ML 4.1 - Decision Tree - example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Internet Resources:\n",
    "\n",
    "[handson-ml/06_decision_trees.ipynb](https://github.com/ageron/handson-ml/blob/master/06_decision_trees.ipynb)  \n",
    "\n",
    "As the name implies, this model will create a decision tree that best fits to the training data. Here's an example of a descision tree:\n",
    "\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/e/eb/Decision_Tree.jpg)\n",
    "\n",
    "Descision trees are easy to train and understand but are prone to overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width  species\n",
       "0             1.4          0.2        0\n",
       "1             1.4          0.2        0\n",
       "2             1.3          0.2        0\n",
       "3             1.5          0.2        0\n",
       "4             1.4          0.2        0\n",
       "..            ...          ...      ...\n",
       "145           5.2          2.3        2\n",
       "146           5.0          1.9        2\n",
       "147           5.2          2.0        2\n",
       "148           5.4          2.3        2\n",
       "149           5.1          1.8        2\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# data set is the iris dataset from https://www.kaggle.com/arshid/iris-flower-dataset\n",
    "# we are only keeping features \"petal_length\" and \"petal_width\"\n",
    "df = pd.read_csv(\"data/iris.csv\").drop([\"sepal_width\", \"sepal_length\"], 1) \n",
    "\n",
    "# convert labels to numbers\n",
    "label_mappings = {label_str:i for i,label_str in enumerate(df[\"species\"].unique())}\n",
    "df.replace({\"species\":label_mappings}, inplace=True)\n",
    "\n",
    "X = np.array(df.drop([\"species\"], 1))\n",
    "y = np.array(df[\"species\"])\n",
    "\n",
    "# max_depth = maximum depth of the tree. this prevents to much overfittin\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42) \n",
    "tree_clf.fit(X, y)\n",
    "print(tree_clf.predict([[3,2]]))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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n4CMPBqCoqIh3XnuHVz9+lamfT2XeJ/N48PYHS/o8t8+57LPrPnFfz094Pu0/l3SKV5J14TMXxmwfbxSfyaVdmRx7pqpwNTkzO6DUx33N7H+lPucBvYBvUhGYiASjSbMmAHy/6nteeOIFPlj8AQ3zGwJw2TWX8cCYB/j4w4/pdlg3AHqe2JPDjz0cgJP7n8wj9z7CC4+/wEXD4z22ImLYqGHUqVuHOnXr0OvEXsyZ8eu9/4cmPZSCM6teXpv0GgDH9jl2q+/ilWRtKopdKbz0p6Uxt2dyaVcmx56ptrVc7AzAo6//xvh+A3Bx0EGJSHBWLF8BgG1nAPTar1eZ7zdt2sSKr1eUfN6j5R5lvt+jxR4lfcSTl5fHbqWeJFm7Tm3Wr6/cJfpM0KVpU75blQf5n8B5R8IDb8PqgUCkxLJ82dsrgyueA5Fo6di2+oknXhleMiVryZa3lW9f2dhVVld527pE3wpoAxjQLfq5+LU7UN/dH0hphCJSJS899RKNd29M67atAXjri7eY+7+5Ja8F6xdw4pknlrT/eknZZ1p9vfTrkqsAxb8kJOvs3meX3LeP9XrusecqeXbh+G5VXuRN3wFQcw2cchZwfPRV/cre4sWTzHGTjVGrvYWvwgQfLW1b4u7bufuM6Ofi1wp3L0pXoCKSnG+//pZ/j/g3T49/mhH/GUHD/IacdOZJXPnnK1n5zUoA1vy0hleee4Wf1/9cst9/X/gv7055l6KiIl54/AXmfDSHPv36ANCocSN+WP0D69Ym99Tohyc/XHLfPtbr5P4nl7TdtGkTBQUFFG0uwrc4BQUFFBZWwyrZ/E+g0bzI8KfRPGh0IzCp2pW9xYsnmeMmG6NWe6seKnrQzdmJvtIZsIjEd9sNt9Fhpw503Lkjpx15Gku/XMpz7z5H71N6A3Dz2Jtp064Npx91Oh126kDPzj15+ZmXiTxFOqLfH/px/3/uZ+9d9mbMDWO495l7adG6BQCHHHUIPY7pwaFtDmWfXfdh2lvTAj+HoYOH0rZOW26/6Xbef/N92tZpy286/Cbw41RZ3wFlP58SKeurbmVv8eJJ5rjJxqjV3qqHuI+qNbPyv6LvAGwPFD+qdjtgE1Do7vWpBvSoWqksPao24vSjTuewow9jyJWJXVrOVc2b/gB/2j8yei/mwFNPQr9+W7V/ZfArMR9Bu2rdKg674zAKN/96haJWjVq8e/G7gdxvnrdyHsfdd9xW2x/r/xh/ePIPCR032RiDOqdU/2yySdKPqnX3esUv4AxgDpGSuFr8Wh73CZBZT6MQScD3/n3YIUh1Vn70DnAd8NnWyR3CK3srP3ovduEzFyZ83GRj1Gpv1Ueii818BvzB3T8ot/1g4CF3b5ei+JKiEbxUlvuPANSoUfFCH1fefSWnDDoFgGfve5YbL7gxbttZm2eVvD+r21l8PuvzmO1O/uPJXH3P1QDMnzmfAQfFSB5Rj374KB27RGIc+aeRPHd/7Mlp7Q9oz4TpE0o+H1DjgJjtoOw5ndThJJYtjH/1IhPPKRV/TgcM7g4PbIzZDoDBQNPo+xeBWbGb1dyjJoUDS80vGBG/y8qc08H/OJjCOwsjS3bFcgBwQvT9t8DY+McP+5yK+2mX3w67z3L2716cc4o5gt9WmVyxlsDPMbb/AjRPsA+RastsF7Zs2faKyD+vrsv/FjYoeV+R4nYARQXx/6kVrqlV0nbt11s9MLKMtV/vzP/qNyjZL56ighpljl+R0ufUd8AF3Hrt8LhtM/GcUvHndFfviVz4wPFx297V8yXa7r0vAKPnDWXyrNjPqG9eby/u7je55PMxI+L/77Qy5/TyKQu5YEJvFq6YG7Nd7zZncnm/mwFYMG8OF46tvuf0er9ff/G84Pbecdtl+9+9bZ1TaYmO4KdG3/Z392+i23YHHon2US1mwGgEL1Xh/mPJCH7x4m3/u5DMtfqXFVz89hncccSTNKzdOOXHO/BA+D7GXZ8GDeCjj1J+eMlyrVpZcvfgyxkI7AYsMbMlZrYEWAI0AgYFFaSISDrcPmckH616l9tmj0zL8WIl94q2iwQhoQTv7l8B+wK/A0YD/wF6A53c/cvUhSciEqzVv6zg6S8fxNnCM18+yHcbVqb0eAcd1JRfb16LpE+iI3g84r/ufpu7j3H31zyR6/siItXI7XNGssUj1b5FXpTyUfzq1SuIP8tNJHXi3tU3s8uBu9y9IPo+LncfHXhkIiIBKx69b9oSmQG/actGnvnyQYZ0vjpl9+KnTfuG7t1T0rVIhSqaRX8xMB4ooOIFZZzIZXsRkWqt9Oi9WPEofmT3O1NyzPx8XZ6XcMRN8O7eKtZ7EZFMNWv1ByWj92Kbtmxk1ur3U3rcBg3iz6IXSZWE6uDNLE8Ly4hIpls98mOIkWhXN+DXB76UEkR52/DhgznmGBg1auunyBx4ILSKMXyqjuVz6S4tlKpLdJLdGjN71cyGm9nBZpaX0qhERFIg2XK1IMrbnnjiPp544r6U9Z8u6S4tlKpL9El2JwNHECmTGwFsNLP3ganA1PKPsBURkYgbb7w37BCqrHxpYSonJUpwEkrw7v4a8BqAmdUGDgX6A9cTuQqgEb2ISAxnnTU47BCqLFZpYaomJUpwEq6DN7N8M+tHZMb8nURWmHuPSJIXEZEsFK+0MNUPCJKqSyjBm9k8YBHwJ2AlcD6ws7sf6e7XpTA+EZGM9vrrk3j99Ulhh1FpFZUWSvWW6D34nYAiIqvH/QysAypYK1FEpPpJtlwtiPK2QYMi0/NjLWCUCeVzYZUWStUleg++mZntCRwZfV0C1DWzd4A33f0/ifRjZhcB5wKdgMfd/dw47c4FxgEbSm0+3t2nJnIcEckuQa3GVtGs9VjlamaJ9w3QujVs/QDvX5dgLV9qVhx7dS5Be/mEj8MOQSopmWfRf+nu9wPnAKcDzwPHAbckcbxvgRuABxJo+4G71y31mprEcUQki4RVThZvtY14x43dflL0Fb/UTCVokgqJ3oM/0Mz+Zmb/B/xIpDyuA/BvIqvKJcTdJ7r788APlYhVRCRjxVvFLt2r20nuSHQE/x6RWvjZREbvu7p7d3cf5u6vpii2/c3sezNbYGZXm1nM2wlmNtjMZpjZjO++0+8NIlI9xVvFLt2r20nuSDTB7+LuB0cT+ivu/nNKo4K3gX2ARsApwJnAFbEauvtYd+/q7l0bNtwtxWGJiCTLAItZavbZ/2arBE1SJqEEn4aEXv54i9x9sbtvcfdPidTan5rOGEREghSr1OySt/urBE1SJuFJdiFzIr8Gi0gOqqiMLZXizaKPd9zY7R3O3y9mqdmy9V+pBE1SJtE6+EBE76PXIPJo2zwzqwVsdvfN5dodB8xy91Vm1h64Gng6nbGKSNUFVd6W7MpqscvVIgl40aKtt8eLs6JZ9MmtAlc9S82qc3meVF26R/BXEaltHwYMiL6/ysyam9l6M2sebXc0MMfMfgYmAxOBm9Icq4hUUXUrb0u27C1Z1XEVuIqoPC+7pTXBu/sId7dyrxHuvixa674s2u6v7p7v7nXcvbW7X+Pum9IZq4hIEAYO7MPAgX3CDmMrKs/LfnEv0ZvZ5Yl24u6jgwlHRCS7vPHGS2GHEJNWiMt+Fd2DvzjBPpzICnMiIlLOffe9GHYIW4m3QpzWec8ucRO8u8eYQiIiIsk45pjqd3m+ohXiNIrPHplSJiciGai6lbclW/aWrOq0ClxFtEJcbki4TM7MdgV+CzQHdij9nbtfH3BcIpIFki1vC0qsUriKpDLOCRPGAnDWWYNTd5AkaYW43JBQgjez7sDLQCHQEPgGaBL9vITIk+ZEJEsEVb8eT6wa8nQwi18fH2t7EOd75ZXnA9UrwUtuSHQE/y/gMSLrwK8FjgJ+Bh4nsm67iGSRsOrXUy2M+vgzzhhU9U5EKiHRBL8vMNDd3cyKgJruvsjMhgITiCR/EREpZ9SosWGHIDkq0Ul2pWdjrAJaRN+vB5oGGpGIiIhUWaIj+FnAgcACYCpwg5nlE3nc7JzUhCYikvlWrfoWgPx8jYUkvRIdwV8JfBt9fxXwHXA7sAtwfgriEhHJCt2770737ruHHYbkoIRG8O4+o9T774DjUhaRiISuQYP4s+gzWWVm0VdVo0ZNqt6JSCUkWib3BtDX3X8qt70+8Ly7H5WK4EQkHEHVhccrt0u2LC3Zsr147XfbLblzO/DAZJeF3dqHH3677UYiKZDoJfojKfdwm6haQI/AohGRrBKvzCzZsrRUb48nW8sFJTdUOII3swNKfdzXzP5X6nMe0IvIQ29ERESkGtnWJfoZRFaLc+C/Mb7fQOKrzomI5Jw+fboAMGnSzJAjkVyzrQTfCjBgEdCNyOz5YhuB1e5elKLYREQy3ty5s8IOQXJUhQne3ZdG32rVORGRSnjxxRnbbiSSAgknbjM7zsxeMrP5ZrZHdNsfzezo1IUnYbvuulvIy2tCXl4TatRoym67teegg37LVVeNYuXK1Sk55oIFX3Hddbfw009rymx/6KEnyctrwvr1Pwd+THdn1Kh7Sz6ffvrhzJ//SeDHyTXxysySXbY11dvjCaKfTp260KlTl+QOLBKARMvk+gP3APcDRwPbR7/KA/4GTElJdFIt7LRTfSZPngDAmjVr+fjjT7nnnvHcd9+jTJ48gS5dOgd6vAULFnH99f/mnHP6sfPOOwXadzw333w7N954T8nnOnXqMmDAMbz66lwaNmyclhhSvYJbKlVUDhdLsuVqyZ5/UD+v6v5zF6lIoiP4vwGD3P0yYHOp7dOA/QKPSqqVGjXy6N69C927d6FXr98wbNgQPvnkDZo0yefMM/9EUVFmT8MoKCjg5pvvYOjQX1f9uvPOpzEzxo+/I21xZHJJVlDlcNno1ltHcOutI8IOQ3JQogl+L+CDGNvXA/WDC0cyxc4778Q//nEVX321hNdeewuIJMqhQ0fSokUXatduwf77H83kyWUv7rRufSBXXHEdN9wwmqZN96V+/TYMGHAha9asBWDq1Pc58cSzAWjTpht5eU1o3frAMn0sXryMnj37Ua9eazp2PIyJE1+u0rm8//4M1q5dx2mn/bZk24471uHoo/vw1lv/V6W+RcaMuY4xY64LOwzJQYkm+G+BtjG2Hw58FVw4kkl+85tDqVGjBh9+GJklfNppgxg//kmGDRvCCy+Mp2vX/TjppHP45JO5ZfZ74onnmTLlHe699xZuuWUEkydPYdCgvwBwwAGd+Ne/rgXgmWfG8d57L/Hssw+U2X/AgAs54YSePPvsA+y5Z2vOOusCli//9WlhW7ZsYfPmzRW+Sl91+OKLL8nLy2OvvVqUOU6bNh346qvPg/uBSU665JJrueSSa8MOQ3JQoqvJjQVuM7M/Rj/vYWY9gH8CI1IRmFR/NWvWpEGDXVm16jumTHmHyZNf5403nuWIIw4BoGfPI1m48CtuumkMTz11X8l+GzYUMGnSo9StWweAOnV25JxzLuazzxbQoUNb2rZtA8D++3eiZcs9tjruJZcM5g9/OBOALl32pUmTfXnppdf405/OAWDkyNFcf/2/K4y9RYtmLFoUucH6448/UbduHfLy8sq02WmnXdiw4Rc2btzIDjvEepCjyLZdeumIsEOQHJXoYjP/NLOdgNeIPJ72TaAQuMXd70xhfFLNefQm65Qpb9O4cSMOPbQbmzf/Ok3jqKN6MH78k2X2OeaYw0uSO0Dfvr05++yL+OijT+jQIdaForJ69jyi5P1uu+1Ko0YN+OabFSXbBg0awO9+d0yFfdSsWbPMZ4sxG6z43GJ9JyJS3SU6gsfdrzSzG4GORC7tz3f39SmLTKq9goICfvjhR/LzG/LNNytYuXI1NWtuPeIuPzJu1KhsjVHt2rWpW7dOwmV35WfW77DD9hQUFJZ8bty40VbHKK900t5ll51Zt279VpMF1679idq1d2T77bcvv3tKZPIKbvFiT+UqbZni008jT7BTqZyk27aeRb8j8C/gJCKlca8DQ9y9UnNgzewi4FygE/C4u59bQdvLgKFAbeBZ4AJ3L4zXXtLvzTffY/PmzXTv3oU333yP3XdvwsSJD2xzv9Wry/712bBhA+vX/0zjxo0CiSvZS/Tt2u1JUVERX365rEybRfeM8FYAABi0SURBVIs+p02b9oHElIigSrJat46/JOqiRVVrX1Ep3+LFiccYb5W2ZFeZywQnnNAVgMWL45QUiKTItkbw1xFJyI8BBcCZwN3AaZU83rfADUQWqakdr5GZ9QKGAUdF93kuGsuwSh5XAvbTT2sYPvxG9tyzFcccczhmxujR91C3bh3at9+rwn1ff/1t1q//ueQy/cSJkzEzunaN1NPvsENkxFxQUFCp2JK9RH/IIV2pX78ezzzzasm2DRt+YcqUSZxxxuBKxRCmeKVpQWxP9Spt2VhWt88+B2y7kUgKbCvB9wUGuvsTAGb2KPCemeVV5hn07j4x2k9XoFkFTc8Bxrn7vGj7kUR+yVCCD8HmzUVMmxa5zLhu3XpmzZrDPfeM55dfNjB58gTy8vI49tgj6NnzSHr1OoO//e3PdOzYjrVr1zF79jwKCgq46aYrS/qrXbsWffoM4C9/uZAVK1YxdOhITjrpODp2bAdAu3aRSXZjxz5Cv34nseOOtenUqUPC8TZt2pimTRN/OE2tWrUYOvQibrjhPyXb/vzn09iyZQvnnKO1lKRqtMiMhGVbCX4P4J3iD+4+3cw2A02Br1MY197AC6U+zwbyzWw3d/+hdEMzGwwMBmjefPcUhpS71qxZy6GHHo+ZUb9+PfbcsyX9+5/CRRcNLLmsbmY8++w4Ro26jTFj7mPZsm/Ydded6dx5by66aGCZ/vr1O5F69eoyaNDlrF//M3369OKuu/5R8n2LFnvwr39dy+23388ddzxAs2ZNSi6np8rQoRdTVPQz11xzGwDr1q3lkUdeo2HD/JQeV0QkVczjXRMDzKwIaOzu35Xatg7Y192TuOO2Vb83AM3i3YM3s6+AP7v7K9HP2xNZva6Vuy+J12/Xrp19+vRX430t1UDr1gdyyinHl9S6VyfuP1KjRkcgs++Xxrq3XSzWffJk2ifbdzwV9RNE/yK5pFUrm+nuXctv39YI3oBHzaz05LZawH1m9kvxBnc/IZgwS5R/Ql7x+3UBH0dEJKUOOqgpAB9++O02WooEa1sJfnyMbY+mIpBy5gGdgaeinzsDq8pfnheRrcWbiR6vnD+Z9kGV8uVSWd3q1Su23UgkBba1Hvx5QR7MzGpEj5kH5JlZLWCzu28u1/Rh4CEzewxYAVwFPBRkLBKOVN9Ll9ilcEG11yptyZs27ZuwQ5AclfB68AG5CthAZDb8gOj7q8ysuZmtN7PmANF77/8k8sS8pdFX9btpKyKyDfn5TcnPbxp2GJKDEn6SXRDcfQTxn11ft1zb0cDoFIckIiKSldI9ghcRySnDhw9m+PDMe2CSZD4leBGRFHriift44on7tt1QJGBpvUQvIpJrbrzx3rBDkBylBC8ikkJnnaXL8xIOXaIXERHJQkrwIiIp9Prrk3j99UlhhyE5SJfoRURSaNCgyJO8M3l9A8lMSvAiIil01FHHhx2C5CgleBGRFBo3TpfnJRy6By8iIpKFlOBFRESykBK8iEgKtWpltGoVZ61ekRRSghcREclCmmQnIpJCKo+TsGgELyIikoWU4EVERLKQEryISAoNHNiHgQP7hB2G5CDdgxcRSaE33ngp7BAkRynBi4ik0H33vRh2CJKjlOClSlatmsiSJaMoLPyGmjV3p2XL4eTn9w07LJFq45hjdHlewqEEL5W2atVEFi78K1u2bACgsHA5Cxf+FUBJXkQkZJpkJ5W2ZMmokuRebMuWDSxZMiqkiESqnwkTxjJhwtiww5AcpAQvlVZY+E1S20US9ac/9WX27I8AuPXWEXTt2ojf/W5/fvObtpx44oE8+OBtFBUVVekYy5cv2SrxHnZYS774Ym6l+7zttpH07Lk3xx3XmT59uvDWW69y5ZXnc+WV5zNq1N948cXHqxSzSDJ0iV4qrWbN3SksXB5zu0hlffzxh/zyy8907nxgybaTTz6bK6+8BYBlyxZx2WUDWLr0S0aMuK3Sx1m+fAlPPDGWs84aXOWYi3Xu3I1Bg/5C7do7Mn/+bM444whOPfU8atSoweDBV3DaaYdx/PH92G47ja0k9fS3TCqtZcvhbLdd7TLbttuuNi1bDg8pIskGTzwxlhNPPCvu982bt+af/3yAxx67m7Vr1wDw5puTOfXUQ+nTpwt9+x7Mxx9PA2DatKkcd1xnrrjiPI4//gBOPLEbCxfOB+Caa/7MwoXz6d17Py644NSS/l9++Sn69j2Yww5ryfjxdyQV+xFH9KJ27R0B6NBhX8C5/PLrGTVqLLvt1pDmzVvz3ntTkupTpLI0gpdKK55Ip1n0EqRp06YyePAVFbZp06Y9tWvvyKJFX7DLLrtx++0jGT/+VerVq8+CBfM477zjeO+9ZQB8/vkcrr32Nrp3P4Jnnx3PX/5yNi++OIPrr7+Tm276Ky++OKNM3xs2/MLEiR+wfPkSevXah1NPPZc6deoyYsQQpk9/O2Y8d9/9LC1atCmzbeLEh2nevA1NmjQr2XbAAQfz/vtT6NHj2Mr8aESSktYEb2a7AuOAnsD3wHB3nxCj3bnRdqVncB3v7lPTEKYkIT+/rxK6BGrlyuU0aJCfcPu3336VZcu+ol+/w0u2bd68me++WwVAy5Z70r37EQCcfPLv+fvfB7Nu3dq4/fXpcwYAzZq1ZKeddmHlyuW0adM+qdsB06a9xejRV/Pww6+xatW3AOTnN6VBg8Zxf0kQCVq6R/B3AhuBfGA/4GUzm+3u82K0/cDdD0trdBIY1cdLZdWsWZvCwoIK23z11Rds2PALbdq0Z86cjzj88N8yevTDMdp9Vonj1yp5v912eWzevBkg4RH8rFkfcPnlAxg79gXatGlXshb84sVOYWEBtWrVjtmHSNDSluDNrA5wCrCPu68H3jWzF4HfA8PSFYeknurjpSratevEokVf0KhRk5jfL1++hGHDBtK//wXUq1efHj16cttt17FgwTzatt0bgNmzPyqZpLdkyZdMn/4O3br14IUXJtCuXSfq1atP3br1WbduTcJxJTKCnz37Iy6+uB933vkM++xzAECZ8/jqq8/o0KFzwscUqYp0juDbAkXuvqDUttnAEXHa729m3wP/Ax4BRrn75hTHKAGoqD5eCV625be/7cvbb79K9+5Hlmx77rmHef/9KWzY8Av16tXnxBP7c845FwPQqtVejB79KEOHDqSgYAObNm2kS5dDSxJ8x477MWnS44wceSnbbZfHv/8dGem3b78vrVu3o1evfWjduj133/1MlWO/5poLKSjYwJVXnl+ybfz4V2nfvhPuznvvTeHCC/9e5eOIJMLcPT0HMusBPO3ujUttGwT0d/cjy7VtDTiwFNgbeBJ4xN23eoKKmQ0GBgM0b757l8WLZ5RvImn29ttNifzxlWccfvi36Q4nYe4/UqNGRyByOVXCsW7dWk477TCef/7DKl/OnjZtasyJdGF4661Xef75R/nPfx4JOxTJMq1a2Ux371p+ezrL5NYD9cttqw+sK9/Q3Re5+2J33+LunwLXA6eWbxdtO9bdu7p714YNdws8aElevDp41cdLIurVq8+VV/6br79eHHYogVq/fi3Dht0cdhiSQ9J5iX4BUMPM9nL3hdFtnYFYE+zKc8BSFpkEqmXL4WXuwYPq4yU5QZWRde9+ZOij9z59ugAwadLMUOOQ3JO2BO/uP5vZROB6M/sjkVn0JwKHlG9rZscBs9x9lZm1B64Gnk5XrFI1qo8X+dXcubPCDkFyVLrL5C4EHgBWAz8AF7j7PDNrDswHOrr7MuBo4CEzqwusAh4FbkpzrFkvmVK2Tz45nbVr3yn5XL9+D/bb76nAyuFUVifZKuwrCJK70jbJLh26du3s06e/GnYYGaF8KRtELqPvtdctWyXW8sm9WK1a7di4cdlWfTRqdDqrVz+VUN/JxpJKmmQnIpmoOkyyk2okmaVeYyV3gIKCL2L2sXLlo0ktI6tlZ0VEgqcEn6NSu9Rr7GU8kz2mlp2VbHDrrSO49dYRYYchOUgJPkeltpQtL5BjqqxOssGYMdcxZsx1YYchOUgJPkcls9Rr/fo9YvZRq1a7mH00bjwgqWVkteysZLNLLrmWSy65NuwwJAdpudgclUwp2377PZX0LPqdduqW8Kx4ldVJNrv00hFhhyA5Sgk+h61ZM53CwhWAU1i4gjVrppOf3zdmMm/S5AwKCxeXJOAmTSJLaga1XKyWnRURCZYSfI5asGAYK1eOL7WliJUrx/P996+yefPKMm3Xrn2HtWvfpfj58ttaHU6ryYn86tNPI0+w69SpS8iRSK7RPfgctXLlozG3l0/uvypbF66yN5HEnHBCV044YasSZZGU0wg+Z8UuZUuGyt5Etq14XXiRdFOCz1l5VDXJV1TeVli4POH2ItlMi8xIWHSJPkc1bjwg5vYaNRrH2aPsYn4qexMRqd6U4HNU27b/oHHjc/j1oTR5NG58Docc8vFWde/16/egXbs7qFmzGWDUrNmswufE5+f3Za+9bkm4vYiIBE+X6DNQsiuvRWbMP0rkknwejRsPoG3bf/DTT9P49TJ9UfQzrF37QZn91679gHXrZuO+FojMil+wIHLM99/fv8zEvBo1GnPIIR+n/JxEMsVBBzUF4MMPvw05Esk1Wk0uwyS78trW5XARZvVLEnbQzOpjtkmryYkArVpFbm/p75SkilaTyxLJlqDFK4dLVXIv7luryYlETJv2DdOmqYJE0k+X6DNM8iVoVS+HC4rK6iQX5ec3DTsEyVEawWeY5Fdei72yWxi0mpyISPoowWeYZEvQ4pXDmdUPPLbSfWs1OZGI4cMHM3z44LDDkBykBJ9hki1Bi1cO16PHF9Sq1a5M21q12nH44SvY+s5Nja1+ITCrz+GHr9iqbr5Gjcb06PFFUjGqrE6y2RNP3McTT9wXdhiSgzSLXiRKs+glFSZMGAvAWWdpFC+pEW8WvSbZZZGgaslj9bN06W0UFHxR0qZWrXZ06zY1wOhFspMSu4RFl+izRHEteeQZ8F6yROuqVROr3M8XX/y5THIHKCj4gunTjwwsfhERCZYSfJYIqpY8Vj/xlE/6IrK111+fxOuvTwo7DMlBukSfJYKqJVftuUiwBg06AdC8Dkk/JfgsEdQSrfH6EZHKOeqo48MOQXKULtFniaBqyWP1E0/5MjsR2dq4cZMYN06X6CX9lOCzRFC15LH6adfuzpg185pFLyJSfaX1Er2Z7QqMA3oC3wPD3X1CnLaXAUOB2sCzwAXuXpiuWDNRfn7fQB4OE6sfPXRGRCSzpHsEfyewEcgH+gN3m9ne5RuZWS9gGHA00BJoDVyXvjBFRILRqpWVLBkrkk5pS/BmVgc4Bbja3de7+7vAi8DvYzQ/Bxjn7vPc/UdgJHBuumIVERHJdOm8RN8WKHL3BaW2zQaOiNF2b+CFcu3yzWw3d/+hdEMzGwwUPyqqMC+vydwAY67uGhC51ZEL0nqu1WDEpT/bLFPq71ROnG9ULp0rhHe+LWJtTGeCrwusKbdtDVAvgbbF7+sBZRK8u48FxgKY2YxYz+PNVrl0vrl0rpBb55tL5wq5db65dK5Q/c43nffg1wPl1yitD6xLoG3x+1htRUREpJx0JvgFQA0z26vUts7AvBht50W/K91uVfnL8yIiIhJb2hK8u/8MTASuN7M6ZnYocCLwSIzmDwMDzayjme0CXAU8lMBhxgYVb4bIpfPNpXOF3DrfXDpXyK3zzaVzhWp2vmldDz5aB/8AcCyRe+nD3H2CmTUH5gMd3X1ZtO3llK2D/5Pq4EVERBKT1gQvIiIi6aFH1YqIiGQhJXgREZEslBUJ3sx2NbPnzOxnM1tqZmeFHVOqmNlFZjbDzArN7KGw40klM6tpZuOif6brzOxjMzsu7LhSycweNbMVZrbWzBaY2R/DjinVzGwvMysws0fDjiWVzGxq9DzXR19fhB1TKpnZGWb2WfT/y1+ZWY+wY0qFUn+exa8iM7s97Lgge9aDL/2M+/2Al81strvHKsHLdN8CNwC9iExAzGY1gK+JPO1wGdAbeMrMOrn7kjADS6FRwEB3LzSz9sBUM/vY3WeGHVgK3Ql8FHYQaXKRu98fdhCpZmbHAjcD/YDpQJNwI0odd69b/D76SPZVwNPhRfSrjB/BJ/mM+4zn7hPd/XnKPdEvG7n7z+4+wt2XuPsWd38JWAx0CTu2VImuv1BcLeLRV5sQQ0opMzsD+AmYEnYsEqjrgOvdfVr03+437v5N2EGlwanAauCdsAOBLEjwxH/G/Var1ElmM7N8In/e2XhlpoSZ3WVmvwCfAyuAySGHlBJmVh+4HvhL2LGk0Sgz+97M3jOzI8MOJhXMLA/oCjQ0sy/NbLmZ3WFm2X7FESILpT3s1aQ8LRsSfDLPuJcMZWbbA48B493987DjSSV3v5DI398eRB4Ola3PfxhJZNXIr8MOJE2GEln6enciD0SZZGbZeHUmH9ieyGi2B5HbpvsTeWBZ1oo+z+UIYHzYsRTLhgSfzDPuJQOZ2XZEnni4Ebgo5HDSwt2LorebmgEXhB1P0MxsP+AY4D9hx5Iu7v6hu69z90J3Hw+8R2ReSbbZEP3v7e6+wt2/B0aTneda2tnAu+6+OOxAimXDJLuSZ9y7+8LotnjPuJcMY2YGjCMyKujt7ptCDindapCd9+CPBFoCyyJ/xNQF8syso7sfEGJc6eRA6OsSB83dfzSz5UTOL5ecDfwj7CBKy/gRfJLPuM94ZlbDzGoBeUT+h1jLzLLhF7V47gY6AH3cfcO2GmcyM2sULS2qa2Z5ZtYLOBN4I+zYUmAskV9c9ou+7gFeJlIdknXMbGcz61X879XM+gOHA6+GHVuKPAhcHP07vQtwKfBSyDGljJkdQuTWS7WYPV8sWxLDhUSecb+ayOzyC7K0RA4i97GuLfV5AJEZqyNCiSaFzKwFcD6Re9AroyM9gPPd/bHQAksdJ3I5/h4iv3wvBS519xdCjSoF3P0X4Jfiz2a2Hihw9+/CiyqltidS3toeKCIygfIkd8/WWviRQAMiV1gLgKeAG0ONKLXOASa6e7W6Naxn0YuIiGShjL9ELyIiIltTghcREclCSvAiIiJZSAleREQkCynBi4iIZCEleBERkSykBC8icZnZEjP7awXfnxutYa8WzOwhM8vaB6qIJEMJXqSaiyYtj742mdkiM7slulRyIvu3jO7bNdWxpks2npNI0LLlSXYi2e514PdEnojWA7gfqEMWLkQjIsHQCF4kMxS6+0p3/9rdJxBZOvckiCzIY2Z/M7OvzGyDmX1qZgNK7Vu8utVH0VHv1Oh+B5rZf6Prk681s3fN7OCqBmpmfcxsppkVmNliM7vRzHYo9f0SM7vKzO6NHne5mV1Rro+2ZvZWtI8vzKy3ma03s3MrOqdS+19iZt+Y2Y9m9qCZ7VjV8xLJNErwIplpA5HRPESecT4Q+DPQERgF3Gtmv4t+3y36398CTYC+0c/1iCzK1CPa5hNgspk1qGxQ0QVyHgPuAPYG/kBkXfCbyjW9DPgUOAC4Gfhn8S8X0eWBnwM2A92Bc4msv1Cz1P7xzono+exDZDnafsDJwCWVPSeRTKVL9CIZxsy6AWcBU6L34S8Herr7O9Emi6Nt/kxkhbbiBVx+cPeVxf24e5lV6szsYuAUIknz0UqGdyXwL3d/MPr5KzMbCjxqZlf4r4tf/Nfd74i+v93MhgBHAx8AxwLtouf0TTS2y4isn14s5jlFrSWy4NRm4DMzezra96hKnpNIRlKCF8kMv43OVq9BZOT+AnAxkRF7LeAVMyu9ctT2wJKKOjSzRkRW/foNkE9kCeLaQPMqxNkF6BZN6sW2i/bbGFgR3Tan3H7fAo2i79sD3xYn96iPgC0JxjA/mtxL931QgvuKZA0leJHM8DYwGNhEJPltAjCzVtHv+wDLyu2zaRt9jieS2C8j8stAITAF2KGCfbZlOyLLF8daF7v0UrDlY3N+vWVo0c+VVVHfIjlDCV4kM/zi7l/G2D6fSGJuUf6Seykbo//NK7f9MGCIu78MYGb5RO5nV8UsoH2cWBP1GbC7mTV192+j27pSNknHOycRiVKCF8lg7r7OzG4BbjEzIzLSr0tkctoWdx8LrCYyKa+XmS0BCtx9DbAAGGBmHxIpufsnvybOyroeeMnMlgJPEZkotw/Qzd3/lmAfrwFfAOOjD9mpDYyO9lU8so93TiISpctWIpnvamAE8FdgHpEEeQrRUrLo/eghwB+J3I9+IbrfH4j8MjATeAJ4gG3ct98Wd38V+B2R+/rTo69hbH37oKI+thCZ+V4zuv944EYiyb1gG+ckIlH266RWEZHqycw6Eynj6+ruM8OORyQTKMGLSLVjZicDPwMLgZZELtEbsL/rf1oiCdE9eBGpjuoReQDOHsCPwFTgMiV3kcRpBC8iIpKFNMlOREQkCynBi4iIZCEleBERkSykBC8iIpKFlOBFRESy0P8Dwkyfe+c15QwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "                                        # x: 0-7.5, y: 0-3\n",
    "def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n",
    "    x1s = np.linspace(axes[0], axes[1], 101)\n",
    "    x2s = np.linspace(axes[2], axes[3], 101)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()] # ravel makes flat\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    if not iris:\n",
    "        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris-Virginica\")\n",
    "        plt.axis(axes)\n",
    "    if iris:\n",
    "        plt.xlabel(\"Petal length\", fontsize=14)\n",
    "        plt.ylabel(\"Petal width\", fontsize=14)\n",
    "    else:\n",
    "        plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "        plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
    "    if legend:\n",
    "        plt.legend(loc=\"lower right\", fontsize=14)\n",
    "    \n",
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(tree_clf, X, y)\n",
    "plt.plot([2.45, 2.45], [0, 3], \"k-\", linewidth=2)\n",
    "plt.plot([2.45, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.plot([4.95, 4.95], [0, 1.75], \"k:\", linewidth=2)\n",
    "plt.plot([4.85, 4.85], [1.75, 3], \"k:\", linewidth=2)\n",
    "plt.text(1.40, 1.0, \"Depth=0\", fontsize=15)\n",
    "plt.text(3.2, 1.80, \"Depth=1\", fontsize=13)\n",
    "plt.text(4.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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