ods a2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz, DecisionTreeRegressor"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.linspace(-2, 2, 7)\n",
    "y = X ** 3 # original dependecy \n",
    "\n",
    "plt.scatter(X, y)\n",
    "plt.xlabel(r'$x$')\n",
    "plt.ylabel(r'$y$');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= X.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "max_leaf_nodes 1 must be either None or larger than 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-2e529b0e36df>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mtree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmax_leaf_nodes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1140\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1141\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1142\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m   1143\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python3\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    246\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mmax_leaf_nodes\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m             raise ValueError((\"max_leaf_nodes {0} must be either None \"\n\u001b[1;32m--> 248\u001b[1;33m                               \"or larger than 1\").format(max_leaf_nodes))\n\u001b[0m\u001b[0;32m    249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: max_leaf_nodes 1 must be either None or larger than 1"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeRegressor(max_depth=1,max_leaf_nodes=1)\n",
    "tree.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.n_classes_, tree.n_features_, tree.n_outputs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydotplus.graphviz import graph_from_dot_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = export_graphviz(                           # Create dot data\n",
    "    tree, filled=True, rounded=True,\n",
    "    class_names=['y'],\n",
    "    feature_names=['X'],\n",
    "    out_file=None\n",
    ")\n",
    "\n",
    "graph = graph_from_dot_data(dot_data)                 # Create graph from dot data\n",
    "graph.write_png('tree_2.png')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmzYtV111VcaNG9c6EgAAq+GdGtA1brrppnz84x9PKSWXX355XvziF7eOBDCgxX3LM6aULFzUl6lTJmRlrZk8wdsyYPTxmw/oCr/97W+fnDf60Y9+NG9+85tbRwIY0NJlK3Lpzffk+tvmZ+myFZk0fmx2f830HLTr1pk4fmzreABDSiEFRrwVK1Zk3333ze9+97vssssu+fjHP946EsCAFvctz6U335Prbrnvye8tWbYi191yX0pJDth5K0dKgVHFHFJgxPvEJz6Rm266KS984Qtz5ZVXZuxYRxiA4WlMKbn+tvkDLrv+1vkZU8oQJwJoSyEFRrRvfetbOemkk1JKyRVXXJG/+qu/ah0JYLUWLurL0mUrBly2ZNmKLFzcN8SJANpSSIER66GHHsp+++2XWmv++Z//ObvttlvrSADPaOqUCaudJzpp/NhMnTxhiBMBtKWQAiPS8uXLs88+++T3v/99dtttt3zsYx9rHQlgjVbWmj1eM33AZXvsMD0rax3iRABtmTUPjEgf//jH853vfCebbrpprrjiCvNGGZbc2oOnmzxhXA7adeuUsmrO6JL+q+zuscP0zNrFVXaB0afUDnwSV0p5S5LTk4xNcn6t9eRnWn/GjBl13rx5g35eYHS68cYb89a3vjWllPz7v/97dtlll9aR4C8sXbYiF990t1t7MKAnP6xY3Jepkydk5cqayRN9WAF0j1LKrbXWGWtab9C/+UopY5OcmeRNSR5I8oNSytdrrT8d7LYBnu43v/lN9t9//9Rac9JJJymjDEtu7cGaPDH+08ZPbpwEoK1OzCF9XZJf1FrvrbX2Jbk6yZ4d2C7An3li3ugf/vCHvOlNb8rxxx/fOhIMyK09AGDtdKKQvjjJr5/y+IH+7/2ZUsrsUsq8Usq8BQsWdOBpgdHmYx/7WP7zP/8zL3rRi3L55ZdnzBjXZWN4cmsPAFg7nXg3N9DHvH8xMbXWOqfWOqPWOmPatGkdeFpgNLnhhhty8sknZ8yYMbnqqqvyghe8oHUkWC239gCAtdOJQvpAkpc85fFmSR7swHYBkiS//vWvc8ABByRJPvnJT+YNb3hD40TwzNzaAwDWTieuqPCDJC8vpWyR5DdJ9k6ybwe2C5Bly5Zlr732ysMPP5y3vvWt+dCHPtQ6EqyRW3sAwNoZdCGttS4vpRyZ5Masuu3LhbXWOwedDCDJ8ccfn+9973vZbLPNcumllw7LeaPuNclAJo4fmwN23iqzdtn6z27toYwCwP/oyDumWusNSW7oxLYAnvCNb3wjp5xySsaOHZurr746m2yySetIf2HpshW59OZ73GuSAbm1BwA8Mx/hA8PS/fffn1mzZiVJPvOZz2SnnXZqnOgvudckAMDgDL9z34BRr6+vL3vttVceeeSR7LHHHjnuuONaRxqQe00CAAyOQgoMOx/+8Idzyy235CUveUkuueSSYTlvNHGvSQCAwRqe7/KAUetf/uVf8sUvfjHjxo3L3Llz8/znP791pNVyr0kAgMFRSIFh47777stBBx2UJPnsZz+bHXfcsW2gNXCvSQCAwXG1DWBYeGLe6KOPPpo999wzxx57bOtIa+RekwAAg6OQAsPCBz7wgfzgBz/IX//1X+eiiy5KGSEXBHKvSQCA504hBZq79tpr86UvfSnjx4/P3Llzs/HGG7eO9Ky41yQAwHNjDinQ1C9/+csccsghSZLPf/7zed3rXtc4EQAAQ0UhBZpZsmRJZs6cmYULF+Yf/uEf8v73v791JAAAhpBCCjRz3HHH5bbbbssWW2yRCy64YMTMGwUAoDMUUqCJuXPn5qyzzsqECRPS29ubjTbaqHUkAACGmEIKDLmf//znOeyww5Ikp556anbYYYfGiQAAaEEhBYbUE/NGH3vssfT09OSII45oHQkAgEYUUmBIHXPMMbn99tvz0pe+NOedd555owAAo5hCCgyZq666Kueee24mTpyY3t7ebLjhhq0jAQDQkEIKDIm77747s2fPTpKcdtpp2X777RsnAgCgNYUUWOcWLVqUnp6e/OlPf8ree++d97znPa0jAQAwDCikwDp39NFH54477sjLX/7yzJkzx7xRAACSKKTAOnb55Zfn/PPPz6RJk9Lb25sNNtigdSQAAIYJhRRYZ+66664nT8/90pe+lFe96lWNEwEAMJwopMA68fjjj6enpyeLFi3Kfvvtl8MOO6x1JAAAhhmFFFgnjjzyyNx5553ZZpttcs4555g3CgDAX1BIgY67+OKLc/HFF2fy5Mnp7e3N+uuv3zoSAADDkEIKdNSdd96ZI444Ikly5plnZrvttmucCACA4UohBTrmT3/6U3p6erJ48eLMmjUrBx98cOtIAAAMYwop0BG11hxxxBG56667su222+bMM89sHQkAgGFOIQU64sILL8xll12WKVOmpLe3N+utt17rSAAADHMKKTBoP/7xj3PkkUcmSc4+++xsu+22jRMBADASKKTAoDz22GPp6enJkiVLcsghh+TAAw9sHQkAgBFCIQWes1pr3vOe9+See+7JdtttlzPOOKN1JAAARhCFFHjOzjvvvFx11VVZb7310tvbmylTprSOBADACKKQAs/J7bffnve///1JknPPPTfbbLNN40QAAIw0CinwrC1cuDA9PT1ZunRp3v3ud2e//fZrHQkAgBFIIQWelVpr3v3ud+cXv/hFXvWqV+X0009vHQkAgBFKIQWelbPPPjtz587N+uuvn7lz52by5MmtIwEAMEIppMBau/XWW3PssccmSc4///xstdVWjRMBADCSKaTAWnn00Uczc+bM9PX15fDDD89ee+3VOhIAACOcQgqsUa01hx56aO69995sv/32OfXUU1tHAgCgCyikwBp9+ctfzrXXXpsNNtggc+fOzaRJk1pHAgCgCwyqkJZSPl9K+Vkp5cellK+WUjbqVDBgePjBD36Q4447Lkly4YUX5mUve1njRAAAdIvBHiH9ZpLtaq2vTHJPko8MPhIwXDzyyCOZOXNmli1bliOPPDLvete7WkcCAKCLDKqQ1lr/rda6vP/hfyfZbPCRgOGg1pqDDz44v/rVrzJjxoyccsoprSMBANBlOjmH9JAk/9rB7QENnX766fna176WDTfcMHPnzs3EiRNbRwIAoMuMW9MKpZRvJdl0gEUn1Fq/1r/OCUmWJ7niGbYzO8nsJJk+ffpzCgsMjVtuuSUf+MAHkiQXXXRRtthii8aJAADoRmsspLXWNz7T8lLKrCRvS7JbrbU+w3bmJJmTJDNmzFjtekBbDz/8cGbOnJnly5fn6KOPzjve8Y7WkQAA6FJrLKTPpJTyliQfSrJzrXVRZyIBrdRac9BBB2X+/Pl53etel8997nOtIwEA0MUGO4f0y0k2SPLNUsrtpZRzOpAJaOQLX/hCvvGNb2SjjTbKNddckwkTJrSOBABAFxvUEdJaqxsSQpf4r//6r3z4wx9OklxyySXZfPPN2wYCAKDrDaqQwmiyuG95xpSShYv6MnXKhKysNZMndMcu9Mc//jF77bVXVqxYkeOOOy5vf/vbW0cCAGAU6I5307COLV22IpfefE+uv21+li5bkUnjx2b310zPQbtunYnjx7aONygrV67MgQcemAceeCA77rhjPvOZz7SOBADAKKGQwhos7lueS2++J9fdct+T31uybEWuu+W+lJIcsPNWI/pI6ec///nccMMNed7znpdrrrkm48ePbx0JAIBRYrAXNYKuN6aUXH/b/AGXXX/r/IwpZYgTdc53v/vdnHDCCUmSSy+91D2CAQAYUgoprMHCRX1ZumzFgMuWLFuRhYv7hjhRZyxYsODJeaMf/OAHs8cee7SOBADAKKOQwhpMnTJhtfNEJ40fm6mTR96tUVauXJkDDjggDz74YHbaaad88pOfbB0JAIBRSCGFNVhZa/Z4zcCnsu6xw/SsrHWIEw3eySefnBtvvDGbbLJJrr76avNGAQBoYuReiQWGyOQJ43LQrlunlFVzRpf0X2V3jx2mZ9YuI+8qu9/5znfysY99LEly2WWXZbPNNmucCACA0UohhbUwcfzYHLDzVpm1y9ZZuLgvUydPyMqVdcSV0d/97nfZZ599snLlynzkIx/JW97yltaRAAAYxRRSWEtP3Npl2vjJjZM8NytWrMj++++fhx56KG94wxty0kkntY4EAMAoZw4pjBKf+tSn8q1vfSvTpk3LVVddlXHjfB4FAEBbCimMAt/+9rfz8Y9/PKWUXHHFFXnRi17UOhIAACik0O1++9vfZt99902tNR/96Efzpje9qXUkAABIopBCV1uxYkX23Xff/O53v8uuu+6aE088sXUkAAB4kkIKXeykk07KTTfdlBe+8IW58sorM3bsyLoqMAAA3U0hhS71zW9+M5/4xCeenDe66aabto4EAAB/RiGFLvTggw9mv/32S601J554YnbbbbfWkQAA4C8opNBlli9fnn322ScLFizIG9/4xnz0ox9tHQkAAAakkEKXOfHEE/Mf//Ef2XTTTXP55ZebNwoAwLClkEIXufHGG/PpT386Y8aMydVXX50XvvCFrSMBAMBqKaTQJR544IHsv//+SVZdXXfnnXdunAgAAJ6ZQgpd4Il5o3/4wx/y5je/OR/5yEdaRwIAgDVSSKELfPSjH813v/vdvOhFL8rll1+eMWPs2gAADH/etcIId/311+ezn/1sxo4dm6uvvjrTpk1rHQkAANaKQgoj2Pz583PggQcmST75yU/m9a9/feNEAACw9hRSGKGWLVuWvffeOw8//HB23333fPCDH2wdCQAAnhWFFEao448/Pt/73vey2Wab5ZJLLjFvFACAEcc7WBiBvvGNb+SUU055ct7oJpts0joSAAA8awopjDD3339/Zs2alST5zGc+k5122qlxIgAAeG4UUhhB+vr6MnPmzDzyyCN529veluOOO651JAAAeM4UUhhBPvShD+X73/9+pk+fbt4oAAAjnnezMEJ89atfzWmnnZZx48blmmuuyfOe97zWkQAAYFAUUhgB7rvvvhx88MFJks997nPZcccdGycCAIDBU0hhmFu6dGlmzpyZRx99NHvuuWeOOeaY1pEAAKAjFFIY5j7wgQ9k3rx52XzzzXPRRRellNI6EgAAdIRCCsPYV77ylZxxxhkZP3585s6dm4033rh1JAAA6BiFFIapX/7ylzn00EOTJKecckpe+9rXNk4EAACdpZDCMLRkyZL09PRk4cKFeec735mjjjqqdSQAAOg4hRSGoeOOOy4//OEPs+WWW+aCCy4wbxQAgK6kkMIwc8011+Sss87KhAkTMnfu3Gy44YatIwEAwDqhkMIw8vOf/zzvfve7kySnnnpqdthhh8aJAABg3elIIS2l/FMppZZSNunE9mA0Wrx4cXp6evLYY4+lp6cnRxxxROtIAACwTg26kJZSXpLkTUnmDz4OjF7HHHNMfvSjH+VlL3tZzj//fPNGAQDoep04QvrFJB9MUjuwLRiVrrzyysyZMycTJ05Mb29vpk6d2joSAACsc4MqpKWUtyf5Ta31R2ux7uxSyrxSyrwFCxYM5mmhq9x9992ZPXt2kuT000/Pq1/96saJAABgaIxb0wqllG8l2XSARSckOT7Jm9fmiWqtc5LMSZIZM2Y4mgpJFi1alJ6enjz++OPZZ599niymAAAwGqyxkNZa3zjQ90spr0iyRZIf9c912yzJbaWU19Vaf9vRlNCl3v/+9+eOO+7IVlttlXPPPde8UQAARpU1FtLVqbXekeQFTzwupfwqyYxa6x86kAu63mWXXZYLLrggkyZNSm9vbzbYYIPWkQAAYEi5Dyk08NOf/jTvfe97kyRnnHFGXvnKVzZOBAAAQ+85HyF9ulrr5p3aFnSzxx9/PD09PVm0aFH233//HHrooa0jAQBAE46QwhB73/vel5/+9KfZZpttcvbZZ5s3CgDAqKWQwhC6+OKLc8kll2Ty5Mnp7e3N+uuv3zoSAAA0o5DCEPnJT36SI444Ikly1llnZbvttmucCAAA2lJIYQj86U9/Sk9PTxYvXpxZs2bloIMOah0JAACaU0hhHau15vDDD8/PfvazbLvttjnzzDNbRwIAgGFBIYV17IILLsjll1+eKVOmpLcQZTFbAAANPElEQVS3N+utt17rSAAAMCwopLAO/fjHP85RRx2VJDnnnHOy7bbbNk4EAADDh0IK68hjjz2Wnp6eLFmyJIceemgOOOCA1pEAAGBYUUhhHai15j3veU/uueeevOIVr8gZZ5zROhIAAAw7CimsA3PmzMlVV12V9dZbL3Pnzs3kyZNbRwIAgGFHIYUO++EPf5ijjz46yapius022zROBAAAw5NCCh20cOHCzJw5M0uXLs3s2bOz7777to4EAADDlkIKHVJrzWGHHZZf/OIXedWrXpXTTjutdSQAABjWFFLokLPPPju9vb3ZYIMN0tvba94oAACsgUIKHXDrrbfm2GOPTZKcd955efnLX944EQAADH8KKQzSo48+mpkzZ6avry+HH3549tprr9aRAABgRFBIYRBqrTnkkENy7733Zvvtt8+pp57aOhIAAIwYCikMwhlnnJHrrrsuU6dOTW9vbyZNmtQ6EgAAjBgKKTxH3//+9/NP//RPSZILL7wwL33pSxsnAgCAkUUhhefgkUceycyZM7Ns2bIcddRReec739k6EgAAjDgKKTxLtdYcfPDBuf/++/Pa1742n//851tHAgCAEUkhhWfptNNOy9e+9rVsuOGGueaaazJx4sTWkQAAYEQa1zrAcHTllVfmuuuuax2DYajWmq9//etJkosuuihbbLFF40QAADByKaQDuPPOO3Pttde2jsEwduyxx+Yd73hH6xgAADCiKaQD2GeffbL99tu3jsEwtckmm2TnnXduHQMAAEY8hXQA2223XbbbbrvWMQAAALqaixoBAADQhEIKAABAEwopAAAATSikAAAANKGQAgAA0IRCCgAAQBMKKQAAAE0opAAAADShkAIAANCEQgoAAEATCikAAABNKKQAAAA0oZACAADQhEIKAABAE4MupKWUo0opd5dS7iylfK4ToQAAAOh+4wbzw6WUXZPsmeSVtdalpZQXdCYWAAAA3W6wR0gPT3JyrXVpktRafz/4SAAAAIwGgy2kWyV5fSnlllLKd0opr13diqWU2aWUeaWUeQsWLBjk0wIAADDSrfGU3VLKt5JsOsCiE/p/fuMkOyZ5bZK5pZQta6316SvXWuckmZMkM2bM+IvlAAAAjC5rLKS11jeublkp5fAk1/UX0O+XUlYm2SSJQ6AAAAA8o8GesvsvSf4+SUopWyWZkOQPgw0FAABA9xvUVXaTXJjkwlLKT5L0JZk10Om6AAAA8HSDKqS11r4k+3coCwAAAKPIYE/ZBQAAgOdEIQUAAKAJhRQAAIAmFFIAAACaUEgBAABoQiEFAACgCYUUAACAJhRSAAAAmlBIAQAAaEIhBQAAoAmFFAAAgCYUUgAAAJpQSAEAAGhCIQUAAKAJhRQAAIAmFFIAAACaUEgBAABoQiEFAACgCYUUAACAJhRSAAAAmlBIAQAAaEIhBQAAoAmFFAAAgCYUUgAAAJpQSAEAAGhCIQUAAKAJhRQAAIAmFFIAAACaUEgBAABoQiEFAACgCYUUAACAJhRSAAAAmlBIAQAAaEIhBQAAoAmFFAAAgCYUUgAAAJpQSAEAAGhCIQUAAKAJhRQAAIAmFFIAAACaGNc6wHCzuG95xpSShYv6MnXKhKysNZMn+GsCAADotEE1rVLKq5Ock2RSkuVJjqi1fr8TwVpYumxFLr35nlx/2/wsXbYik8aPze6vmZ6Ddt06E8ePbR0PAACgqwz2lN3PJfl/a62vTvLP/Y9HpMV9y3PxTXfnulvuy9JlK5IkS5atyHW33JdLbr47i/uWN04IAADQXQZbSGuSqf1fb5jkwUFur5kxpeT62+YPuOz6W+dnTClDnAgAAKC7DXZy5DFJbiylnJJV5fbvVrdiKWV2ktlJMn369EE+bectXNT35JHRp1uybEUWLu7LtPGThzgVAABA91pjIS2lfCvJpgMsOiHJbkmOrbVeW0qZmeSCJG8caDu11jlJ5iTJjBkz6nNOvI5MnTIhE8ePHbCUTho/NlMnT2iQCgAAoHutsZDWWgcsmElSSrk0ydH9D3uTnN+hXENuZa3Z4zXTc90t9/3Fsj12mJ6Vddh1aAAAgBFtsHNIH0yyc//Xf5/k54PcXjOTJ4zLQbtunXfuuEUm9V9Rd9L4sXnnjltk1i5bu/ULAABAhw22Zb07yemllHFJlqR/juhINXH82Byw81aZtcvWWbi4L1MnT8jKldUtXwAAANaBQRXSWut3k+zQoSzDwhNHQl3ACAAAYN0a7Cm7AAAA8JwopAAAADShkAIAANCEQgoAAEATCikAAABNKKQAAAA0oZACAADQhEIKAABAEwopAAAATSikAAAANKGQAgAA0IRCCgAAQBMKKQAAAE0opAAAADRRaq1D/6SlLEhy/5A/8bOzSZI/tA7BXzAuw48xGX6MyfBjTIYn4zL8GJPhx5gMPyNlTP661jptTSs1KaQjQSllXq11Rusc/DnjMvwYk+HHmAw/xmR4Mi7DjzEZfozJ8NNtY+KUXQAAAJpQSAEAAGhCIV29Oa0DMCDjMvwYk+HHmAw/xmR4Mi7DjzEZfozJ8NNVY2IOKQAAAE04QgoAAEATCikAAABNKKT9SimfL6X8rJTy41LKV0spG61mvbeUUu4upfyilPLhoc452pRSekopd5ZSVpZSVnt561LKr0opd5RSbi+lzBvKjKPNsxgT+8oQKaU8r5TyzVLKz/v/u/Fq1lvRv4/cXkr5+lDnHA3W9LovpUwspVzTv/yWUsrmQ59ydFmLMTmolLLgKfvGYS1yjiallAtLKb8vpfxkNctLKeVL/WP241LKa4Y642izFmOySynl0afsJ/881BlHm1LKS0opN5VS7up/33X0AOt0xb6ikP6PbybZrtb6yiT3JPnI01copYxNcmaStybZNsk+pZRthzTl6POTJP+Q5D/WYt1da62v7qb7Mg1TaxwT+8qQ+3CSf6+1vjzJv/c/Hsji/n3k1bXWtw9dvNFhLV/3hyZ5pNb6siRfTPLZoU05ujyL30XXPGXfOH9IQ45OFyd5yzMsf2uSl/f/mZ3k7CHINNpdnGcekyT5z6fsJycNQabRbnmS42qtf5NkxyTvG+D3V1fsKwppv1rrv9Val/c//O8kmw2w2uuS/KLWem+ttS/J1Un2HKqMo1Gt9a5a692tc/A/1nJM7CtDa88kl/R/fUmS/6dhltFsbV73Tx2rryTZrZRShjDjaON30TBUa/2PJA8/wyp7Jrm0rvLfSTYqpfzV0KQbndZiTBhitdaHaq239X/9WJK7krz4aat1xb6ikA7skCT/OsD3X5zk1095/ED+8oVBGzXJv5VSbi2lzG4dBvvKEHthrfWhZNU/YElesJr1JpVS5pVS/ruUorR23tq87p9cp/9D0EeTPH9I0o1Oa/u76J39p7t9pZTykqGJxjPwb8jw9L9KKT8qpfxrKeVvW4cZTfqnd2yf5JanLeqKfWVc6wBDqZTyrSSbDrDohFrr1/rXOSGrDpFfMdAmBvie++YM0tqMy1rYqdb6YCnlBUm+WUr5Wf+nfTwHHRgT+0qHPdOYPIvNTO/fT7ZM8u1Syh211l92JiFZu9e9fWNorc3f9zeSXFVrXVpKeW9WHcH++3WejGdiPxl+bkvy17XWP5VSdk/yL1l1mijrWCll/STXJjmm1rrw6YsH+JERt6+MqkJaa33jMy0vpcxK8rYku9WBb9D6QJKnfnK6WZIHO5dwdFrTuKzlNh7s/+/vSylfzarTtBTS56gDY2Jf6bBnGpNSyu9KKX9Va32o/1Sd369mG0/sJ/eWUm7Oqk9bFdLOWZvX/RPrPFBKGZdkwzhNbl1a45jUWv/4lIfnxbze4cC/IcPMU4tQrfWGUspZpZRNaq1/aJmr25VSxmdVGb2i1nrdAKt0xb7ilN1+pZS3JPlQkrfXWhetZrUfJHl5KWWLUsqEJHsncaXKxkop65VSNnji6yRvzqoL79COfWVofT3JrP6vZyX5i6PYpZSNSykT+7/eJMlOSX46ZAlHh7V53T91rN6V5Nur+QCUzljjmDxtvtXbs2qeFm19PcmB/VcQ3THJo09MS6CNUsqmT8x3L6W8Lqs6xB+f+acYjP6/7wuS3FVrPXU1q3XFvjKqjpCuwZeTTMyq0z2T5L9rre8tpbwoyfm11t1rrctLKUcmuTHJ2CQX1lrvbBe5+5VS3pHkjCTTklxfSrm91vq/nzouSV6Y5Kv94zYuyZW11v/TLHSXW5sxsa8MuZOTzC2lHJpkfpKeJCmrbsvz3lrrYUn+Jsm5pZSVWfVG4uRaq0LaQat73ZdSTkoyr9b69ax6c3FZKeUXWXVkdO92ibvfWo7J+0spb8+q6ToPJzmoWeBRopRyVZJdkmxSSnkgyYlJxidJrfWcJDck2T3JL5IsSnJwm6Sjx1qMybuSHF5KWZ5kcZK9fZi2zu2U5IAkd5RSbu//3vFJpifdta8UryUAAABacMouAAAATSikAAAANKGQAgAA0IRCCgAAQBMKKQAAAE0opAAAADShkAIAANDE/w81oNLqEz4x5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.scatter(X, y, c='steelblue',                  # Plot actual target against features\n",
    "            edgecolor='white', s=70)\n",
    "plt.plot(X, tree.predict(X),                      # Plot predicted target against features\n",
    "         color='black', lw=2)\n",
    "#plt.xlabel('% lower status of the population [LSTAT]')\n",
    "#plt.ylabel('Price in $1000s [MEDV]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_var_criterion(X,y,t):\n",
    "    left,right = split(X,t)\n",
    "    l = left**3\n",
    "    r = right**3\n",
    "    le = len(left)/len(X)*np.var(l)\n",
    "    ri = len(right)/len(X)*np.var(r)\n",
    "    q = np.var(y) - le-ri\n",
    "    \n",
    "    return q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('mlbootcamp5_train.csv.txt', \n",
    "                 index_col='id', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>18393</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>62.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20228</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>18857</td>\n",
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       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17474</td>\n",
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       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "id                                                                          \n",
       "0   18393       2     168    62.0    110     80            1     1      0   \n",
       "1   20228       1     156    85.0    140     90            3     1      0   \n",
       "2   18857       1     165    64.0    130     70            3     1      0   \n",
       "3   17623       2     169    82.0    150    100            1     1      0   \n",
       "4   17474       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "    alco  active  cardio  \n",
       "id                        \n",
       "0      0       1       0  \n",
       "1      0       1       1  \n",
       "2      0       0       1  \n",
       "3      0       1       1  \n",
       "4      0       0       0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age in years'] = df['age'].apply(lambda x : math.floor(x/365.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot = pd.get_dummies(df, columns=['cholesterol'], prefix=['cholesterol'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      age  gender  height  weight  ap_hi  ap_lo  gluc  smoke  alco  active  \\\n",
       "id                                                                           \n",
       "0   18393       2     168    62.0    110     80     1      0     0       1   \n",
       "1   20228       1     156    85.0    140     90     1      0     0       1   \n",
       "2   18857       1     165    64.0    130     70     1      0     0       0   \n",
       "3   17623       2     169    82.0    150    100     1      0     0       1   \n",
       "4   17474       1     156    56.0    100     60     1      0     0       0   \n",
       "\n",
       "    cardio  age in years  cholesterol_1  cholesterol_2  cholesterol_3  \n",
       "id                                                                     \n",
       "0        0            50              1              0              0  \n",
       "1        1            55              0              0              1  \n",
       "2        1            51              0              0              1  \n",
       "3        1            48              1              0              0  \n",
       "4        0            47              1              0              0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df['cholesterol'],  inplace=True)\n",
    "df= one_hot\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "      age  gender  height  weight  ap_hi  ap_lo  smoke  alco  active  cardio  \\\n",
       "id                                                                             \n",
       "0   18393       2     168    62.0    110     80      0     0       1       0   \n",
       "1   20228       1     156    85.0    140     90      0     0       1       1   \n",
       "2   18857       1     165    64.0    130     70      0     0       0       1   \n",
       "3   17623       2     169    82.0    150    100      0     0       1       1   \n",
       "4   17474       1     156    56.0    100     60      0     0       0       0   \n",
       "\n",
       "    age in years  cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  gluc_2  \\\n",
       "id                                                                              \n",
       "0             50              1              0              0       1       0   \n",
       "1             55              0              0              1       1       0   \n",
       "2             51              0              0              1       1       0   \n",
       "3             48              1              0              0       1       0   \n",
       "4             47              1              0              0       1       0   \n",
       "\n",
       "    gluc_3  \n",
       "id          \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df, columns=['gluc'], prefix=['gluc'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', 'smoke', 'alco',\n",
       "       'active', 'cardio', 'age in years', 'cholesterol_1', 'cholesterol_2',\n",
       "       'cholesterol_3', 'gluc_1', 'gluc_2', 'gluc_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['cardio']\n",
    "X = df.drop(['cardio'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>active</th>\n",
       "      <th>age in years</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>gluc_1</th>\n",
       "      <th>gluc_2</th>\n",
       "      <th>gluc_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18393</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>62.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20228</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18857</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17474</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  height  weight  ap_hi  ap_lo  smoke  alco  active  \\\n",
       "id                                                                     \n",
       "0   18393       2     168    62.0    110     80      0     0       1   \n",
       "1   20228       1     156    85.0    140     90      0     0       1   \n",
       "2   18857       1     165    64.0    130     70      0     0       0   \n",
       "3   17623       2     169    82.0    150    100      0     0       1   \n",
       "4   17474       1     156    56.0    100     60      0     0       0   \n",
       "\n",
       "    age in years  cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  gluc_2  \\\n",
       "id                                                                              \n",
       "0             50              1              0              0       1       0   \n",
       "1             55              0              0              1       1       0   \n",
       "2             51              0              0              1       1       0   \n",
       "3             48              1              0              0       1       0   \n",
       "4             47              1              0              0       1       0   \n",
       "\n",
       "    gluc_3  \n",
       "id          \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', 'smoke', 'alco',\n",
       "       'active', 'cardio', 'age in years', 'cholesterol_1', 'cholesterol_2',\n",
       "       'cholesterol_3', 'gluc_1', 'gluc_2', 'gluc_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X,y, train_size = 0.7, test_size = 0.3, random_state = 17)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_tree = DecisionTreeClassifier(random_state=17, max_depth=3).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(my_tree, feature_names=df.drop('cardio',axis=1).columns, out_file = './asmnt_tree.dot', filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_pred = my_tree.predict(X_valid)\n",
    "ac_score = accuracy_score(y_valid, tree_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7212857142857143"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ac_score"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "\n",
    "tree_params = {'max_depth': range(2,11)}\n",
    "\n",
    "tree_grid = GridSearchCV(my_tree, tree_params,\n",
    "                         cv=5, n_jobs=-1, verbose=True)\n",
    "\n",
    "tree_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16a0b9f0>]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = np.arange(2,11)\n",
    "plt.plot(params, tree_grid.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 6}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_score_computed = tree_grid.score(X_valid,y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7258095238095238"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_score_computed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=6,\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=17,\n",
       "             splitter='best'), 0.7319591836734693)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.best_estimator_, tree_grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc1 = my_tree.score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 6}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseEstimator.get_params of DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\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=17,\n",
       "            splitter='best')>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tree.get_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7258095238095238"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_pred_grid_search = tree_grid.predict(X_valid)\n",
    "ac_score_grid_search = accuracy_score(y_valid, tree_pred_grid_search)\n",
    "ac_score_grid_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "standard tree accuracy score: 0.7212857142857143\n",
      "grid search tree accuracy score: 0.7258095238095238\n"
     ]
    }
   ],
   "source": [
    "print(\"standard tree accuracy score: \" + str(ac_score))\n",
    "print(\"grid search tree accuracy score: \" + str(ac_score_grid_search))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#formula\n",
    "acc2 = ac_score_grid_search\n",
    "acc1 = ac_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6271869016967002\n"
     ]
    }
   ],
   "source": [
    "result = (acc2 - acc1)/ acc1* 100\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "      <th>age in years</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>gluc_1</th>\n",
       "      <th>gluc_2</th>\n",
       "      <th>gluc_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>17623</td>\n",
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       "      <td>82.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  height  weight  ap_hi  ap_lo  smoke  alco  active  cardio  \\\n",
       "id                                                                             \n",
       "0   18393       2     168    62.0    110     80      0     0       1       0   \n",
       "1   20228       1     156    85.0    140     90      0     0       1       1   \n",
       "2   18857       1     165    64.0    130     70      0     0       0       1   \n",
       "3   17623       2     169    82.0    150    100      0     0       1       1   \n",
       "4   17474       1     156    56.0    100     60      0     0       0       0   \n",
       "\n",
       "    age in years  cholesterol_1  cholesterol_2  cholesterol_3  gluc_1  gluc_2  \\\n",
       "id                                                                              \n",
       "0             50              1              0              0       1       0   \n",
       "1             55              0              0              1       1       0   \n",
       "2             51              0              0              1       1       0   \n",
       "3             48              1              0              0       1       0   \n",
       "4             47              1              0              0       1       0   \n",
       "\n",
       "    gluc_3  \n",
       "id          \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age_40-55'] = df['age in years'].apply(lambda x: 1 if  x >=40 and x < 55 else 0)\n",
    "df['age_55-60'] = df['age in years'].apply(lambda x: 1 if  x >=55 and x < 60 else 0)\n",
    "df['age_60-65'] = df['age in years'].apply(lambda x: 1 if  x >=60 and x < 65 else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ap_hi_120-140'] = df['ap_hi'].apply(lambda x: 1 if  x >=120 and x < 140 else 0)\n",
    "df['ap_hi_140-160'] = df['ap_hi'].apply(lambda x: 1 if  x >=140 and x < 160 else 0)\n",
    "df['ap_hi_160-180'] = df['ap_hi'].apply(lambda x: 1 if  x >=160 and x < 180 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['male'] = df['gender'].apply(lambda x : 1 if x ==  2 else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  height  weight  ap_hi  ap_lo  smoke  alco  active  cardio  \\\n",
       "id                                                                             \n",
       "0   18393       2     168    62.0    110     80      0     0       1       0   \n",
       "1   20228       1     156    85.0    140     90      0     0       1       1   \n",
       "2   18857       1     165    64.0    130     70      0     0       0       1   \n",
       "3   17623       2     169    82.0    150    100      0     0       1       1   \n",
       "4   17474       1     156    56.0    100     60      0     0       0       0   \n",
       "\n",
       "    ...  gluc_1  gluc_2  gluc_3  age_40-55  age_55-60  age_60-65  \\\n",
       "id  ...                                                            \n",
       "0   ...       1       0       0          1          0          0   \n",
       "1   ...       1       0       0          0          1          0   \n",
       "2   ...       1       0       0          1          0          0   \n",
       "3   ...       1       0       0          1          0          0   \n",
       "4   ...       1       0       0          1          0          0   \n",
       "\n",
       "    ap_hi_120-140  ap_hi_140-160  ap_hi_160-180  male  \n",
       "id                                                     \n",
       "0               0              0              0     1  \n",
       "1               0              1              0     0  \n",
       "2               1              0              0     0  \n",
       "3               0              1              0     1  \n",
       "4               0              0              0     0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', 'smoke', 'alco',\n",
       "       'active', 'cardio', 'age in years', 'cholesterol_1', 'cholesterol_2',\n",
       "       'cholesterol_3', 'gluc_1', 'gluc_2', 'gluc_3', 'age_40-55', 'age_55-60',\n",
       "       'age_60-65', 'ap_hi_120-140', 'ap_hi_140-160', 'ap_hi_160-180', 'male'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smoke</th>\n",
       "      <th>cardio</th>\n",
       "      <th>cholesterol_1</th>\n",
       "      <th>cholesterol_2</th>\n",
       "      <th>cholesterol_3</th>\n",
       "      <th>age_40-55</th>\n",
       "      <th>age_55-60</th>\n",
       "      <th>age_60-65</th>\n",
       "      <th>ap_hi_120-140</th>\n",
       "      <th>ap_hi_140-160</th>\n",
       "      <th>ap_hi_160-180</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    smoke  cardio  cholesterol_1  cholesterol_2  cholesterol_3  age_40-55  \\\n",
       "id                                                                          \n",
       "0       0       0              1              0              0          1   \n",
       "1       0       1              0              0              1          0   \n",
       "2       0       1              0              0              1          1   \n",
       "3       0       1              1              0              0          1   \n",
       "4       0       0              1              0              0          1   \n",
       "\n",
       "    age_55-60  age_60-65  ap_hi_120-140  ap_hi_140-160  ap_hi_160-180  male  \n",
       "id                                                                           \n",
       "0           0          0              0              0              0     1  \n",
       "1           1          0              0              1              0     0  \n",
       "2           0          0              1              0              0     0  \n",
       "3           0          0              0              1              0     1  \n",
       "4           0          0              0              0              0     0  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.drop(columns=['age', 'gender', 'height', 'weight', 'ap_hi','ap_lo', 'alco', 'active', 'age in years', 'gluc_1', 'gluc_2', 'gluc_3'])\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['smoke', 'cardio', 'cholesterol_1', 'cholesterol_2', 'cholesterol_3',\n",
       "       'age_40-55', 'age_55-60', 'age_60-65', 'ap_hi_120-140', 'ap_hi_140-160',\n",
       "       'ap_hi_160-180', 'male'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_tree = DecisionTreeClassifier(max_depth=3, random_state=17)\n",
    "\n",
    "new_tree.fit(new_df.drop('cardio', axis=1), new_df['cardio'])\n",
    "export_graphviz(new_tree, feature_names=new_df.drop('cardio', axis=1).columns,out_file='./asmnt_tree_final.dot', filled=True)\n",
    "\n",
    "#export_graphviz(my_tree, feature_names=df.drop('cardio',axis=1).columns, out_file = './asmnt_tree.dot', filled=True)\n",
    "\n",
    "# import graphviz\n",
    "# graph = graphviz.Source('asmnt_tree_final.dot')  \n",
    "# graph "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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