SUupd6/4+o5TyWPu91nat\ndbwmS+x9XCm1I+b+zrb2Z/w7h9Z6UF+AE9gGHAd4gDXAjMG2Iw07dwJVCft+BNxlrd8F/FcG7fsY\ncCqwrif7gEuBlzFDfs8A3s0Se+8D7kjSdob1ufACk6zPi3OQ7R0NnGqtFwNbLLuy8h53Y29W3mPr\nPhVZ627gXeu+PQtcY+3/BXCLtf5V4BfW+jXAM4N8f1PZ+zhwVZL2Gf/OZcJznwfUaq23a60DwNPA\nFRmwoy9cAfzWWv8t8OlMGaK1fhM4krA7lX1XAL/ThhVAmVJq9OBYakhhbyquAJ7WWvu11juAWszn\nZtDQWu/TWn9grbcAG4GxZOk97sbeVGT0Hlv3qdXadFsvDXwceM7an3h/7fv+HPAJpQZvCHs39qYi\n49+5TIj7WGBPzHYd3X8IM4UGliql3ldKLbT2jdRa7wPzZQJGZMy65KSyL5vv+W3WY+tjMWGurLLX\nCgGcgvHWsv4eJ9gLWXqPlVJOpdRq4CDwKubpoVFrHUpiU6e91vEmoDKT9mqt7fv7H9b9fVAp5U20\n12LQ728mxD3Zr202puycrbU+FVgA3KqU+limDToGsvWePwocD8wG9gH/Y+3PGnuVUkXAn4Bvaq2b\nu2uaZN+g25zE3qy9x1rrsNZ6NjAO89QwvRubss5epdRM4G5gGnAaUAF8x2qecXszIe51wPiY7XFA\nfQbs6Batdb21PAg8j/nwHbAfrazlwcxZmJRU9mXlPddaH7C+MBHgV0TDAllhr1LKjRHK32ut/2zt\nztp7nMzebL/HAFrrRuANTGy6TClll0WJtanTXut4KemH+fqVGHvnW+EwrbX2A78hi+5vJsR9JTDF\n6hX3YDpHFmfAjpQopQqVUsX2OnAxsA5j5w1WsxuAFzNjYUpS2bcY+KLVg38G0GSHFjJJQgzyM5h7\nDMbea6wMiUnAFOC9QbZNAf8LbNRaPxBzKCvvcSp7s/UeK6WqlVJl1no+cCGmn+B14CqrWeL9te/7\nVcBr2uq5zKC9m2J+6BWmfyD2/mb2OzfYPbg62pO8BRNj+7dM2NCDfcdhMgnWAOttGzExvr8DW61l\nRQZtfArzmB3EeAk3pbIP84j4sHW/1wJzs8TeJyx7PsJ8GUbHtP83y97NwIIM2HsO5jH6I2C19bo0\nW+9xN/Zm5T0GZgEfWnatA+619h+H+ZGpBf4IeK39edZ2rXX8uCyx9zXr/q4DniSaUZPx75yMUBUE\nQchBZISqIAhCDiLiLgiCkIOIuAuCIOQgIu6CIAg5iIi7IAhCDiLiLgiCkIOIuAuCIOQgIu6CIAg5\nyP8DvzKR4VOE0dAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbe4b358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve_r2(algo, X_train, X_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1, len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "    \n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(r2_score(y_train[:i], y_train_predict))\n",
    "    \n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(r2_score(y_test, y_test_predict))\n",
    "        \n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               train_score, label=\"train\")\n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               test_score, label=\"test\")\n",
    "    plt.legend()\n",
    "    plt.axis([0, len(X_train)+1, -0.1, 1.1])\n",
    "    plt.show()\n",
    "    \n",
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=3), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制决策树算法的模型复杂度曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1d42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxSampleLeaf = 100\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in range(1, maxSampleLeaf+1):\n",
    "    dt_reg = DecisionTreeRegressor(min_samples_leaf=i)\n",
    "    dt_reg.fit(X_train, y_train)\n",
    "    y_train_predict = dt_reg.predict(X_train)\n",
    "    train_scores.append(r2_score(y_train, y_train_predict))\n",
    "    test_scores.append(dt_reg.score(X_test, y_test))\n",
    "    \n",
    "plt.plot([i for i in range(1, maxSampleLeaf+1)], train_scores, label=\"train\")\n",
    "plt.plot([i for i in range(1, maxSampleLeaf+1)], test_scores, label=\"test\")\n",
    "plt.xlim(100, 1)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRUQCSOEuIhJACncRkQD6b9bp4g2wZFwjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xccad748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxSamplesSplit = 200\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for i in range(2, maxSamplesSplit+1):\n",
    "    dt_reg = DecisionTreeRegressor(min_samples_split=i)\n",
    "    dt_reg.fit(X_train, y_train)\n",
    "    y_train_predict = dt_reg.predict(X_train)\n",
    "    train_scores.append(r2_score(y_train, y_train_predict))\n",
    "    test_scores.append(dt_reg.score(X_test, y_test))\n",
    "    \n",
    "plt.plot([i for i in range(2, maxSamplesSplit+1)], train_scores, label=\"train\")\n",
    "plt.plot([i for i in range(2, maxSamplesSplit+1)], test_scores, label=\"test\")\n",
    "plt.xlim(maxSamplesSplit, 2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}