decision_trees.ipynb

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   "id": "infectious-concentration",
   "metadata": {},
   "source": [
    "# Decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "searching-eagle",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355138b1",
   "metadata": {},
   "source": [
    "## Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "senior-rachel",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([\n",
    "    [1, 0], \n",
    "    [-1, 0], \n",
    "    [0, 1], \n",
    "    [0, -1]\n",
    "])\n",
    "y = np.array([1, 1, -1, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "supported-horse",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = tree.DecisionTreeClassifier()\n",
    "dtree = dtree.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "activated-mexican",
   "metadata": {},
   "outputs": [
    {
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       "array([[0., 1.]])"
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     "execution_count": 15,
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   ],
   "source": [
    "dtree.predict_proba([[0, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bright-sweden",
   "metadata": {},
   "outputs": [
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    "import graphviz \n",
    "dot_data = tree.export_graphviz(dtree, out_file=None, \n",
    "                                filled=True, rounded=True,  \n",
    "                                special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph "
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