notebooks/L07demo_random-forests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision tree, KNN, and RandomForest demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgelbart/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'%.2f'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import numpy.random as npr\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import  KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from plot_classifier import plot_classifier\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "%precision %.2f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make up some fake data\n",
    "\n",
    "N = 50\n",
    "X = npr.randn(N,2)\n",
    "y = npr.choice((-1,+1),size=N)\n",
    "X[y>0,0] += 1\n",
    "X[y>0,1] += 1\n",
    "\n",
    "plt.scatter(X[y<0,0], X[y<0,1], color=\"b\", marker=\"x\", label=\"class $-1$\")\n",
    "plt.scatter(X[y>0,0], X[y>0,1], color=\"r\", marker=\"o\",  label=\"class $+1$\")\n",
    "plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False) # remove ticks\n",
    "plt.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=1)\n",
    "dt.fit(X,y)\n",
    "plot_classifier(X, y, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=3)\n",
    "dt.fit(X,y)\n",
    "plot_classifier(X, y, dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAF21JREFUeJzt3X9w1PWdx/HXbnb5IWlCzwIaQEYMmimYoWAFSjxbbe9UfnlN8aam6sxN7649Ztqr2oq2Sul5ok7F0uo4vaoz9EjltCgVKzOeelbA1h9QWJYfAQQhGvkhQkJCfuxmv/dHWEg2+3u/u9/P7vf5mGHGsLvZzxfhtd+8P5/35+OxLEsAAOd5nR4AAKAPgQwAhiCQAcAQBDIAGIJABgBDEMgAYAgCGQAMQSADgCEIZAAwhC+TJw8f8TdWxWfH5WsscMBnK8tU9uH7kn+IswMJ9ej4yGpZveGET7lQRxRq75R8Do8VyNDO48c/sSxrVKrnZRTIFZ8dp4ZFL2Y/KhjnxrkjVbm4Xr4Lxjs6jvDhZjXOWatw6/GEz/mx91F9tGmXykZVFXBkQO6mPL3yYDrPo2QBAIYgkAHAEAQyABiCQAYAQxDIAGAIAhkADEEgA4AhCGQAMASBDACGIJABwBAEMgAYgkAGAEMQyABgCAIZAAxBIAOAIQhkADAEgQwAhiCQAcAQBDIAGIJABgBDEMgAYAgCGQAMQSADgCEIZAAwhM/pATjJikS0O/Citmx8Tu1tLSqvqNK0uoWqqZ0vj5fPKgCF5dpAtiIRrWu8XYfe/0ihnsWSanW6PaBXX1imvcE/ad7NjxDKAArKtYmzO/DimTDeIKle0iRJ9QqHNurg3mY1BdY5PEIAbuPaQN6y8bkzd8bDYh4ZpnDobm3e9KwTwwLgYq4N5Pa2Fkm1CR69XO2tLYUcDgC4N5DLK6okBRI8ul3llVWFHA4AuDeQp9UtlM+/TFJXzCNd8vmXafrsm5wYFgAXc20g19TO14TqcfL56yStkbRH0hr5/HWaMGm8Lqud5/AIAbiNa5e9ebxezWtYrqbAOm3etFztrS0qr6zS9Nm36LLaeSx5A1Bwrg1kqS+Ua6YuUM3UBU4PBQDcW7IAANMQyABgCAIZAAxBIAOAIQhkADAEgQwAhiCQAcAQBDIAGIJABgBDEMgAYAgCGUiirbtH31n/itq6e5weClyAQAaSaAwG9e7HH6sxuGPQY4Q17EYgAwm0dffodzt2aa2kZ3bsHBS8ycIayAaBDMe1dfdo0dt/Vdfpk04PZYDGYFDzLEvXSZpjWQOCN1VYA9kgkOG4xmBQ7x0/oa2vPeb0UM6KBu69vb2SpPt6ewcEb7KwBrJFIMNR/e80t73+uLo625wekqRzgVt95utqnQveVGENZItAhqP632nOtXoV2PhkVt/Hzgm22MCNigbv09u2JQxrIBcEMhwTG3xLQ13avumprO6S7ZxgawwGNSsSUZmkA/1+lUmaEYlodZKw5i4ZuSCQ4Zi4ZYFIJOO7ZLsn2D44cVLb/X5dPXTooF/vejz6nGXFDeuZkQh3yciJq8/Ug3OiIfpOzJ3mz8Jdmr7pKdXWfVvDhlek9b3iTbB9d/oXsh7bQ1+9JuFjd736ugJHjurqBI97TpzI+n0BAhmOiC0LRJVJmtUbVmDjk7rya7en/D6xwX5fb69m7NiphimTVTF0iO3jThbWQK4IZDjibFngzNdWJKLuIRWSFZEkjTnSlNb3SbYaIpe7ZMAJBDIcEXunGT7crMY5axVuPZ7290hU9sj2Lrmtu0c/ev0NPXzNl/Nydw2kwqQeilay1RDZTLDRCg2ncYeMohVb9oiVyQRb/5UaDXmsQQPJEMgoWnZOsNm9UiOKMggyQckCrpfPVmjKIMgEgQzXS7ZSIxfsCIdMEchwtVT7VuQSouwIh0wRyHA1u1dqRLEjHLLBpB5cbVCDiqT2nh6VDxkij7JvhaZhBdkgkOFqsSs1nti8RU9u266GmsuyDk67G1bgHpQsgDPsmoTLVxkEpY87ZOAMu9Yi29mwAnchkOG4iGXpjx8d1m8fmqtTnx5SeUWVptUtVE3tfHm8hfkhzs5d49gRDtmiZAFHRSxL//7aX3R/0Ksjh76n0+2v6GjL7Xr1hVVa97s7ZEUiBRlHvtYiA5kgkOGol/cf0J9bhqmz911J9ZImSapXOLRRB/c2qymwLu9jsHstsp3n+8FdCGTkXbKA+m2wWZ3h+yQNi3lkmMKhu7V507N5Hx+7xsEU1JANY0Ui2h14UVs2Pqf2thZH6ql26x9QsZNkRzo6JNUmeOXlam9tyfv42DUOpiCQDWJFIlrXeLsOvf+RQj2LJdXqdHtAr76wTHuDf9K8mx8pulBOFVBjRozQ8a6A+koVsbarvLIq72Mshl3j4A7F9a+7xO0OvHgmjDfIqXqq3VLt53DrlPEa7lsqqSvmlV3y+Zdp+uybCjXUnNEujVwRyAbZsvG5M3fG9tRTrUhEu7auVeNjDfr1A19R42MN2rV1bcFWLqQTUDdMvFizqro1vOwKSWsk7ZG0Rj5/nSZMGq/LaucVZKx2YKUGckXJwiDtbS2yq55qQvkjnf0cvB6PfnHtTL205T2tOPHLvnXIlVWaPvsWXVY7r2hKNLRLww4EskHKK6p0ut2eeurA8kf0jnuSwqE5Ori3Tk2BdaqZusCGUceXSUB5PR7dMPYCnfiXzA45NUnsSo2o/is1qCUjleK4/XCJaXUL5fMvkx31VLvLH5ly234OZ1dqDB066Nd2v18f0C6NNHCHbJCa2vnau/0NHdxXp3DobkmXS9oun39ZxvVUO8sf2XDbfg60S8MOBLJBPF6v5jUsV1NgnTZvWq721pas66nZlD/sPJDTbQHFYaawAyULw3i8XtVMXaCGRY3613v+Tw2LGlUzdUHGk1vZlD9SdZjREpwY3XmwA4Fcompq52tC9Tj5/HVKZzlZOnsBEzrxJfuz40MMmSCQk3B6HW8uouWPr/3DLRo9drnOK/97jR7b93W8JW+pGjg4QTmxZH92fIghE9SQEzBhHW+uouWPVMvb2kKhlHsB0xIcX7J9lCWxrwUyYnaiOKgU25gTeWb/oaQdZsXWEpxLmSDT1yZrfkn1UwcQi0BOwOl1vOnKtaxyqq1Vqz9oTroXcLG1BOdSJsjktan2UV4V3Fk0H2IwA4GcgNPreNMRLau8trZRR1tuz+q0jedX/kqzLCthA8fT2wK2bt6eb7nUujN9bbLmly/29uqSSKRoPsRgBgI5gfKKKkmBBI8WZlvIVOwoqzTv261AmS9hh9lbh5qLquMulzJBpq9N1J33t0OH6i3L0ljLGvB8Uz/EYA4m9RKYVrdQr76wTOHQHA0sW0TX8d7i1NDOSl1WWZ5yQu8nK1apcnG9fBeMj/v4Xa++rkB3d1F03OVyUGk2r03U/PLE5i06sH2HVkQi7GuBjBDICdjZxmyn/ieKHG3ZoXyXVUzpuLMiET23bbd+9eZOHeneqjEjRujWKeN1w8SL5fV4JKW3u1wiubw2ltvaxmEfAjkBO9uY7TJ4Kd4j6iur5L47XPhws02jjC9iWVrfckSrDpzUka4ujRk2TN+6eKSurxpzNlATiV73f71/WKd7Vkiq1fGugJZuWqr/PfC2Hr12htp7Qllvf2n31pmmfIih+BDISaS7jjdduZ6XN3hLzS5JD0rKvqyy9qWTUt1T2V1Qms59kJxSqOdnkmr1aU9AS3Yu09OhynNruhNsvXnuut9W/61EO8Nz9FbLlVq//4AOnTyZ9faXbJ0JUxDIBWJHo8ngmvE3Jf1R0lWSFsuUskqsXPdmTlYr7wwv0crgnbp4RG/WZQJKDDAFgVwgdmwYP3gpnlfSKkmr1Ve+2KnRYycbd9pGrpOP8ZcgnpRP31BYD+pIR4eeXXBD1uNLVmKI7uLW1t1Dlx3yzox/sS5gR6NJ/KV4Xkk3S7pDo8dOznp3uHzKdU13vOv26efy6nWV6SGNGTHClnHGw14UKCRz/tWWODsaTew8UaSQcl3TPfi6T8qnX+gPsjREz+ummgttHO05bKiEQiOQC8SORpNMt9Q0Ra4fJLHX7dM9mq+QrpM032PpaHtHXsbNXhQoNAK5QOy4u810S01T5PpB0v+6P3fhw/LrCf2n+u5W77csrc7D3WuxbaiE0sCkXoHY1Whi91K8QrBjTXf0utuO7lP1sc2qDvcFZbSBY1Vwhy4aWanfBpt1pKMjbuNIJuxsFAHS5bFi+u2TGTOu1mpY9GIeh1ParEjkTCg92y+UbjJqRYTJujrbtOrBmdoS6jwblJK0T9IXPB6FPTXqivyH+mr1AQ0vW6KZnzulR6ZdklYoR9vH27p7NOd/ntM74fCg95nh8+mlf1zIigtkZMrTKzdblnVFqudxh1xAxXh3a5LAht9odqQ3bgPHDEt6w6qW9LikQ5IuUmfvHdrw6eO6d0yDPv/F5CWhhj/eePa/aRSBUwhkFI3Wo3u0Z+gIzdDAZW5dnadkWR75tUG9elLRO2TpQYVD5Xrnlad06aXXpv0+NIrAKQQyisbXvvXruL//+NIvqqd7gqSN6t9009dSPlutxw9l9D7sRWGGaFPOw9d82TUlIgqXKHqRiF/S3YrXdCPdc+ZxFBs3NuUQyCh6ffOhiZtumC8tPm5tyuGvKoreyPMvVrKmm77HUUzc2pRDDRlFr+90lwcSnO7ygKbN/pZ2bV2b9banKKxcTn4pdvxthDGyPUG7rxNwvMp8M+VTraT3FO0EvKh6vPYG38jpIFjYr627R99Z/0rcUkSxnXJuJwIZRsjlBO1oJ2D1pNHyaruG+b96tqX80slX69D7LTkdBAv7JZqwi21Zj3JL6zqBDCPkeoJ2d3e7WvZt1B8kDfGEVP9PT6hm6gJteev3OW97Cnslm7CLbcox/ZRzu1FDRkI108apfusdaT//oc/cr3CcY5jSOboq1X7RLZvu0n9P25/wvR/e+Gd93erRdZJujPSoYtO39cO/m6VVbfuUbAWG1bpPP/Y+qg/TvkrkKt6EXbTz0e1NOQRygeR6np4Taqra9eEvg2f3eEgmfLhZcx/s1dqXBv5+ukdXpdov+sPjbTq8pzXuo23dPfrNhr/qnTMbDt0bDmvGm3/VggurNWroeTrWnvgg2FFDz9PhPa1pXSNyl2rCzu1NOWYmQYnJpT5a7NItRaTaLzrZqSDJJoFunTJew31LFW/b0+G+pbptCkFcSG6esEsHgVwAudZHTZBsVjyZdI+uSrZfdLLgTDUJVDd2rGZVdWu470r134t5uO9KfamqR9dPZI1yobh9wi4dBHIB2HGentOybWNN9+iq6Cb25/lnKJPgTDUJ9MyOnfrFtTP109kj9fnz79T5w+r0+fPv1E9nj9Sj187Iaq9kZMftE3bpoIacJ/1rxkdbdijX8/Sc1H9WvCHDBfrlFVU6naSGGz26Krp0rSb4b/rl+u/rSHePxowYodumjNf1STaZT2cSyOvxaM4lEzXnkolpjRn54fYJu3QQyHkweCLrEfXVR5OHkqmSzYqn0tdFtyxBF90yTZ99y4APL6ttn0aV+fXDGTVpnfbh9kmgYsL/q9QoWeTB4JrxIkkPqthOi5aktlAop7PlUp2nd+mUOQMmPI+1b9TO1hVauumkfvDa24pkcKINUOwI5DwYXDP+pvrujq9SMZ0WLUnP7D+U06x4qoNZm4IvxZ3w7Ay/o7dahmj9/gMp3gEoHZQs8mDwRJZX0ipJq9VXvtip0WMnZ3TIpxNOtbVq9QfNeqd34LK8TDd7SXZ0VbIJz87wEq0M3mlc7TfRxulu3FAd9jIzCYpc/DW1Xkk3S7pDo8dOVsOiRtVMXWBsGEvS8yt/pVmWlddZ8VSrMI50dOT8HnZLtOLEjRuqw16uuEMudJdcOhNZ2Sj0dTTv261dZT5d7Y//ve2YFU+1CiNZQ4gTEq04yWUlChBV8oGcbuuunWpq52vv9jd0cF+dwqG7JV0uabt8/mVZ14yduI6frFilysX1abcVT355iSbOvS2j97jYc70W/+wBne6cI6lLPn1DYf1e0jAjO+kSrTjJZSUKEFXygTxwxcO5AzDDoTk6uLdOTYF1cWubuYhOZDUF1mnzpuVqb21ReWVVTjVjJ64jE74LxuujN4PSmz/M6HVXWZZmVHyiv/RcoVDvZfLqdZXpnzXE12RcJ12ifRjmXzrJtRuqw14lH8ipu+SW5yXIkk1kZcOp68hEWRYb9JRJWnHDeD2/Z68e3vSC1shSvdboRzNm6uuXTjKqky7RPgxL/vRmwpUo3CUjE+bOKNkk3dZd05XKdcTj9Xh0rL1d3yjz6jpJ9WVeHWvvMCqMk+3DsO3oMX2f/Rlgg5IP5FS7iJneJRdVKtcRT2zYmRhmyfZhqFPfokb2Z0CuSr5kka8VD4VWKtcRT7ItGU35kT/RPgwdoZDCkYg2e736vd8/6HXsz4BMlHwg52PFgxNK5TpixU6URZk2McY+DCiEkg/kfKx4cEKpXEes2FJAVP8f+U25SwbyreQDWbJ/xYNTSuU6+mNLRuAcVwQyzEUpADinOH/OBYASRCADgCEoWQCSwoebnR4CQCADUSvrnnJ6CChVL6e3JwslCwAwBIEMAIYgkAHAENSQcVbsiSQvr7pIt/o/0bwx4wq68xpn08GtuEOGpHMnkry2tlFHW27X6fZX1LTze7o/6NEPXntbEcsq2Fg4mw5uRSBDUuyJJPXqO+OuXp297+mtliFav/9Aiu9gj/5n05m2BSeQbwQyJCU/kaQzvEQrg4VZpxvvbLpSZEUi2rV1rRofa9CvH/iKGh9r0K6ta2VFIk4PDQ6ihgxJqU8kOdLRkfcxJDqzzpQtOO3ixIG1KA78X4ek1CeSjBkxIu9jSLZRfSlJVB4Khzbq4N5mNQXWOTxCOIVAhqS+E0l8/mWSumIe6dJw31LdNiXzA0wzkezMulKrJac+sPZZJ4YFAxDIkNR3IsmE6nHy+eskrZG0R9IaDS+7Ql+q6tH1E9Nr/cxWsjPrSu1sulI+sBa5oYYMSfFPJBl/0UW6zW9p7rQZeV+H7KaN6ssrqnS6PaC+UkWs4j6wFrkhkHFW7IkkN84dqcrF9QVpCnHTRvWlfGAtckMgu0xsN155RZWm1S1UTe18ZvYLpFQPrEXuCGQXYbmVGUr1wFrkjkB2kYHLraI/Kk9SODRHB/fWqSmwrqQOUDVZKR5Yi9zxUewiLLcCzEYguwjLrQCzEcgukqobj+VWgLMIZBdJ1o3Xt9zqJieGBeAMAtlFEnXj+fx1LLcCDMAqCxdhuRVgNgLZZVhuBZiLQEZK4cOF2ZwecDsCGQmtfemkVPeU08MAXIOiIQAYgkAGAEMQyABgCAIZAAxBIAOAIQhkADAEgQwAhiCQAcAQBDIAGIJABgBD0DoNo3AqNtyMQIYxOBUbbsffbhhj4KnY9ZImSapXOLRRB/c2qymwzuERAvlFIMMYnIoNtyOQYQxOxYbbEcgwBqdiw+0IZBiDU7HhdgQyjMGp2HA7lr0hIV/l+brr1E8K94Ze6Z5bLtLzgS49seEutbSeUlXlZ/Tdq6r19drx8npX5O2th9RO1ZKtX8nb9wfSQSAjoblX9erDxUF5ysry/l5toZDufi+gZVfUapbfr1nTJp57sCOij/+8O6/vb70ZlOoIZDiLQEZKZaPyP5m2evMWvfvJp1r98af67vQv5P39YnGyNkxADRmOa+vu0e927NJaSc/s2Km27h6nhwQ4gkCG4xqDQc2zLF0naY5lqTG4w+khAY4gkOGo6N3xvb29kqT7enu5S4ZrEchwVPTuuPrM19XiLhnuRSDDMbF3x1HcJcOtCGQ4pjEY1KxIRGWSDvT7VSZpZiTCXTJch2VvcMwHJ05qu9+vqxM87jlxoqDjAZxGIMMxD331GqeHABiFkgUAGIJABgBDEMgAYAgCGQAMQSADgCEIZAAwBIEMAIYgkAHAEAQyABiCQAYAQxDIAGAIAhkADEEgA4AhCGQAMASBDACGIJABwBAEMgAYgkAGAEMQyABgCAIZAAxBIAOAIQhkADAEgQwAhiCQAcAQHsuy0n+yx3NM0sH8DQcAStIEy7JGpXpSRoEMAMgfShYAYAgCGQAMQSADgCEIZAAwBIEMAIYgkAHAEAQyABiCQAYAQxDIAGCI/weTrN9imNWS1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X,y)\n",
    "plot_classifier(X, y, dt)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn = KNeighborsClassifier(n_neighbors=1)\n",
    "nn.fit(X,y)\n",
    "plot_classifier(X, y, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn = KNeighborsClassifier(n_neighbors=3)\n",
    "nn.fit(X,y)\n",
    "plot_classifier(X, y, nn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHcNJREFUeJzt3X10VOWdB/DvvIQXQxMKFboYao1Qc44xpVCNyohS60ENbzbFbgmUPaU9dsXDLthu0e4u4ikSi2ZdDtb14Hg63YT6Rhlg0IOiqAQqWtJ0CBBAeUuIATY1k0aSkJl794/JHSeTebkzc2fuM/d+P+fwR5g4uVH85uH3PL/fY5FlGUREpD+r3g9ARERBDGQiIkEwkImIBMFAJiISBAOZiEgQDGQiIkEwkImIBMFAJiISBAOZiEgQ9mQ+eWT+GLngy0WZehZT+MrfTwN5w/R+DCLKoiMdHf8ny/KViT4vqUAu+HIRqpZtT/2pCEvqlwIA7F+dqPOTEFG2lL7oOqPm81iyyDKXwwkA8Le36PwkRCQaBrIOlFAmIgrHQCYiEgQDmYhIEAxkIiJBMJB1xI09IgrHQNYJT1sQUSQGso542oKIwjGQBcBVMhEBDGTdsXRBRAoGsgDqKtwAgABDmcjUGMgC8Ps6MLZ6PWS9H4SIdMVAFkSNZwwAli6IzIyBLBDWk4nMjYEsGKWezFAmMh8GsmD8vo5QKBORuTCQBcZVMpG5MJAFxFUykTkxkAXHs8lE5sFAFpTf1wFf9RaeTSYyEQaywDx7bXo/AhFlEQOZiEgQDGQiIkEwkImIBGHX+wH0JEsSmr3b0VD/Krq72jCqYAKmOhagpGwuLFb+rCKi7DJtIMuShB11K3H2k3Pov7wKQBkudXuxe+s6nGh6D3MWPs1QJqKsMm3iNHu3D4TxXgCVACYDqIS/vx5nTrTgmHeHzk8YPPoGsGOPyCxMG8gN9a8OrIxHRLwyAv7+R3Bw3yt6PNYQHDZEZB6mDeTurjYAZTFevQHdvrZsPk5M4W3UDGUiYzNtII8qmADAG+PVQxhVOCGbjxOX39fBWclEJmDaQJ7qWAB73joAvRGv9MKetw7Tpt+vx2PFxZUykbGZNpBLyubi6klFsOc5AGwBcBzAFtjzHLh68kRcVzZH5yccSlkpF80ohb+9hcFMZDCmPfZmsVoxp6oGx7w7cHBfDbp9bRhVOAHTpi/GdWVzhD7ytlZaAXvFWFTtnA9/ewvsX52o9yMRkQZMG8hAMJRLpsxDyZR5ej9K0vy+DhxYvgvlG2YxlIkMQtxlICXU3NA6aLOPs5OJchsD2QBcDidnJxMZAAPZINyeTm72EeU4BrKBrJVWsIRBlMMYyAbkcjgxtno9SxhEOYaBbFA1njEsYRDlGAayga2VVgzq7gtcFGM+BxFFx0A2OKW7r/ihRZADAb0fh4jiYCCbxOrGmQC42UckMgayibgcThxYvgsyOKCISEQMZJNpbmiFr3oLAIayGl19l/GzN95EV99lvR+FTICBbEJuT2colFm+iK+uqQkfffop6poOD3mNYU1aYyCblBLKLF/E1tV3GZsPH4UbwB8OHxkSvPHCmigVDGQTC18p60nUlWZdUxPmyDLuBlAhy4OCN1FYE6WCgWxi82ePRuGqSlh0fg4RV5pK4P7HwFHB/wwEBgVvvLAmShUD2cS+1boVAGDTcZayqCtNJXAnDXw8CV8Eb6KwJkoVA9mk5s8ejZMba3UfbK/VSlPLskdk4CqU4H3xr3+NGdZE6WAgk260XGlqWfaoa2rCLZIEG4BTYb9sAMolCS/FCWuukikdDGSTcns6UfzQIl2HD8UrCyRD67LH6c86cSgvD7cPHz7k10cWC74iy1HD+mZJ4iqZ0mLqO/XMbnXjTMyvvg+FqyoRaG/Jai1ZCdEPo6w0yw8fQVXp9SgYPkzVe0Ure/zztG+l/GxPfvc7MV/75e534D1/AbfHeN3y2Wcpf10iBrLJuT2dmF+9BYWrKrN6WWpkWUARvtJUE6qRwZ5KoCcjXlgTpYslCxp0Hjlb5Yt4ZYFDeXk4rXKlqVXZg0gEXCETgMEr5cDFNtiunJDRr6fFSlPLsofyfv/2zrv4zXfuyMjqmigRrpApxO3pDF79FAjkRDt1vNMQqWywidigQubCQKZBajxjcmYanFZlD0DcBhUyF5YsaAi3pxMly3ehfMOsrJ++SIaWG2xan9RQsAxCyeAKmaJS5iabYRpcJluhWQahZDCQKSa3pxMuhxOAsa9+ytRJDZZBKFkMZErI5XAOWi0bacWcaG5FOiHKiXCULNaQSRW3pxMYWC3/yvpfaH2/KfSa3gOK0qFVg0qkbDeskDEwkClpa6UVgAOwF45F1c758Le3wGKzZfzsciaETmoMfCwD6L58GaOGDYMFqbdCxyuDaLFZSMbEQKaU+X0dcDmcWDn7b+hY9Yustl5rJfKkxnMHG/DCXw+hquS6lINT64YVMg/WkCltNZ4xoc2/XN7402oTTuuGFTIPBjJpZmz1er0fIS1abcJp2bBC5sKSBelOkmW8fvIUft/UgvOff47x+fn4UelE3Ft8DayW7Nz4p+UmHCfCUaq4QiZdSbKMf337A6zZ58ORjqfQ0bsPRzqewpp9nVjx9gFIspyV5+DUOBIBA5l09frJU/hT2wj0+A8AqAQwGUAlevwfYn/bMLxx8lSCd0if1meRtbzfj8yFgUwZFy+gft/Ugh7/fwIYEfHKCPT4V8PVlPlNQk6NI1GwhiwYWZLQ7N2OhvpX0d3VhlEFEzDVsQAlZXNhsYr/81MGhgwkCg+oyKNk5z//HEBZjHe7YeD1zIo8ixwpmbPI4Sc1qnjEjZLEQBaILEnYUbcSZz85h/7LqwCU4VK3F7u3rsOJpvcwZ+HTQodyjWfMkOugEgXU+Px8dPR6ESxVRDqE8fn5GX/uXJgaR+Yg7v/dJtTs3T4QxnsRXk/199fjzIkWHPPu0PkJE1MGEhXNKIW/vQX/+8H+uEfJflQ6ESPtawD0RrxTL0ba12BJae40mmRyahyZAwNZIA31rw6sjIfWU/39j+DgvleSej9ZknC00Y26jVV4/omZqNtYhaONbsiSpNkzx7JWWoEx//Q9vHS6JW5A3Vt8DW6Z0IeR9psAbAFwHMAWjLTfhFsnXMY9xddk/Fm1wpMalC6WLATS3dWGePXUbl+b6vcSofzxzFt/wRwZcec5WC0WPHPnzXjj5Cm4mn4eOoe8pHQi7sniOeR0sV2atMBAFsioggm41B27njqqUP3wnsHlD2XFPRn+/gqcOeHAMe8OlEyZp8FTR9fb04Xa321GQ8RqPFpAWS0WVFxbjIprizP2PJmWqalxZC4sWQhkqmMB7HnrEK2eas9bh2nT71f9XlqXP5Ll3bsJ06WAaeY5sF2atMAVskBKyubixKF3ceZjB/z9jwC4AcAh2PPW4erJE3Fd2RzV76Vl+SMVvgvHcXx4PsrxxSmJK+z9kHr6AKs15bGWomK7NGmBgSwQi9WKOVU1OObdgYP7atDta8OowgmYNn0xriubk1TNN5XyR29PF3ZtfhCzFv4WI0YWpP6NALhr0fNDfm/NlD04tbFW2EtT08HLTEkLLFkIxmK1omTKPFQtq8MDj+5B1bI6lEyZl/QGXCrlD+/eTTj3yX5461+I+p69PV3Y5lyE3p6upJ7FDNidR1pgIBtUSdlcXD2pCPY8B8KPk9nzHFHLH709XfDuc8INGYf2OaOGbqLAViPZe/mUzw2E/QKAwMW2QR/rKd4cZc61oGQwkOPQ8xxvupTyx133Lca4q2pwxahZGHdV8ONoR968ezdhriwFGzgkaUjoqgnsRFY3zoTL4URdhRvAwE3WF2PXspXQPrB8F8ZUr8eY6vWhQJcDgUEf6yneHGWunCkZFjmJ8Ybji8rkqmXbM/g44oh2jhfwhjbYRG9jTkZvTxdqq29GQ38PJgH4GMC0YVeg6pd/CtWSP3zzaXy9fhNq/X1YaB+Bs7f9FDfdtTKtrxuvpqyErHITSTyRl65aEFyJKzJ5rVRX32VUvPwqPvT7Q//uyu12eH6wAABQ8fKr2Oz3o2rg91hfNqfSF10HZVn+dqLPM0aiZIAR2pjVUlbHgxo4wlbJyur4MX8fAOBxf2/Kq+RoIssOShgrK+lE1kor4HI4cWD5LgCAr78fi9uAPT9+edD7qZFsiSFed55WN5CQeTCQY9D7HK9a6ZZVIsNWER66iQI7VX+wLsY1Dy0aVFf2t7egaEYpXA4n/L6OpN6vuaEVLocTj/R/E8cb9+C1Zx9XtcIOl0yJIdEc5dqmI5xrQUlhIMeg9zleNZSyytvuOlxoW4lL3W/iQttK7N5aix2bH1YVyvEaOG4J+NHw7rMJAztVzQ2tobqyy+FE8UOLAABPfunXKb9nZK37zhnqW6+TveQ03hzlGwMBXCtJnGtBSeE55Bi0bGPOFC3ao6M1cIQbduStQYGtUALbW/9C2rVkxerGmYBjJpDkyjhc5ObkziW342fXXavqn012dGasOcoyAF9fH+6I+H3OtaBEGMgxTHUswO6t6+Dvr8DgsoVyjnexXo8WkrisUpMwkKM1cIR7q/YBHOzpjBnY488fS+KJM0tZHf8urNZdfroVi2++FYnaXFK55DRWd95zBxtw6tBh/Lckca4FJYWBHIOWbcxaCr9R5ELbYWS6rJIosLNFzU0q3r2bcF+gb0iZoPaD/Xjw9jvivn+8zblkw1PLG0jIXBjIMWjZxqyVoUfxngYgdllFkc7VVGpGifb1dQdXx5HT5SQJ5adb8Y/FJ1GQlxf1CJzWozM514JSxUCOQ2lj1mpMZbr35Q2tGfcCqAYgblkFSH82s5paedeFj2PWum+EDY/4p2DbnRa0vt80JJQ5OpNEwUDOEi0Gxg+tGf8QwE4AtwFYBVHKKpHS3XxUUysfVzgq7ubk+PPH0Pq+P+prLDGQKBjIWaLFiYihR/GsAGoBvIRg+eIIxl11va5llWjS3XyMfgSxE3Z8H35Uo9vXhqplexI/SP3SqCWLeCUGZYpbV99lnoygjBPj/1gT0KLRZFTBBARrxuGsABYCeBjjrro+5elwmZTume5o37cdT8GKd2DDk0nVypOde8FZFJRN4vxfa3BaNJpoeaNINkX/QaJIvPk49PvuhB3PYBtkDMMfUXbjbFXPobYVW5FsowhRuhjIWZJuKAHJj9QURbo/SCK/bzsexVz0424AcyzApU51x/uUVmy1q2TOoqBsYyBniRar22RHaooi3R8k4d/3V/7hN8jD/2AtgqvVtbKEpv0vqm7hVmZb+BPMUo6cU8FZFJQNHL+ZJcopizMft0ZtNBE5ULUgS9LAme5Xws5035/05mP4GFDFQvsInHH8BAXjrlV9pLBkahHKN8wCEH0853MHG/D3piNwhZ1N/pHNhoLS63kEjpKmdvwmAzmLtAols4qc26z4GMA3LTZctt8Af/+/I5nZ1Wum7MHJjbUAvgjmyBnH4V+nnHONKQVqA5nH3rJI60YTs4mcTKewAbhFlvBu/0QAzwI4C+Br8Pcvw+njG+MeKVQGGi2pXwp/ewssAOrOXWSjCOmCgUw5I9Zkut6ev0OWLcjDXgTwApQVMlCNgP9L+HP9ywl/CLocTsyfPRqFqypxqv1TNoqQLhjIlDNiDTp6ds2NuNx3NYB6hDfdBFvKp8PXcVbV+7s9nVgC4IlpZRm99onUUZpyfvOdO0xTImLhknKeJOUBeATRmm6ARwdeT6xkahGAzN7BR+qZsSmHgUw5L7hfF7vpRu1+afmGWVB/vwhlklmbchjIlPNGj70G8Zpugq+ro/7MEWWSWZtyWEOmnBe83eWJGLe7PIGp0xfhaKM75hnl8DPJpL9Ubm8xCgYyCSPVedHK7S6nT9wMi1+CHy8COAN73jp8bdJEnGh6F2c/aYs59jRegwhlRrwNOy1vb8k1LFmQENK5QVtprZ40eRysOIQRed8NtZR/4/rbB8J4L4BKBE9fVMLfX48zJ1pwzLsj1E5N2RNrwy6yZV1hltZ1BjIJYfC86OjBGU9fXzfaPq7HNgDDLP2o/PFzKJkyDw37X1M99jTZ0ZyUmngbdpG3tyi/wptyjIwlC8o4NaWIdIfYe/duwlxZCm4CSRK89S/gprtWqh576nI4B7VRAyxhZEq0DTulFGH221sYyFmS7n16uUrt1VXpzIvu7ekKXnA6MHDocX8vpu1zoszxE4wqmIBL3eouglXaqAGEWqkBwALAxnDWRKINO7NfEGvcJBBIOvXRXKe2FJHOvGhldTxoE2hglZzq2FOXwwmXwwlf9RbICJYz/O0tCFxUN3uZoou3YUcM5KxItz4qgt6eLmxzLlI9d1ih9uqqVINTWR0/FjaOEwiukg/tc+Lrk+9Iaxaz29MZCueiGaWQA4FQOFNyzL5hpwYDOQu0uE9Pb969m3Duk/3w1r+Q1D+nthSR6hD7yAlw4ZtAtwT8OLT/Rc2G+q+VVsDlcOLA8l0AwGBOktk37NRgDTlDwmvGF9oOI9379PSkrELdkPGDgdrsiJEFqv5ZtTVc5ehacF50Tdi86Pg3aMeaAKcYf/6Y5mNPmxta0TxwVC5yIxAALDYb5LBVIDcHg8y+YacGAzkDhm5kPY1gfTTxxpKIYp1gUCPYRbcuRhfdOkybvjjqhueMe/9F1YZnrAlw2RK+EQgENwPlQABjq9fj9bYylG+YhUB7CzcFAdNv2KnBQM6AwTXjEQjWRasRHAcZPZREFe8Eg5pVstJFd+ZjR9Srq75RWqHqFEauCDWZeACgFSXVW1C4qhL+9haulCmh3PmTnkOG1ox/iODq+Dbk0m3RQPwTDGokupj1WJMn5zc841E2BYtmlLLmTAlxhZwBQzeyrABqAbyEYPniCMZddX3C+qjeIlfHimRXyfFquOk2hOiht6cLuzY/iFkLfzvo+4/1+0BwQ9BeMRZVO+cHr4qy2WC7UuxSFWWfmEmQ46KfqbUCWAjgYYy76npULatDyZR5woYxkPgEQ7InLqJJpyFEL7FOnCQ6ieL3dcDlcGJs9Xoen6OoTLFCznaXnJqNrFRk+/tQc4IhXcl00okg1omTZE6i1HjGAAO1ZnYEUjjDB7La1l0tJdrISqVmrMf3kY0TDIN/ePXCju/Dj9cAjBBywzPWiZNUT6Iom4CcyUyACUoWenTJJdrISiU4jdDtF014Q4gdS2HFO7Dhp0JueEZ2BSrdgL7PWqP+frJdjQAnzpmd4QNZry45ZSOralkdHnh0T9o1YyN0+0Wj/PC6o6ISeRY3tkHGcMsfcUdFpXBH3mKdOHnn5RVpnUQBgs0mymo5NDeD4Ww64vxpz5Bc3DSKxijfRzQWqxU9nW34ni0PdwOYZxuGHt+nQoVxvJkZ58/+GStjzNJQu0oumVqEJfVLB/2eMtSIzEOcP/EZks4UMZEY5fuIJlYpIJW/8mdKvBMnDgQPNWp1EiW8gYTNJOZi+EBOdYqYaIzyfUSjpvlk/uzRWDNlD5bUL8WS+qVYM2VPVp/Rd+E4Dg7PR/kVXx7060a7DQcsgMtuw22jxw56rWF4PnwqT6I0N7SGhhYBwSBmGJuP4U9ZZOLEgx6M8n1EUtN88uQtB3FyVS1O4YujYSc31qJk+WI0N7Rm5TljnThRygwWBEsMQ/lxYGpR6KNsPS/lJsMHcqpTxERjlO8jUmQpQBH+V/4/TK9BOWqBsO42C4DyDbNCU9f0UlfhRtXO+ZARvbzgb28ZdJytPMp7jK1eHzybTKZnkeXoP9ejGV9UJlct257BxyGzeav2AZw//VHM18d//Ubctej50JjL8JZjZcNL71uj7YXBluhUSgyB9pYhK2uWKoyn9EXXQVmWv53o8wy/QiaxqW0+Wd04E7+a0Yhz7zeFjoMpZYIl9Ut1D2UAKU10Y2cehWMgU844fO8afKtsa/QXG7P7LOH8vo5Q6YJjNikdDGTKGW5PJ9yYmfgTdRAeyoGLbZzkRinJzZ0gIgH5fR2hSW5EqWAgE2lIOS3B1mdKBQOZhGcvHIv5s0dj/uzRej+KKsrN1OrPLxEFMZBJaGum7EHVzvkoXFWJwlWVsBeO1fuRVGluaEXxQ4s4i4KSwkAmofmKvqn3I6RsdWNwA5LlC1KLgUwhsiThaKMbdRur8PwTM1G3sQpHG92QJSmrz9Hb04VtzkXo7elCjWeMEGeMU+VyOOGr3sLyBanCQCYAX9xI8ra7DhfaVuJS95u40LYSu7fWYsfmh7MaytHupnM5nHA5nPD7OrL2HFpxezpDt04TxcNAJgDi3EgSfjedaCM40/Hkl34NYKB8cTF3Z1dTZjGQCYA4N5JEu5vOCCJvnL786VlsO/gRFrz2FmbU7sD9296D55OTkJKYLUPGw0AmAGLcSJILg+rTVeMZg9/dugkLThfgsaNX4GjXBvzt8gc40vEU1uzrxIq3DzCUTYyBTADEuJFEzaB6I4hVHurxf4j9bcPwxslTCd6BjIqBTAD0v5Ek3p11RlslxysP9fhXw9XEzT+zYiATgOCNJFdPKoI9zwFgC4DjALbAnufIyo0k8e6sS/VuOlElKg+d//zzbD4OCYTT3giA/jeS+C4cx/Hh+ShHftTXx6u8my6TlNbtwlWVAABf9ZaU3uf12q/h2BEvgqWKSIcwPj/6vwMyPt4YQpSAcm+eIvb9eersPNeOXzdZ0BP4MwaXLXox0n4THps+GhXXFqfxFUg0vDGEopIlCc3e7WiofxXdXW0YVTABUx0LUFI2N2fv5cuU8CDWcuj8nPFFePuzD/CntpvQ418N5cLakfY1uHXCZdxTfI1mX4tyCwPZRJRuvOAO/yoAZbjU7cXuretwouk9zFn4NEM5zIHluwZdUKoVq8WCZ+68GW+cPAVX089x/vPPMT4/H0tKJ+Ke4mtgtVg0/5qUGxjIJjL4uJXyV+XJ8PdX4MwJB455d6Bkyjw9H1EozQ2taHY4QxesAtqtlK0WCyquLWZpggbhcshEROnGyzWrG2eGBhxxchtlEgPZREToxstl4ZPbGMyUCQxkExGhGy/XuT2dcDmcKH5oEa4amODGKW6kFQayiejdjWckqxtnYq20AnUVbgCc4kbaYCCbiN7deEYUOcWNq2VKBxtDTEaWpIFuvFfCuvHuz0o3nhkoZ5ctAGwanl2m3MbGEIrKYrWiZMo8Hm/LEJfDiZKpRSjfMCu0WtayqYSMjYFMpDHl/DKAjJxhJuPi31GJMohnmCkZDGSiLIg8w0wUDQOZKEuUM8xFYeeXGc4UjjVkoixbK60AHIC9cCyqds6Hv70FFpsNtivZmGN2XCET6YRnmCkSA5lIZzWeMdz4IwAMZCJhuBxOHFi+ixt/JsZAJhJIc0NraHgRN/7Mh4FMJCDl/HKqF6lSbmIgE+UArpLNgYFMJDC3p5OrZBPhOWQSCm/FHsqz14YqBFfJnIdhbOb8E05CUm7FfttdhwttK3Gp+01caFuJ3VtrsWPzw5AlSe9H1IXf18FVskkwkEkYg2/FrgQwGUAl/P31OHOiBce8O3R+Qv24PZ0AWEs2OgYyCYO3YsfHVbLxMZBJGLwVOz63pzN0PpmMiYFMwuCt2In9peg+AGB7tUExkEkYvBU7MeUYnPqbMCmXMJBJGLwVm8yO55BJGBarFXOqagZuxa4JuxV7MW/FjiJwsY0zlA2GgUxC6O3pwq7ND2LWwt/yVuwE3J5OrKxej45Vv9D7UUhjXHKQELx7N+HcJ/vhrX9B70fJCSdRrPcjUAYwkEl3vT1d8O5zwg0Zh/Y50dvTpfcjEemCgUy68+7dhLmyhLsBVEgSV8lkWgxk0pWyOn7M3wcAeNzfy1VyEnge2VgYyKQrZXU8aeDjSeAqWQ2eRzYmBjLpJnJ1rOAqmcyKgUy68e7dhOlSADYAp8J+2QDcEvBzlUymw3PIpBvfheM4Pjwf5ciP+vr488ey/ERE+mIgk27uWvS83o9gCIH2Fth4k4ghsGRBlKN4357xMJCJiATBQCYiEgQDmYhIEAxkohwngx17RsFAJsphbk8nxlav1/sxSCMMZCIiQTCQiYgEwUAmIhIEA5kox51EMTf2DIKBTJTjuLFnHAxkIiJBMJCJiATBQCYyCN4ekvsYyEQG8HpbGQBu7OU6BjKRATQ3tHJjzwAYyEREgmAgExEJgoFMZCDc2MttDGQig6jxjAHAjb1cxkAmMhBu7OU2BjIRkSAYyEREgmAgExkMN/Zyl0WW1f/ns1gsFwGcydzjEBEZ0tWyLF+Z6JOSCmQiIsocliyIiATBQCYiEgQDmYhIEAxkIiJBMJCJiATBQCYiEgQDmYhIEAxkIiJBMJCJiATx/4U/9SBeI58yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn = KNeighborsClassifier(n_neighbors=10)\n",
    "nn.fit(X,y)\n",
    "plot_classifier(X, y, nn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=1)\n",
    "rf.fit(X,y)\n",
    "plot_classifier(X, y, rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=5)\n",
    "rf.fit(X,y)\n",
    "plot_classifier(X, y, rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=50)\n",
    "rf.fit(X,y)\n",
    "plot_classifier(X, y, rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Averaging KNN, Decision Tree, Random Forest\n",
    "\n",
    "- The next cell will be slow the first time you run it, as it needs to download the data set (internet connection needed).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.datasets\n",
    "\n",
    "# load the newsgroups data\n",
    "train = sklearn.datasets.fetch_20newsgroups_vectorized(subset='train')\n",
    "X_train = train.data\n",
    "y_train = train.target\n",
    "\n",
    "test = sklearn.datasets.fetch_20newsgroups_vectorized(subset='test')\n",
    "X_test = test.data\n",
    "y_test = test.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n = 11314\n",
      "d = 130107\n",
      "t = 7532\n",
      "number of classes = 20\n"
     ]
    }
   ],
   "source": [
    "print(\"n =\", X_train.shape[0])\n",
    "print(\"d =\", X_train.shape[1])\n",
    "print(\"t =\", X_test.shape[0])\n",
    "num_classes = len(np.unique(y_train))\n",
    "print(\"number of classes =\", num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the uniformity of the training labels\n",
    "plt.hist(y_train, density=True, bins=np.unique(y_train));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, they aren't perfectly uniform but the situation isn't too bad either."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_errs(model):\n",
    "    train_err = 1-model.score(X_train, y_train)\n",
    "    test_err  = 1-model.score(X_test,  y_test)\n",
    "\n",
    "    print(\"Train error:\", train_err)\n",
    "    print(\"Test  error:\", test_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error: 8.838607035532853e-05\n",
      "Test  error: 0.4403876792352629\n",
      "33.9 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "print_errs(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error: 0.001502563196040252\n",
      "Test  error: 0.4419808815719597\n",
      "8.57 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print_errs(rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above: ironically, the random forest trains faster than the decision tree, presumably because it's only considering $\\sqrt{d}$ features per split and $d$ is very large here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error: 0.20867951210889168\n",
      "Test  error: 0.5916091343600638\n",
      "38 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print_errs(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error: 0.20867951210889168\n",
      "Test  error: 0.5916091343600638\n"
     ]
    }
   ],
   "source": [
    "# run this cell again because Jupyter is buggy :(\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print_errs(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgelbart/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error: 8.838607035532853e-05\n",
      "Test  error: 0.3349707912904939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgelbart/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "classifiers = {\n",
    "    \"decision tree\" : dt,\n",
    "    \"random forest\" : rf,\n",
    "    \"KNN\"           : knn\n",
    "}\n",
    "\n",
    "ensemble = VotingClassifier(classifiers.items())\n",
    "ensemble.fit(X_train, y_train)\n",
    "\n",
    "print_errs(ensemble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall the results here aren't that amazing. And yet it's cool that the ensemble is better than all 3 individual classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (bonus) XGBoost\n",
    "\n",
    "- This requires some extra installation: `conda install py-xgboost` worked for me.\n",
    "- This code takes several minutes to run (for me at least).\n",
    "- The following code cell is a hack to make XGBoost work on macOS, see https://github.com/dmlc/xgboost/issues/1715"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ['KMP_DUPLICATE_LIB_OK']='True'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb = XGBClassifier(n_estimators=10, max_depth=10, learning_rate=1, objective='multi:softmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3min 16s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgelbart/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train error: 0.0003535442814212031\n",
      "Test  error: 0.3283324482209241\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mgelbart/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "print_errs(xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This uses boosting with decision trees -- a specific form of boosting called [gradient boosting](https://en.wikipedia.org/wiki/Gradient_boosting). XGBoost tends to do very well on a lot of tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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