Chapter6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "da23d340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "iris = load_iris()\n",
    "X = iris.data[:, 2:]\n",
    "y = iris.target\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2)\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b2788323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'digraph Tree {\\nnode [shape=box, style=\"filled, rounded\", color=\"black\", fontname=helvetica] ;\\nedge [fontname=helvetica] ;\\n0 [label=\"petal length (cm) <= 2.45\\\\ngini = 0.667\\\\nsamples = 150\\\\nvalue = [50, 50, 50]\\\\nclass = setosa\", fillcolor=\"#ffffff\"] ;\\n1 [label=\"gini = 0.0\\\\nsamples = 50\\\\nvalue = [50, 0, 0]\\\\nclass = setosa\", fillcolor=\"#e58139\"] ;\\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\\n2 [label=\"petal width (cm) <= 1.75\\\\ngini = 0.5\\\\nsamples = 100\\\\nvalue = [0, 50, 50]\\\\nclass = versicolor\", fillcolor=\"#ffffff\"] ;\\n0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\\n3 [label=\"gini = 0.168\\\\nsamples = 54\\\\nvalue = [0, 49, 5]\\\\nclass = versicolor\", fillcolor=\"#4de88e\"] ;\\n2 -> 3 ;\\n4 [label=\"gini = 0.043\\\\nsamples = 46\\\\nvalue = [0, 1, 45]\\\\nclass = virginica\", fillcolor=\"#843de6\"] ;\\n2 -> 4 ;\\n}'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(\n",
    "    tree_clf,\n",
    "    out_file=None,\n",
    "    feature_names = iris.feature_names[2:],\n",
    "    class_names=iris.target_names,\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c67e7fdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(133.92000000000002, 181.2, 'X[0] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]\\nclass = setosa'),\n",
       " Text(66.96000000000001, 108.72, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]\\nclass = setosa'),\n",
       " Text(200.88000000000002, 108.72, 'X[1] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]\\nclass = versicolor'),\n",
       " Text(133.92000000000002, 36.23999999999998, 'gini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]\\nclass = versicolor'),\n",
       " Text(267.84000000000003, 36.23999999999998, 'gini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]\\nclass = virginica')]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ikJiYiMGDByM5ORkfffTRe/spS2BTkbIENteuXYOenh48PT2xceNGLFu2rEby10XWTZs2QSwWM88ORoPDLF0FExgYiBkzZvDHEokEOjo6ePjwIRISEuDo6Ig+ffrA3Ny8SiUSEhIiozQqpl2sKhlNY7B3717Mnj0bAGBubg6xWCwTdlwXGiqBTUPIymDUFqZ0FYyPjw8OHTrEvw4fPXoU5ubmMDQ0hIGBAU6fPo3r168jLi4Of/75J584pqaUJaOJjIzEtWvXEBERAT8/Pz7rWHmio6OrDMUt/4+hJixevBhCoRDjx4/HvXv3AABZWVmQSCTQ1dXl69Um2UxZAhsbGxuZdI71TWDTELIyGIqCTS8oGF1dXdjb2+O///0vvLy8sHPnTvj5+QEoTd04a9YsxMXFgeM4pKenIz4+Xmb+9H1Ul4ymLOlNGYMGDZL7+l5bdu/eDYFAACJCcHAwhg0bhuTkZLl1a+o+11AJbBpCVgZDkTBLtwHw9fXFzp078ezZM8TGxvI5Z5cuXYqOHTvi+vXruHnzJkaNGiV3gUhdXV1me/W3b9/yn6tLRlMRRVm6ZWG+HMfB19cXr1+/RkpKCrS1taGhoYH09HS+bk2TzTRUApuGkJXBUCRM6TYAo0aNwu3bt7Fy5Up4eHhAU1MTAJCTk4Pu3btDQ0MDKSkpOHr0qNz7jY2NcfPmTRQWFkIqlcpsQFldMpqKlFm68sq2bdtq9Cxv377Fy5cv+eOoqCioqamhR48eAICJEyciKCgIAJCYmIj4+Hg+3WNERAS8vb3ltlufBDZDhgyplAO4vrIyGMqCTS80ABoaGvyKe3nlsHz5cnh7e2P//v34+OOPMXToULn329nZYdSoURAKhRAIBLCzs0NCQgKA6pPRNAR5eXkYMWIEioqK0KJFC3Ts2BGRkZHQ0NAAAHz//ffw9vaGsbEx1NTUEBwcjLZt2wIAHjx4gPbt28ttt64JbEpKSnDjxg10795dobIyGMqChQHXk+YWBrxixQq8evWqkstYXRg9ejQ2b94MAwODerdVxpUrV/Dzzz/XK71lVVSXEIiFATMUBbN0GTK0bdsWe/bswZ07d2rtWVGRhvB57du3L/r27avwdgMCAhAVFYX+/fsrvG0GozzM0q0nzc3SZciHWboMRcEW0poQzs7OzSZiKiYmplGDOhiMxoIpXUajwJQu40OFKV0V5K+//oKTkxNEIhFEIpGMy1gZ4eHhsLOzg5WVFUQiEQ4ePMhfW716NUxNTSEWiyESiZCYmAipVIp//vOfMDMzg1gshpWVFbKyshQib2xsLKytrWFpaQlzc3OsW7cOQGkI9JIlS2BrawsrKyuMHj0aGRkZvMta2eaVZVsUhYeH889cPqjhwYMHGDBgAMRiMSwsLHgvh5qGVTMYKgURsVKPUvoVKo7s7Gzq2rUrRUdHExGRVCqlzMxMIiIaOHAgRUREEBFRZmYmSaVSIiJ6+vQp6enpUU5ODmVnZ5OWlha9efOGiIgKCwupoKCArl+/TqamplRcXExERHl5eVRUVFSp/7t375JYLJZbRo0aJVdmd3d3CgsL44+zsrKIiGjNmjX09ddf83Ju3ryZPD09iYjo22+/pblz5/L33Lp1i3R0dCg1NZWIiIKCgsje3p6IiObMmUNr1qyp1H5eXh69ffuWiIjevXtHDg4OdPz48fd+x3Xh779zo483Vpp+Yd4LKsbFixdhbGzMh/lyHIfOnTtXqvf48WN8/vnnSE1NhYaGBrKzs3Hv3j307dsXJiYm8PT0hIuLC0aMGAEDAwP06tULJSUlmDJlCgYNGgQ3N7dKuwADVWf+qo4hQ4Zg9erVePDgAZydneHk5ASg1HshNzeXDwIpLi6GlpaW3DbOnDmDESNG8IEM06dPh7+/P/Lz8+Hs7Iz58+cjPz8fAwcO5P2bFRFWzWAoGza9oIKUzztQFZMmTcKUKVP4yCpdXV0UFhaiRYsWuHjxIubPn4+cnBw4OTkhKioKWlpauHXrFry9vfHw4UNYWlrKVa737t2rMnTY3d1drixz5sxBVFQUunfvjlWrVsHT0xNA6VvUjz/+yEfB3bp1CxcuXKj1dzBmzBhcvHgR5ubm2LFjB6/UaxpWzWCoFI1tajf1AiVML7x8+ZKIZKcXOnXqRJcvXyYiot9//50AUHR0NOXl5dGTJ0/49vz8/GjlypWUkZHBt0NENHToUNq1a5dCZL5z5w7/+dy5c9SzZ08iIlq9ejW5uLjQ69eviah0quPGjRtERLRx40aaPHkyf19iYiJ17dqV0tLSiIho69at1K9fPyIiun//Pj8tkpaWRi1btiSJREJjx46lH374gYiIHj9+TNra2vTtt98q5JkqAja9wIqCCpteUDE6duyI3377DfPnz0deXh6A0lSF//jHP2TqbdmyBR4eHtDT04OlpSUsLCwAALm5uRg3bhwKCgqgpqYGgUCAwMBApKamYtq0aZBIJCAiWFlZYcKECQqROSgoCGfOnEGrVq3AcRyf32DRokUoKiqCvb09OI6DVCrFnDlzIBKJMHbsWOzZsweWlpYYPnw4AgMDsXHjRri5uQEAunXrhtDQUACl+XVDQ0PRsmVLSKVS/Pzzz1BXV69xWDWDoUqw4Ih6woIjPgxYcARDUbA5XQaDwVAiTOkyGAyGEmFKl8FgMJQIU7oMBoOhRJj3Qj1p3br1C47juja2HIyGpXXr1i8aWwZG84B5LzQxOI7rCOAsgP1EtKax5VEmHMe5AAgFMAVAMABdIpI2rlQMRu1gSrcJwXFcGwCnAFwGMO9D8lXjOG4VgEcAJAACARQCmEREVxtVMAajlrA53SYCx3EtARwCkARg/oekcP8mBoA7gB8BpADQAzCxMQViMOoCs3SbABzHtUDpa3VbAOOISNLIIjUaHMfpAPAC8BWAYiKqvEMlg6HCMKWronAcFwDgBYC9KLXuhACGExHL6AKAK82Io0FERY0tC4NRG5jSVVE4jrsOYC6AISh9rR5ERLmNKxWDwagvzGVMBeE4Tg+AAYA+AP4BwKGpKVxNTc3nb9++Za50zYTWrVu/KCws7NbYcjQHmKWrgnAc5wdgKoDuABYAcAPwiojmNqpgtYAlAmpesIQ/ioMpXRWE47gLAPqidE73FUp9UvcQUWZjylUbmNJtXjClqziY0lVBOI5LQakvbiCA601RezGl27xgSldxMKXLaBCY0m1eMKWrOFhwBIPBYCgRhXsvsFXrpgVblWYwlIvCLd23b992beyN31ipeWlK/yBfvXoFgUCAmJgY/tyePXsgFotRVFSEmJgYaGpqwtLSEmlpaQCAo0ePwtraGq1atYK/v79Me+vXr4eRkREsLS2V9xB/M2vWLBgYGIDjuGq3vI+IiJDZkVlXVxd9+vThr3McB6FQyF+PiopSgvSMeqHoHzEUvDsuo2FBA+1y21Dj4OTJk2RgYEB5eXmUmppKOjo6/A7D0dHRJBaLZerfvXuX4uPjadmyZTR37txK7cm7pyZkZmbWQfr/ERMTQ2lpaaSvr09xcXE1vm/YsGG0ceNG/hgA5eTk1EuWmtBQ4+RDLCw4gtGkcHFxgZubG+bOnYvU1FQEBARAJBJVWd/ExARAqcVYX5KTk7Fv3z7s378fTk5O+PHHH+vc1sCBA2t9T0pKCs6dO4fw8PA698tofJjSZTQ51q9fD0NDQwgEAixcuLBB+3r69CkOHDiAffv2QSqVwsPDA0ePHoW+vj5fx9HREfn5+XLvP3nyJHR0dBQiy6+//gp3d3doa2vLnB86dCgkEgkGDhyIVatWQUtLSyH9MRoGpnQZTY5Lly6hRYsWePLkCXJyctC5c+cG6efw4cOYMGECxo8fj/DwcBgaGsqtd+7cuQbpvzxSqRQhISEIDg6WOZ+SkgKBQIDCwkL4+/tj5syZCAsLa3B5GHWnWbuMubm54d69e++t98033yh0oL58+RJubm4wNjaGmZkZjh8/XmXdK1euwMrKCr1794aDgwMePnyoMDmaI7m5ufDz80N4eDi8vb0xc+bMBuvLxcUF27dvR05ODtzd3fH111/j1q1bleo5OjrKLHaVLxkZGQqR5fjx49DQ0MDgwYNlzgsEAgCApqYmZs+erZR/AIx6ouhJYrCFNJo6dSotXbqUiIhu3bpFOjo69Pr160r1pFIp9e7dm06ePElERL/88gsNHTpUqbKiiS2keXt707x584iI6N27dyQUCmnfvn1EVP2i2LfffluvhbQXL15QUFAQOTg4kLm5Oe3cubOujyBDTRfSxowZQ2vWrJE5l5mZSYWFhUREVFJSQosXLyZ3d3eFyFWRhhonH2JpMj+2qjhy5AiZmJiQWCymJUuWkJaWFj169IiIZAf0wIEDacGCBeTo6EiGhobk7e1NUqmUiIh8fHxo06ZNCpPpo48+omfPnvHHLi4udPDgwUr1rly5Qr179+aPi4qKqG3btpSRkaEwWd5HU1K6ERERZGZmxisaIqK4uDjq1q0bpaeny1Wgx48fp48//pjatWtHbdu2pY8//pgOHDjAX6+L90JqaiqdOHGiPo9Cfn5+9PHHH5Oamhrp6OjQxx9/LHPtt99+449fvHhBmpqa9PTpU5k2Tp48SRYWFiQSicjc3JwmTZpEz58/r5dcVcGUruJKk57TzcjIgK+vLy5cuABTU1Ps2LEDublVZ0BMSkrCmTNnUFJSAktLS5w8eRKurq7V9hEQEIDo6Gi519auXQs3NzeZc1lZWZBIJNDV1eXPGRgYIDU1tdL9KSkpMgsyGhoa0NXVRVpaGrp06VKtXB8io0ePxujRo2XOWVpaIj09HQBw9+7dSve4urriyZMnCpWjR48e6NGjR73a2LFjR42v6ejo4M2bN5Xqubi4ICEhoV5yMJRPk1a6sbGxEIlEMDU1BQBMnjwZX375ZZX1J06cCHV1dairq8PGxgYPHjx4r9LdtGlTveUsNRTkU7oBQs3qMqqnZcuWyMrKgqWlJX7//ff3Ksb169cjODgYenp6SpKQwWjiC2lEJKO0/k7KUWX91q1b85/V1NRQXFz83j4CAgKqXCSRF/2jra0NDQ0N3voC/rfCXBGBQIDHjx/zxxKJBM+fP6+3FfWh0r9/f6SlpSE+Pr7a77BsgXXhwoW4ffs2Tp8+LbdeYy6wGhgYwMTEhB9r1VnGjKZFk7Z0+/XrB19fX9y7dw8mJibYtWsXSkpKFNpHXSzdiRMnIigoCGvWrEFiYiLi4+MxfPjwSvVsbGwglUpx6tQpuLi4ICQkBHZ2dmxqoYGpaajsd999p9B+lyxZAisrK0RFRSExMRGDBw9GcnIyPvroI7n19+/f3yghyoyGpUlbujo6Oti+fTs+++wz9O/fH0+fPoWmpiY6dOjQqHJ9//33uHbtGoyNjTFu3DgEBwejbdu2AIBt27bhm2++AVBqmYeGhmLhwoXo3bs3QkJCsG3btsYUvdnw22+/wdTUFJaWlli6dCk6dOjAv1UYGBjw+Q6cnZ2xcOFCODk5wcjICD4+Pvzb0uTJk7F582aFybR3717Mnj0bAGBubg6xWIxjx44prH1GE0HRK3NQsvdCXl4e//nw4cNkamqq1P6bOmhC3gs15cWLF9SpUye6c+cOERFt376dAFTp1TJ69GiSSCT09u1bMjU1pePHjxNR9V4t/v7+JBaL5Zb/+7//q1T/5cuX1LJlS5lz06ZNow0bNshtX19fn0QiEVlYWJCPj08lzwVl01Dj5EMsTXp6AQC2bt2K8PBwlJSUoH379iwah9EsFljPnj0LfX19FBcXY+3atZgwYQIuXLhQ7z4ZjU+TV7qLFi3CokWLGlsMhgpBpJwF1tq4EpZfYC1zJ0xJSalSuZe5EqqrqyMgIAArVqyARCKBhobGe2VjqDZNek5XkcTExDT6okVISAi0tLT4FWsbGxuZ64GBgTA0NIShoSH8/f0hlUobSVLVpl+/frhx4wYfAt5QC6zx8fFyS0WFW0bZAiuAahdYc3NzZRLohIaGQigUMoXbTGjylm5zY9CgQThy5Eil8xcuXMCuXbsQHx8PTU1NDB06FHv37oWnp6fyhVRxyi+wdurUCSNGjFCZBVZvb28YGxtDTU2t0gLrs2fP8N133yE1NRVeXl78HKBAIMDBgwcbVXaGAlH0JDHqsYDy5s0bmjhxIpmZmZFQKCQ7OzsiIpJIJOTq6krW1tZkbm5O48eP5xM3R0dHk7m5Oc2YMYOEQiGZm5vTjRs3yMfHhywsLMjW1pZfhAgODqbBgwfThAkTyNLSkuzs7PjFlorhoMePH6cBAwZQnz59yNramo4dO0ZERBkZGTRs2DCysLAgoVBIY8eOrfPzViQ4OJg+++wzuddmzZpFa9eu5Y/DwsLo008/rXefaIYLaURsgVXRNNQ4+RCLSv3YDh8+TC4uLvxxVlYWEZUmhnn58iV//quvvqLFixcTUamyVFNToytXrhAR0eLFi0lbW5tXptOnT6dFixYRUalSa9myJSUkJBAR0bZt26hv3758O2VK9+HDh9S3b19esaemppKenh7l5OTQxo0badq0aZVkrEhoaGiVq9srV66Ue09wcDBpa2uTWCwme3t72rNnD39t5MiRFBYWxh9fuHCBhEJhdV9njWiuSjcwMJDPSdCvXz+6du1ao8rT1GFKV3FFpaYXLC0tce/ePcyYMQNOTk783BgRYfPmzYiMjERxcTFev37Nr0wDgJGRET//aWNjg+joaP66ra2tjC9kv379YGFhAQDw8/PD7NmzkZeXJyPH8ePH8ejRIzg7O/PnWrRogaSkJPTr1w+bN29GQEAAnJyc5M7JAYCnp2etX/1HjhwJDw8PtGnTBklJSRg2bBi6du0KFxcXALIhw6W/A0ZVsAVWhqqiUgtpPXv2xO3btzFy5EhcvXoVn3zyCZ49e4bw8HCcPn0aMTExSEhIwMqVK1FYWMjfV3H1ubrV6Iq5DuSdIyK4uLjILI6kpaXBxsYG9vb2iI+Px4ABAxAVFQWRSCQjSxlhYWFVhg9XFenUuXNntGnTBkDpP5IxY8bw+VErhgxXFVrMYDBUG5VSuk+ePAERYeTIkVi/fj3at2+PpKQk5OTkQFtbG1paWnjz5k294tAvXryIxMREAEBwcDD69OmDdu3aydRxdXXFqVOnEBcXx5/766+/AACPHj1CmzZtMH78eGzZsgUZGRnIzMys1I+np2eVq9tlEWnynr+Mly9f4sSJE7CysgIATJo0Cbt370Z+fj6Ki4vxyy+/YNKkSXX+HhhVowqeLLGxsejfvz/atGlTKbMaUL0nS0hICIyNjWFkZAQvLy+5RgGj8VCp6YWEhAQsXrwYAFBcXAw3Nzc4ODhALBbj6NGjMDMzg66uLuzs7HDlypU69eHs7IzAwEDcunULrVq1wq5duyrVMTIywr59+zBr1iwUFBSgqKgIhoaGiIyMRExMDDZs2AA1NTWUlJRg6dKlCrM4161bhzNnzkBDQwMlJSXw9fXFmDFjAJTuTuDl5QWxWAygdCriH//4h0L6Zageenp62LJlC65fv14pVLg6T5aUlBQsWbIE165dg56eHjw9PbFx40YsW7askZ6EUQlFTxJDhXeOqM474EMFKrqQ9qF7spQhb8xW58myfv16mj59On/twoULZG5uXm85GmqcfIhFpSxdBqOM48ePIzs7G7dv3wYAZGdnAyidow8LC+N3xF20aBHWrVuH77//HkBpIvOQkBD89NNPWLJkCQYPHozz58/D1NQUX3zxBf79738jMDAQAHD+/Hlcu3YNFhYW+Pnnn+Ht7Y3Lly/LyJGcnIyvv/4aJ0+eRIcOHZCWlgZ7e3skJiYiNDQU+vr6OHHihIyMFQkLC8P69evlXhs7dmyV001VkZKSgv79+/PH5ZPkV0yMX1UCfUbj8UEp3cmTJ2Py5MmNLQajBnzonizvozpPFublotp8UEqX0XQo82SJjo7GmTNnsGDBAly9ehVnzpzhPVm0tLSwe/du7Ny5k7+voTxZwsPD5coZHx+PP/74A1FRUfjqq69w8+ZNaGpqytRRtKVbnSeLQCBAUlKS3GsM1UClvBcYjDI+dE+W6qjOk2XChAk4evQonj17BqA0Cx/zclEtVFbprlixAv7+/o0tBlasWIEuXbrIZINydnZGz5495frd1mZLlorUNaHNlStXYGVlhd69e8PBwQEPHz7krzk6OqJTp04KTcatDBISEjBgwACIxWKIRCLek8Xb2xvv3r2DmZkZRo4cCTs7uzr3UebJYmVlhV9//fW9nixisRhmZmZYtWoVgFLXMmtra4jFYtja2irUk+X27dvo3r075s2bhxMnTqB79+7YuHEjAFlPlt69e0MkEvGeLAYGBlizZg2cnJxgaGgIqVSKefPmKUQmhoJQ9MocFOS98O2339LcuXMV0pai5Rg4cCBFRETIrT916lRaunQpERHdunWLdHR06PXr1+/t5/z582Rqakp5eXkkkUho4MCBFBoa+t77pFIp9e7dm06ePElERL/88gsNHTpUpk51ybihot4LDQ3zZKkdDTVOPsTS4JZuYGAgZsyYwR9LJBLo6Ojg4cOHSEhIgKOjI/r06QNzc/MqfQlDQkJkHMQrOq+fOHECDg4OsLa2ho2NTa2sS0VT1y1Z9u7dC29vb7Rr1w7q6uqYPn069u7d+977rl27BgB8qPDkyZMRGxsr9zWXwWA0Pg2udH18fHDo0CE+Kubo0aMwNzeHoaEhDAwMcPr0aVy/fh1xcXH4888/efebmlLm0hMZGYlr164hIiICfn5+ePXqVaW60dHRVYbmlv/HUBMWL14MoVCI8ePH83lbs7KyIJFI+CTVQM1ddurq6lPxPg0NDejq6iItLa02j/PBMXnyZLkpNBmMhqbBvRd0dXVhb2+P//73v/Dy8sLOnTvh5+cHACgsLMSsWbMQFxcHjuOQnp6O+Pj4926VUp7qXHoqJgEfNGgQvyFhfdi9ezcEAgGICMHBwRg2bBiSk5Pl1i19M6sZdXX1kbfizmAwVBOlLKT5+vpi586dePbsGWJjYzFu3DgAwNKlS9GxY0dcv34dN2/exKhRo+TGiaurq8tk/n/79i3/majq5DQVUZSlW7ZYwnEcfH198fr1a6SkpMhsyVJGTV126prQpuJ9EokEz58/R48ePWr8PKqOKi+qPnz4EA4ODujduzesrKxw9erVGrXVULuEGBgYwMTEhG+3vHdHdbJOnDgR3bp1U4nvubmjFKU7atQo3L59GytXroSHhwfvx5iTk4Pu3btDQ0MDKSkpOHr0qNz7jY2NcfPmTRQWFkIqlcpsPlmdS09FyixdeaWmW5+/ffsWL1++5I+joqKgpqbGK7nqtmSJiIiAt7e33Hbfl9DG1NQUT58+rXSfjY0NpFIpTp06BaD0x2xnZ4cuXbrU6HkYtcPT01NmCmzGjBnw8fHB/fv38cMPP8DT07PGbxrlx2N5BVg+t8K9e/cQHx9fo/n9Mvbv38+3O3Xq1BrJun///lpPsTHqhlKUroaGBjw9PfHLL7/A19eXP798+XLs3r0bNjY2mDNnDoYOHSr3fjs7O4waNQpCoRBDhw5F9+7d+WvVufQ0BHl5eXB1dYVQKIRYLMYPP/yAyMhIfv+q77//HteuXYOxsTHGjRsnsyXLgwcP0L59e7ntVucGlJGRgaysLHTq1KnSfRzHITQ0FAsXLkTv3r0REhJS438gyqa5LapmZmYiNjaWj3J0cXEBEfGLm3WlrouqjSEro/YoLSJtw4YN2LBhg8w5KysrJCQkyK2/YsUKmeMy67GMslh7ABgyZAiGDBmiGEHfg46OTrUDVUdHp8of+sWLF6v1l122bJlcZXP27FnMnTu3UqRTGXZ2dgqZq25ofHx8IBQKsWnTJmhqasosqubn5+P06dNo1aoVioqKMGTIEJw4caJW8/vV5UmouD9adHQ0AgIC5LZjb29fo39cqamp0NXVldkwsmwBVN70VkXOnz8PS0tLaGpqYtasWfDy8gJQfW6FmuDj4wOpVApra2usXbsWenp69ZaVoThYGPB7aNu2Lfbs2YM7d+7U2rOiInVdLZ8wYUKd+3R0dMSTJ08wYMCAOrehKJrjompdFzEbapeQs2fPQl9fH8XFxVi7di0mTJiACxcu1EtWhmJReaXr7OwMf39/uYmclcGCBQuwYMEChbQVExODt2/fVpkYpSEo23lCVfD19UVQUBAGDx6M2NhYHDp0CIDsoqqGhgZ8fX3rvKhaVZ6E8ijC0hUIBHj27BkkEglvQdZ0AbRz58785/K7hLi4uNRrl5Ay90F1dXUEBARgxYoVkEgk9ZKVoVhUNgy4ORITE9OogRuqQHNaVO3SpQtsbW0REhICADh16hSICNbW1gBKp8SWLFki99767BJS1aJqbm4u8vPz+ePQ0FAIhUJoaGi8V1aG8lAZpfvXX3/ByckJIpEIIpFI5sdURnh4OOzs7GBlZQWRSISDBw/y11avXg1TU1M+Vj8xMRFSqRT//Oc/YWZmBrFYDCsrK2RlZSlE3tjYWFhbW8PS0hLm5uZYt24dgNLFoSVLlsDW1hZWVlYYPXo0MjIy+B9z2d5pZTtkhIeH889c3t/3wYMHfO4BCwsLzJkzBwBqvOCkqjSnRVUA2LZtG4KDg9G7d28sXLgQYWFh/Gv87du3+by/FVm3bh3Mzc1haWmJQYMGVblLSG0WVVNTU+Hg4ACRSAShUIjIyEiZ30h1sjKUiKLjilGHmPvs7Gzq2rUrRUdHE1FpPoHMzEwiks1zkJmZSVKplIiInj59ym+Lnp2dTVpaWvTmzRsiIiosLKSCggK6fv06mZqaUnFxMRER5eXlUVFRUaX+7969W+V26aNGjZIrs7u7u8yW6GVbsa9Zs4a+/vprXs7NmzeTp6cnEVXO41CWmyE1NZWIiIKCgsje3p6IiObMmUNr1qyp1H5eXh69ffuWiIjevXtHDg4OdPz48fd+x1WBDzT3Ql2obT6QAQMGUF5enkJlOHDgAK1atUqhbZZR3fM11Dj5EItKzOlevHgRxsbG/AIIx3Eyc15lPH78GJ9//jlSU1OhoaGB7Oxs3Lt3D3379oWJiQk8PT3h4uKCESNGwMDAAL169UJJSQmmTJmCQYMGwc3NrVLqPgAwMTGp9aLKkCFDsHr1ajx48ADOzs5wcnICULpYlpuby78eFxcXQ0tLS24bZ86cwYgRI3gf3+nTp8Pf3x/5+flwdnbG/PnzkZ+fj4EDB/KWnyIWnBh1o7aLqufPn1e4DPVZVK2OiRMn4tq1a2zfPSWgEkoXkJ9QuiKTJk3C2rVr4eHhAQDo1asXCgsL0aJFC1y8eBGxsbE4e/YsnJycsG3bNri5ueHWrVv4888/eX/OY8eOVdrp9d69e5g4caLcPgUCgdz5xTlz5sDd3R1//PEHVq1aBR0dHYSHh4OI8OOPP2LYsGH1+g7GjBmDfv364fTp09ixYwe+++47XLx4scYLTgzFo8hFVVVj//79jS3Ch4OiTWcoaHrh5cuXRCQ7vdCpUye6fPkyERH9/vvvBICio6MpLy+Pnjx5wrfn5+dHK1eupIyMDL4dIqKhQ4fSrl27ai2fPMo2MSQiOnfuHPXs2ZOIiFavXk0uLi58OsfCwkK6ceMGERFt3LiRJk+ezN+XmJhIXbt2pbS0NCIi2rp1K/Xr14+IiO7fv89Pi6SlpVHLli1JIpHQ2LFj6YcffiAiosePH5O2tjZ9++23dX4OqOD0QnWpM5szV65cIQ8Pj3q1AYDfqFORNNQ4+RCLSli6HTt2xG+//Yb58+fze1QtXry40qvOli1b4OHhAT09PVhaWvL7W+Xm5mLcuHEoKCiAmpoaBAIBAgMDkZqaimnTpkEikYCIYGVlpbDXs6CgIJw5cwatWrUCx3F88MaiRYtQVFQEe3t7cBwHqVSKOXPmQCQSYezYsdizZw8sLS0xfPhwBAYGYuPGjfz+X926dUNoaCgA4ODBgwgNDUXLli0hlUrx888/Q11dHcuXL4e3tzf279+Pjz/+uMoFJ4ZqUlxcDHV1+T87GxubRrE4q5OJ0QAoWoujGS6gNGfQiJZubGwsOTo6klAoJKFQyCdtL2/phoWFka2tLVlaWpJQKKQDBw7w969atYpMTExIJBKRUCikW7duUUlJCc2ePZtMTU1JJBKRpaWlzNtOfTA1NaVLly7xx4cPHyZHR0ciIkpPTycPDw/q27cvCYVCmjdvHpWUlPDPs2TJEhoyZAj169evyq3bK279HhUVRX379iWRSEQikYj++OMPIiK6du0a9e/fn4RCIVlbW/MJ7IlkLd331Vu/fj05OzvTzJkz3/vsDTVOPsTClO4HTmMp3abosbJu3TqaNm0afzxy5EgKCQkhIiIXFxc6ffo0EREVFxfTuHHjaPv27fzzuLq60rt374iodJqpfDtlninlle79+/epS5cudOvWLSIikkgklJ2dTe/evSOBQECRkZFERBQXF0edOnWijIwMIvqf0q1JvRUrVlT3J5KBKV2mdBkKorGUbmRkJDk4OMi9Vl7pXrlyhYYPH06ffPIJicViat26NcXGxlJJSQnZ2trSmDFjaOvWrfTo0SMiInr16hUZGxvT559/Tjt37qTnz5/X7YuRw/Pnz6lTp05UUFBA6enp1LFjRyooKKDXr1+TmpqajOI2NjamBQsW8M+zZ88evp1Lly6RQCAgf39/Onz4MP+Po7zSDQoKIi8vr0oy3Lx5k/T19WXOOTk50e+//05E/1O6NalXtpZQE5jSVVxhEzmMRqOpeax07doVAwYMwKFDh5Ceno7x48ejTZs2eP36NYDSgJnyW76XpyzTHFAaZixv6/a6fD/1obxMDCWiaC0OZuk2KaBC0wuq7rFCRBQREUHOzs5kampKsbGxMv0sW7aMnwp58eIFJScnV3oeIqLk5GQ+wKWgoIDat29PKSkpMpbugwcPZKYXiouL+ekFfX19ioqKIiKiGzdukLa2ttzphZrUqykNNU4+xKIyYcCMD4syj5Xly5dDJBJBLBbLDTgo81gZMGAAjh07JuOxMnbsWFhYWEAsFuPFixeYOXMm0tLSMGzYMD4UVldXV6EBBSNHjsSdO3fQokULme3fw8LCkJKSAqFQCKFQiE8//VRmB5Hy1GTrdiMjI4SEhMDHxwcikQjW1taIj49Hy5YtcfjwYaxevRpisRhTpkzB3r17KyWtr2k9hvLhSv+JKQ5NTc3nb9++7arQRhkNRuvWrV8UFhZ2U3S7HMeRoscWo/HgOA5ExBI1KACFK10GA2BKt7nBlK7iYNMLDAaDoUSY0mUwGAwlwpQug8FgKBGmdBkMBkOJsOAIRoPQunXrFxzHMS+WZkLr1q1fNLYMzQXmvcBosnAc9wWArwA4EJF8p9hmCsdxbQCcBHAFwDzmKtJ0YNMLjCYJx3EeAL4BMOxDU7gAQERvAIwCMATA0kYWh1EL2PQCo8nBcdwwAD8CcCGih40tT2NBRDkcx7kCOM9xXBYR1WwbY0ajwixdhsrDcZyQ4ziDvz/bAwgFMJaIKmeJ+cD428p3AbCc47iJAMBxXEeO4xwaVzJGVbA5XYbKw3Hc/wEIBnAHwBkAU4goqnGlUi04jhMBOAXAG8BTAL8BMGJzvaoHs3QZKg3HcZoAHAHcB3AcQABTuJX52+ofC2APgHYAWgHo3ahCMeTClC5D1XEGcAvAfwGsAxDPcZxdtXd8gHAcZwqgBIAPgCMALgFwa0yZGPJhSpeh6owG0AvAXQCfAzgNQNiYAqko+gB2A9gI4A+UejWMbVSJGHJhc7oMlYbjuNcAWgM4jNJ53VNEVNy4UqkmXOlWE/0ATAHghdLvrS0RFTSqYAwZmNJlqDQcx00HcJiIXja2LE0JjuM+AuAL4D9EJG1seRj/gyldBoPBUCJsTpfBYDCUCItIa0DY1kVNi4bauqgibFw0LRQ9Ltj0QgPCtqxpWihrSxo2LpoWih4XbHqBwWAwlAhTugwGg6FEmNJlMBgMJcKUbjPBzc0N9+7de2+9b775BmFhYQrr9+XLl3Bzc4OxsTHMzMxw/PjxKuvOmjULBgYG4DgO8fHxMteKiorg7+8PY2NjCIVCuLu789f++usv9OvXD1ZWVjAzM8PKlSsVJn9zpymMiytXrsDKygq9e/eGg4MDHj6snK0zODgYHMfhyJEj/Ll58+ZBJBLBysoKlpaW2Ldvn8Lkb1CIiJUGKqVfb/Nm6tSptHTpUiIiunXrFuno6NDr16/l1o2JiaG0tDTS19enuLg4mWvz58+nL7/8kkpKSoiIKD09nb9mYWFBv/32GxER5eXlUY8ePejixYsKf5a//15sXCiAmo4LqVRKvXv3ppMnTxIR0S+//EJDhw6VqfP48WPq168f2dvbU0REBH8+JyeH//zkyRNq164dZWZmKvxZFD0uGl0xNeei6B/XkSNHyMTEhMRiMS1ZsoS0tLTo0aNHREQyimzgwIG0YMECcnR0JENDQ/L29iapVEpERD4+PrRp0yaFyfTRRx/Rs2fP+GMXFxc6ePBgtfdUVLoFBQXUrl07ys3NlVvfwsKCdu3aRUSlylggENCDBw/qL3wFmqrSbcrj4sqVK9S7d2/+uKioiNq2bUsZGRlERFRSUkJDhgyhq1ev0sCBA2WUbnnu3LlDbdu2pRcvXijsGcpQ9Lhg0wtNhIyMDPj6+uLIkSOIj49Hr169kJubW2X9pKQknDlzBomJibh8+TJOnjz53j4CAgJgaWkpt0RFVc6mmJWVBYlEAl1dXf6cgYEBUlNTa/VsSUlJ6NixIwIDA9G3b18MGDAAkZGR/PXdu3fjm2++QY8ePdCzZ08sWrQIRkZGteqjudLUx0VKSgr09fX5Yw0NDejq6iItLQ0AsHHjRgwYMADW1tZyZVu/fj169+4Na2trbN++HTo6Ou99nsaGBUc0EWJjYyESiWBqagoAmDx5Mr788ssq60+cOBHq6upQV1eHjY0NHjx4AFdX12r72LRpU73lLDUMaodEIkFqaiqMjIywdu1a3L17F05OToiNjUWvXr2wbt06rFq1Cp9//jmePHmCgQMHwtraGnZ2LMNjcxgXpXl6KtdNTEzEoUOHcO7cuSrvXbhwIRYuXIjr16/D29sbLi4u0NbWrre8DQmzdJsIRCQzOP922K6yfuvWrfnPampqKC5+f2Ku2lo02tra0NDQQHr6//aFTElJgUAgqOljAQD09fXBcRy8vLwAAKamphCLxbh+/TpevnyJw4cP4/PPPwcAdO/eHYMHD8aff/5Zqz6aK019XAgEAjx+/Jg/lkgkeP78OXr06IE///wTKSkpMDY2hoGBAWJjYzF9+nQEBQVVaqdPnz7o1q0bYmJi3vs8jQ1Tuk2Efv364caNG/xK9K5du1BSUqLQPjZt2oT4+Hi5xc1Nfj7siRMn8j+CxMRExMfHY/jw4bXqt3PnznB1deVXuNPT05GQkAChUIiOHTuibdu2OHXqFAAgNzcX586dg0gkqseTNh+a+riwsbGBVCrl/74hISGws7NDly5d8OWXXyI9PR2PHz/G48ePYW9vj19++QWzZ8/m2y3jwYMHuHHjBj755BOFPntDwKYXmgg6OjrYvn07PvvsM3Tq1AkjRoyApqYmOnTo0Khyff/99/D29oaxsTHU1NQQHByMtm3bAgC2bduGZ8+e4bvvvgMATJ06FcePH8fz58/h6uoKDQ0NPHnyBADw008/wc/PD8uWLQPHcdiwYQNMTEwAAAcOHMCiRYuwYMECFBUV4fPPP3/vK/GHQlMfFxzHITQ0FF988QXevHmDLl26YNeuXTXq45///CdevHgBDQ0NqKurY+vWrTAzM2vIx1IILPdCA6LoGPv8/Hy0a9cOABAREYGlS5fizp07Cmv/Q6ep5l5g46JhUfS4YJZuE2Lr1q0IDw9HSUkJ2rdvr1BndkbThY2LpgWzdBsQlk2qadFULV1Gw8KyjDEYDEYThildRiViYmJgaWnZqDKEhIRAS0uLd02ysbGpVKewsBBmZmaNLuuHgiqMizIyMjLQtWtXjB49Wub8hQsXYG9vD3Nzc3zyySe8V4QqweZ0GSrLoEGDZBKcVGTRokVwcHDAlStXlCcUQyX44osvMHLkSGRlZfHnnj9/Dk9PT0RGRsLCwgISiQR5eXmNKKV8mKWrohQWFmLSpEn45JNPIBKJYG9vDwAoLi7G8OHDYWNjAwsLC0yYMAGvXr0CUGqJWFhY4Msvv4RIJIKFhQVu3ryJyZMnQygUws7ODs+ePQNQakkOGTIEHh4esLKygr29Pe7evStXlhMnTsDBwQHW1tawsbHh/WkzMzPh6uoKoVAIkUiEcePGNfwX8zenT5/G06dP4enpqbQ+VQE2LoBff/0VPXv2hKOjo8z5//znP5g0aRIsLCwAlIYUq2R0miITObCiuMQmhw8fJhcXF/44KyuLiEqzMr18+ZI//9VXX9HixYuJiCg6OprU1NToypUrRES0ePFi0tbWpjt37hAR0fTp02nRokVERBQcHEwtW7akhIQEIiLatm0b9e3bl29HLBYTEdHDhw+pb9++fEan1NRU0tPTo5ycHNq4cSNNmzatkowVCQ0NJbFYLLesXLlS7j3BwcGkra1NYrGY7O3tac+ePfy1nJwcsrKyohcvXsjIWl/QBBLefOjjIjk5mWxsbOjNmzcUHBxMn332GX9tzJgx5O/vT0OHDiWxWEzTpk2jV69evf9LfQ+KHheNrpiac6nPjys5OZkEAgF98cUXFBYWxg/ukpISWr58OVlaWpKFhQUZGBjQ8OHDiaj0R2FiYsK3cejQIbKzs+OPd+zYQePGjSOi0h/XwIED+WsSiYTU1dUpNzdX5sf1n//8hzp37izzg+jevTtduXKFLl26RAKBgPz9/enw4cP05s2bOj9vRTIzM6mgoICIiB48eEA9e/bk0/95enrSgQMH+Gf+kJTuhzwupFIpOTs706VLl3hZyyvdkSNHklgspqysLCouLqYvv/yS/Pz86t2voscFm9NVUXr27Inbt28jOjoaZ86cwYIFC3D16lWcOXMGp0+fRkxMDLS0tLB7927s3LmTv69ibH11sfYVE43IO0dEcHFxQXh4uFw54+Pj8ccffyAqKgpfffUVbt68CU1NTZk6YWFhWL9+vdz7x44di2+++abS+c6dO/OfjYyMMGbMGJw7dw4uLi44f/48zp8/j4ULF+Lt27fIzs6GkZERkpKS5PbRnPiQx0VeXh5u3ryJSZMmAQBev36NN2/ewNnZGTExMTAwMIBQKESnTp0AAJ6envjiiy/ktt+oKFKDs6I4iyYtLY3y8/OJiKi4uJhMTEzo7Nmz9O9//5s+/fRTIirNQ+vo6MhbJhWtvoiICBmrpbxlUPYaeevWLSIqTR5ta2tbqZ0HDx5Q586d6fr163w7sbGxRFRqdb19+5aXpX379pSSklLnZ674/GVkZmaSubk5HT58uFK9D83S/dDHRXkqWrqxsbHUr18/KiwsJCKipUuXkqenZ737UfS4YJauipKQkIDFixcDKF0kcXNzg4ODA8RiMY4ePQozMzPo6urCzs6uzqv3zs7OCAwMxK1bt9CqVSu5Me9GRkbYt28fZs2ahYKCAhQVFcHQ0BCRkZGIiYnBhg0boKamhpKSEixdurTWGcaqYt26dThz5gw0NDRQUlICX19fjBkzRiFtN2U+9HFRHXZ2dhg/fjz69OkDNTU1GBsb4+eff27wfmsLi0hrQFQ58igkJARHjhyp1iXrQ4NFpLFxIQ8WkcZgMBhNGGbpNiCqbNEwKsMsXYY8mKXLYDAYTRimdJsAK1asgL+/f2OLgRUrVqBLly4yCcQfPnwIBwcH9O7dG1ZWVrh69Wqt2pQXQ19QUIApU6ZAKBTC1NQUixcvRk0tQwMDA5iYmPA5G3bs2MFfc3R0RKdOnbB58+ZayaiqNMdxERsbi/79+6NNmzaV8irUlMmTJ4PjOD4iD1CtccGULqNWeHp64sSJE/zxjBkz4OPjg/v37+OHH36Ap6dnjRUk8L8Y+vKsXbsWJSUluHHjBhISEnDjxg0cOnSoxm3u37+f305m6tSp/Plz587B3d29xu0wao6ixoWenh62bNlS580wDx8+DA0NDbnXVGVcMKWrRAIDAzFjxgz+WCKRQEdHBw8fPkRCQgIcHR3Rp08fmJubY9myZXLbCAkJkbEAKmZ+qioeviHIzMxEbGwsJk+eDABwcXEBEeHatWs1ur+qGPobN25g+PDhaNGiBTQ0NODi4oI9e/YoWnyVgY2L/yEQCNC3b1+0atWq1v2+ePECa9euxYYNG2p9rzJhfrpKxMfHB0KhEJs2bYKmpiaOHj0Kc3NzGBoaIj8/H6dPn0arVq1QVFSEIUOG4MSJE7XaCyw5ORlff/01Tp48iQ4dOiAtLQ329vZITEystGdWdHQ0AgIC5LZjb2+Pbdu2vbe/1NRU6OrqylgWBgYGSE1NlZuKsTyPHj3Ctm3b8Oeff2L//v0y16ytrXHw4EGMGzcOEokER44ckXlVfB8+Pj6QSqWwtrbG2rVroaenV+N7GwM2LhTDtGnTsG7dOrRv317udVUZF0zpKhFdXV3Y29vjv//9L7y8vLBz5074+fkBKM0eNWvWLMTFxYHjOKSnpyM+Pr5WP67jx4/j0aNHcHZ25s+1aNECSUlJlQb7oEGDEB8fX+9nkhce+j6ICL6+vvjxxx8rhYYCwOLFi7F48WLY2dlBS0sLtra2iI6OrpE8Z8+ehb6+PoqLi7F27VpMmDABFy5cqNnDNBJsXNSfHTt2QCAQYMiQIXKvq9K4YEpXyfj6+iIoKAiDBw9GbGwsP1e5dOlSdOzYEdevX4eGhgZ8fX1RWFhY6X51dXWZLbbfvn3LfyaqPh6+PIqwaAQCAZ49ewaJRMJbNSkpKe+NPnpfDL2mpia2bNnC1w8MDKzx1tr6+voASr+ngIAArFixQkY+VYWNi/oRHR2NP//8E5GRkfw5kUiEw4cPw8bGRqXGBZvTVTKjRo3C7du3sXLlSnh4ePCWXk5ODrp37w4NDQ2kpKTg6NGjcu83NjbGzZs3UVhYCKlUKrMJoaurK06dOoW4uDj+3F9//SW3nTKLRl6pyQ8LALp06QJbW1uEhIQAAE6dOgUigrW1NQAgKCgIS5YsqXSflpYWsrKy8PjxYzx+/Bj/+te/MGzYMMTExAAoVcpv3rwBUDoN8dNPP2H+/Pn8/aampnj69GmldnNzc5Gfn88fh4aGQigUqrzCBdi4qAlPnz6Fqamp3GthYWFIS0vjxxQA3Lx5EzY2Nio3Lpilq2Q0NDTg6emJjRs34vLly/z55cuXw9vbG/v378fHH3+MoUOHyr3fzs4Oo0aNglAohEAggJ2dHRISEgBUHw/fUGzbtg0+Pj5Yv3492rRpg7CwMP7V8vbt2+jVq1et20xOTsbEiROhpqYGNTU1bNq0iV8UysjIQFZWFp9Jqjypqanw8vLiE4sIBAIcPHiwXs+nLNi4AH9t2LBhePPmDQoLC9G9e3fMmzcP8+bNw9OnT6GuXnuVpWrjgkWkNSDNLfJoxYoVePXqVY39GR0cHHDs2DG0a9dOYTIcPHgQ9+7dw/Lly+t0/+TJk2FpaSnXv5VFpNUNZY2L9evXQ1dXF15eXnWQsnqUOS6YpcuoMW3btsWePXtw584dGZ/Mqjh//rzCZZgwYUKd73V0dMSTJ08wYMAABUrEUNa4WLhwYZ3uex/KHhfM0m1AmptF09xhli5DHiz3QjPF2dn5g0ynd/XqVUycOLFebVQM+WzONMVxMnXq1Bq7/JXn2bNnlQJnqsLNzQ337t2rdR+NAZteYDQ4xcXFVS6A2NjYVAqOUAbVycRQLOXzHJSnpKQEampqVd6np6eHc+fO1aiPqKioOsnWGDBLV8n89ddfcHJygkgkgkgkknHtKSM8PBx2dnawsrKCSCSSWWldvXo1TE1NIRaLIRKJkJiYCKlUin/+858wMzODWCyGlZUVsrKyFCKvmZkZYmNj+eOIiAg4OTkBAJ4/f46JEyfC1tYWIpEI8+fPh1QqBVBqkS1duhRDhw6Fk5NTldtyVwxXPXbsGGxtbSEWiyEWi3HmzBkAwPXr1zFgwACIRCLY2Njg1KlTcuWtrh7HcfjXv/6FQYMGYe7cuQr5fhqK5jROylvnkydPxuzZszFixAgIhUK8e/cO3377LYyMjGBra4tly5bBwMAAAPD48WOZiDmO4xAYGAg7OzsYGBjgP//5D3/NwMCAD+pIT0+Hh4cHhEIhxGIxvzj2xx9/oH///nxIdVBQkEKevdYocu8fVqrfCys7O5u6du1K0dHRRFS6u2lmZiYREQ0cOJAiIiKIqHRPMKlUSkRET58+5be2zs7OJi0tLX531cLCQiooKKDr16+TqakpFRcXExFRXl4eFRUVUUXu3r1b5ZbXo0aNqlSfiGjdunUy22mPHDmSQkJCiIjIxcWFTp8+TUSl+3WNGzeOtm/fzj+Pq6srvXv3joioym25y++7df/+ferSpQu/P5dEIqHs7Gx69+4dCQQCioyMJCKiuLg46tSpE2VkZBCVftGUk5NTo3orVqyQ+5xl16kRxkVFmts4KS+zj48PiUQiysvLIyKiyMhIMjc3p7y8PJJKpeTl5UX6+vpERPTo0SPS0tLi2wRA//rXv4iodAv4Nm3aUG5uLhER6evrU1xcHBERDRo0iFatWsXfV/b3z87O5p89NzeXevfuzW9DXx2KHheNrpiac6n444qMjCQHBweSR/mBeeXKFRo+fDh98sknJBaLqXXr1hQbG0slJSVka2tLY8aMoa1bt9KjR4+IiOjVq1dkbGxMn3/+Oe3cuZOeP38ut4+68Pz5c+rUqRMVFBRQeno6dezYkQoKCuj169ekpqYm84M0NjamBQsW8M+zZ88evp2qtuUur3SDgoLIy8urkgw3b97kf4hlODk50e+//05ExCvdmtQrv+FlRVRF6TancVJRZh8fHxmFGBAQIPOPMDo6ulqlm56ezh93796dEhISiOh/Sjc/P5/U1dX5f/blefDgAY0dO5bMzc1JLBZT+/btad++fe99NkWPCzappWTkbW9dkUmTJmHt2rXw8PAAAPTq1QuFhYVo0aIFLl68iNjYWJw9exZOTk7Ytm0b3NzccOvWLfz555/86/qxY8dkXtsB4N69e1UuWgkEArnRTl27dsWAAQNw6NAhpKenY/z48WjTpg1ev34NoDT/afntvMvTtm1b/rO9vb3cbbnr8v3Uh/IyqTLNZZzIo/zfgIhq9Tevbuv49zFjxgwMHz4cBw8eRIsWLTB48GC5IdUNjiI1OCvVWzTyXhtfvnxJRLLWQKdOnejy5ctERPT7778TAIqOjqa8vDx68uQJ356fnx+tXLmSMjIy+HaIiIYOHUq7du0iRREREUHOzs5kamrKb7Nd1s+yZcv4V9wXL15QcnJypechqnpb7orbepefXiguLuanF/T19SkqKoqIiG7cuEHa2tpypxdqUq8qoCKWbnMbJxUt3U2bNvHXfv/9dxIKhZSfn09SqZR8fHyqtXTL//3KTymU/zx48GC50wt9+vShAwcOEBHRtWvXqGXLlhQcHPze51L0uGALaUqkY8eO+O2337B8+XKIRCKIxWK5zuRbtmyBh4cHBgwYgGPHjsHCwgJAaW6BsWPHwsLCAmKxGC9evMDMmTORlpaGYcOGQSQSQSgUQldXt15BBBUZOXIk7ty5gxYtWsDOzo4/HxYWhpSUFAiFQgiFQnz66adIT0+X20ZMTAysra0hFotha2srd1tuIyMjhISEwMfHByKRCNbW1oiPj0fLli1x+PBhrF69GmKxGFOmTMHevXvRpUsXmftrWk/VaW7j5H33uLu7w9LSEs7OzujRo0eldJO1JTQ0FPHx8TA3N4dYLMb3338PoDRx0rJly2Bvb48NGzbwC33KhgVHNCDMCb5pwYIjGof8/Hy0a9cORIR58+bh7du3+OmnnxpbLB4WBsxgMJoVU6ZMQVJSEt69ewdzc/MaZzNrqjBLtwFhFk3Tglm6DHmwMGAGg8FowjCly2AwGEqEzek2IK1bt37BcVzXxpaDUTNat279Qln9sHHRdFD0uGBzugwGg6FE2PQCg8FgKBGmdBkMBkOJMKXLYDAYSoQpXQaDwVAiTOkyGAyGEmFKl8FgMJQIU7oMBoOhRJjSZTAYDCXClC6DwWAoEaZ0GQwGQ4kwpctgMBhKhCldBoPBUCJM6TIYDIYSYUqXwWAwlAhTugwGg6FE/j/3/406SocjZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dot형식으로 내보낼 필요없이 plot_tree를 사용해 그릴것\n",
    "#gini는 불순도 -> 한 노드에 한 클래스만 존재할 시 0\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "tree.plot_tree(tree_clf, class_names=iris.target_names)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c391e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.90740741, 0.09259259]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict_proba([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c715f3eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6ee9bbf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b75bd961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(133.92000000000002, 181.2, 'X[0] <= 2.45\\nmse = 0.667\\nsamples = 150\\nvalue = 1.0'),\n",
       " Text(66.96000000000001, 108.72, 'mse = 0.0\\nsamples = 50\\nvalue = 0.0'),\n",
       " Text(200.88000000000002, 108.72, 'X[1] <= 1.75\\nmse = 0.25\\nsamples = 100\\nvalue = 1.5'),\n",
       " Text(133.92000000000002, 36.23999999999998, 'mse = 0.084\\nsamples = 54\\nvalue = 1.093'),\n",
       " Text(267.84000000000003, 36.23999999999998, 'mse = 0.021\\nsamples = 46\\nvalue = 1.978')]"
      ]
     },
     "execution_count": 43,
     "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": [
    "tree.plot_tree(tree_reg, class_names=iris.target_names) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5d2c513c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(min_samples_leaf=10)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_reg = DecisionTreeRegressor(min_samples_leaf=10)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ec1f8f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(148.8, 195.696, 'X[0] <= 2.45\\nmse = 0.667\\nsamples = 150\\nvalue = 1.0'),\n",
       " Text(111.60000000000001, 152.208, 'mse = 0.0\\nsamples = 50\\nvalue = 0.0'),\n",
       " Text(186.0, 152.208, 'X[1] <= 1.75\\nmse = 0.25\\nsamples = 100\\nvalue = 1.5'),\n",
       " Text(111.60000000000001, 108.72, 'X[0] <= 4.65\\nmse = 0.084\\nsamples = 54\\nvalue = 1.093'),\n",
       " Text(74.4, 65.232, 'X[0] <= 4.45\\nmse = 0.024\\nsamples = 40\\nvalue = 1.025'),\n",
       " Text(37.2, 21.744, 'mse = 0.0\\nsamples = 29\\nvalue = 1.0'),\n",
       " Text(111.60000000000001, 21.744, 'mse = 0.083\\nsamples = 11\\nvalue = 1.091'),\n",
       " Text(148.8, 65.232, 'mse = 0.204\\nsamples = 14\\nvalue = 1.286'),\n",
       " Text(260.40000000000003, 108.72, 'X[1] <= 1.85\\nmse = 0.021\\nsamples = 46\\nvalue = 1.978'),\n",
       " Text(223.20000000000002, 65.232, 'mse = 0.076\\nsamples = 12\\nvalue = 1.917'),\n",
       " Text(297.6, 65.232, 'mse = 0.0\\nsamples = 34\\nvalue = 2.0')]"
      ]
     },
     "execution_count": 48,
     "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": [
    "tree.plot_tree(tree_reg, class_names=iris.target_names) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2bea2b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}