Code-Example-019-1-Decision_trees.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Trees**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "import graphviz\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"decision_trees\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training and Visualizing a Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data[:, 2:] # petal length and width\n",
    "y = iris.target\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This code example generates Figure 6–1. Iris Decision Tree:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<graphviz.files.Source at 0x7f9001c47490>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphviz import Source\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(\n",
    "        tree_clf,\n",
    "        out_file=os.path.join(IMAGES_PATH, \"iris_tree.dot\"),\n",
    "        feature_names=iris.feature_names[2:],\n",
    "        class_names=iris.target_names,\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )\n",
    "\n",
    "Source.from_file(os.path.join(IMAGES_PATH, \"iris_tree.dot\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 6–2. Decision Tree decision boundaries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_decision_boundaries_plot\n"
     ]
    },
    {
     "data": {
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WvBvvTHmn+tjnnniOfXfYl5222onzTj6PFctXpBT3SWefxKH9D2WrFltt1P3ni0RlxXV7b6rU14uT6Jy5LF/vK58kO2j4QGCzGNsbAZntQxaRnNH/5P6UflfK1198zeXnXM5Xn3/Fs28/y4yFM9j1V7ty9lFns2bNL0P4nnjgCX43+Hd88r9PuOCKCxh0/CC+nfstvffqzc1/v5n2ndvz+bLP+XzZ5+x14F4AVFZW8sZLb/DiBy8y5fMpzPpwFg/e9WD1Oc/qfxY9t+4Z9/XsY88G/r1ki5cmvMRLE16Kuz9RWfH8n2KP25n307yE18zXUuV8va98krBM3Mx2r/FxFzP7X43PRUA/4LtMBCYiuadNuzYALClbwvix43lnzju0LG4JwCXXXsIDdzzAB+9+QJ99+wDQ9+i+7H/o/gAce/qx/Ou+fzH+8fFccGW8OUAjrhh+BU2aNqFJ0yb0O7ofM6f/MvbnoQkPZeDO8sPAYwYC8QcZZ6KsOF9LlfP1vvJJfT0404H3iMxc/N/o56rXu8CVwA2ZDFBEcseiBYsAsE0MgH679qvuOdmlxS6sWbOGRd8uqm6/Xcftah2/XYftqs8RT1FREdvUWJalcZPGrFiR2iOqfNa7bVvaF21H+7Y/0P6qnWnf5kfgSDbb7IjqNrNKZ7HTbTvVO46mprLlZZw45sRAxpokii/VOII+LqjzyfrqS3A6AV0AA/pEP1e9tgWau/sDGY1QRHLGxCcn0nrb1nTu2hmA1754jU/+90n1a/aK2Rx96tHV7b+dW3v+0G/nfVvdC1SVJG2oMw4/o3rcTqzXM48+k+Ld5Zbvy4oib44bAA2XwvGnARNYvXpidZtU5rQJct6XRPGlGkfQxwV1PllfwgTH3ee5+1x338Tdp0c/V70WuXtlUIGKSPZa+O1C/jr0rzw15imG/m0oLYtbcsypx3DVH6+i9LtSAJb+tJQXnnmBn1f8XH3cf8f/lzcnv0llZSXjHx/PzPdm0v/k/gC0at2KHxb/wPJlG7YE3cOTHq4etxPrdezpx1a3XbNmDeXl5VSurcTXOeXl5VRUVKThG8kSxR9Cq1mR/6K2mgWtfnmUV7MqKplqKPilcsjdM14xlCi+VOMI+rigziexxU1wzOyMZF9BBiwi2eHOG++k+xbd6bFlD0488ETmfTWPZ958hsOPPxyAW0fdSpcdu3DSQSfRfYvu9O3Vl+f//TyRJekiTv7dyfzzb/9kp6124o4b7+C+f99Hh84dANj7oL3Z75D92KfLPvTcuidTX5ua9nsYMmgIXZt05a6b7+LtV9+ma5Ou/Lr7r9N+ndAcN6D25+N/KYdPZU6bIOd9SRRfqnEEfVxQ55PYEs1kXPe/TZsBmwLrop83ITKjcYW7NycLaCZjyTaayTi+kw46iX0P3pfBV23YlP+SnPZtf4A/7BbpvakyNPLLf777T60Ziaskmpm45qy9VTI1e2/dGZNrxrfN5tukFEeq8af7voP8HgtFvJmMcfd6X8ARRAYb70Ok8qpB9P27wJHJnCOIV+/eu3hl5SK99Mqa19q1nzqRQfpxX8PvHe7zK+f7/Mr5Pvze4QnbVrWbXznfe+7eM267U39/anW7idMmJjznxGkTq9ue+vtT47bruXvPWtfXPWX3PXHeTs51iX/2OBJnaPR1ZOK2AyYM8M2GbRZp2yZ+u0zcE20iMXa9u6sPmDAgbb9Pmw3bzH874bf1/j5tUrLJL9/ToMTf04beE0MjceTTz14If56mx8oJkl1N/Hbgd+7+To1tb5nZxURW+Z4Y8ygREQnH1l/X7r3ZSLHmfQnavJ/m0aAo2X+26pfsvDXr1q2rt83GxiHpF/cRVa1GZquAX7n7zDrbewFT3b1xhuLbIHpEJdmoqChSFfTNN//jm4oVrNp8CU2ahByUZKUffv6eYS9eybX9bmHrJsE+ypz94adcdsL2LP/pV+vt26rlGp7+7JNA4xFJ1kEtdo/5iCrZVPhd4E4zO93dvwMws22BvwHpH/knkqc6N2zK96Xb1d9QCtLdn5zPxws/5PHJz3L5TvcEeu0/HFI1r+v6/+n98ftNafL1boHGI7Kxkk1wBgLPAnPNrGrm4m2BL4BjMhGYSL7p0KGCefMa0krjCCWGxSsX8fzCB3HW8fx3DzJkz2to2bh1YNfv2XN3PknQSaOfW8k1Sa1F5e5fA7sQGWw8gkjPzeHAzu7+VebCExEpDHfNHMY6j4z1qPRK7vxoWKDXnzBhBqB1lCR/JLvYJh7xX3e/093vcPeXPJkBPCIiktDilYt46qsHWbMuMth0zbrV/PurB/l+VWnIkYnkrriPqMzsUuDv7l4efR+Xu49Ie2QiIgWiZu9NlapenGF7BjsWRyRfJBqDcyEwBiiPvo/HiTy2EhGRFLy/+J3q3psqa9at5v3FbwcWw69+1ZZNNoF16xaut6+F5qaUHBQ3wXH3TrHei4hIej1/1Adhh8DixZFV3OfMCTkQkTRJqorKzIq0sKaISGbssQcsWbL+9hYt4L330n9cLFOnfpdwfzqvFabFKxdx4euncPcBTwRapSbBS3aQ8VIze9HMrjSzvcysKKNRiYgUkFiJQ6LtG3tcLMXFbSkubhvItcJ018xhvFf2ZuBVahK8ZBOcY4msRXUEMAX4qWbCk6ngRERE0qWqWs1Zpyq1ApDsPDgvufvV7r4vsCWRhGchcAPwZgbjExGRAFx55SCuvHJQ2GFkVNhzDUmwkp4Hx8yKzexkIhVT9wCnAG8RSXJERCSHjR17P2PH3h92GBmjuYYKT7KDjGcBHYFpRB5RnQu84+4VGYtMREQCc9NN94UdQkZprqHCk+xaVFsAlcBK4GdgOaD13UVE0qBFi/gVSpk4LpbTTkv8eCqd1wpDNsw1JMFKKsFx93Zmtj1wYPR1EdDUzN4AXnX3vyVzHjO7ADgL2Bl43N3PitPuLGA0sKrG5iPdfUoy1xERyaR0l0wnqlBKdK1Ude4MsRbaMYNvvoldSl11X7laZp0Ncw1JsDZkLaqv3P2fwJnASURWFz8MuH0DrrcQuBF4IIm277h70xqvKRtwHRGRjAmyZDrRtVKNI/YqghNwnwAkLqVWmbXkiqQSHDPbw8wuN7P/AD8SGYfTHfgrkVXFk+Lu49z9WeCHFGIVEZGMOQo4KmEptcqsJZck24PzFpHS8I+I9N5s7e57uvsV7v5ihmLbzcyWmNlsM7vGzGI+TjOzQWY23cymf/+98iYRkdQcCRyZsJRaZdaSS5JNcLZy972iCc0L7v5zRqOC14GeQCvgeOBU4LJYDd19lLuXuHtJy5bbZDgsEZF8NQGajopbSq0ya8k1yU70l+mEpu71vnH3Oe6+zt0/JjLXzglBxiAiUnAOiF9KnajMWiQbJVsmHjYHLOwgREQg2JLp+q6VShxmcQYat0tcSq0ya8klgSY40XE0DYAioMjMGgFr3X1tnXaHAe+7e5mZdQOuAZ4KMlYRkXjSvXr2nDnpPV99vvlm/W2dOhncB3PmxCyxEsk5QffgXA1cV+PzAOB6M3sA+BTo4e7zgYOBh8ysKVAGPALcHHCsIpIn0j1vTarqm38mlkSx//BD/PNts0123HOm5Op8PBKcpOfBSQd3H+ruVuc11N3nR+e6mR9t92d3L3b3Ju7e2d2vdfc1QcYqIvkjyHlrEok9/0z87ZA49kTn29B7njPHc6r3RvPxSH3i9uCY2aXJnsTdR6QnHBERkcTqzsczuNc16sWR9SR6RHVhkudwIiuMi4iIZFys+Xi0YKbUFTfBcfdOQQYiIiLhGTiwPwCjR08IOZLE4s3Ho14cqSvQMTgiIpKdXnllIq+8MjHsMOql+XgkWUlXUZnZ1sBvgPbAZjX3ufsNaY5LRCRtgpy3JpF4889Yglm+EsWeahVVLPff/1z8ILLI+4sTz9UjUiWpBMfM9gSeByqAlsB3QJvo57lEZhoWEdlomSjpjndcp06RVyz1JSOp7IuXdFTFUld9CVi80vJUHHJI//SdLIOeP+qDsEOQHJHsI6q/AI8C2wLlwEFEenKmA7dmJjQRKUS5UNKd6r549xDvmCVLsuf7EMk1ySY4uwB3u7sDlUBDdy8DhgBDMxSbiIgE5LHHRvHYY6PCDkMkbZIdg1PzgWcZ0AH4DFgBtE13UCIiEqyrrjoXgNNOGxRyJCLpkWyC8z6wBzAbmALcaGbFRJZamJmZ0EREJCinnHJO2CGIpFWyCc5VQLPo+6uBh4G7iCQ8v8tAXCIiEqDhw/V4SvJLUgmOu0+v8f574LCMRSQiBS1XSrrTWUUV71pV95wN34dIrkm2TPwV4Dh3/6nO9ubAs+5+UCaCE5HCk4mVruOVnidKLBLFEe9822xT/3Gx1HfchsaRSkl9WdlCAIqLNaxS8kOyj6gOpM7kflGNgP3SFo2ISAakUp6dyvkydVwQ59tzz20BcmpFcZFEEiY4ZrZ7jY+7mNn/anwuAvoRmfRPRERyWKtWbcIOQSSt6uvBmU5ktXAH/htj/yqSX3VcRESy1LvvLgw7BJG0qi/B6QQY8A3QB/i+xr7VwGJ3r8xQbCIiIiIpSZjguPu86FutOi4iIiI5I+nExcwOM7OJZvapmW0X3fZ7Mzs4c+GJiGy8eCXV8Vbxrq8EO97+TB0XxPn69+9N//69UwtEJAslWyZ+OvAP4J/AwcCm0V1FwOXA5IxEJznn+utv54Yb/gqAmbHFFs3ZfvuOHHroAVxwwUBat26V9mvOnv01jz/+DBdddA5bbrlF9faHHnqCgQMvZunSr2jatElar+nu3HLLnfzjHw+zZMn/2GOPXowceSO77tozrdeR9Eh36Xm6VzUPOo5YPvnk/fSdTCQLJFsmfjlwjruPNbPf19g+Fbgh/WFJLttii+ZMmvQYAEuXLuODDz7mH/8Yw/33P8KkSY/Ru3evtF5v9uxvuOGGv3LmmSfXSnAy6dZb7+LGG0dy223XsOOO2/O3v91H374nMXPmlIwkcemSznlTsk2ie4P8ve90ee656fU3EskhySY4OwDvxNi+AmievnAkHzRoUMSee/7S1d2v36/5wx/O5MADj+XUU//AZ5+9SVFRUYgRbpzy8nJuvfVurrjiQv74x8hKJXvtVULnzntwzz0PMGzYFSFHGF+652HJJqncWz7cd7rsvLMeT0l+SXYMzkKga4zt+wNfpy8cyVdbbrkFt9xyNV9/PZeXXnoNiCQKQ4YMo0OH3jRu3IHddjuYSZNqP+3s3HkPLrvsem68cQRt2+5C8+ZdGDDgfJYuXQbAlClvc/TRZwDQpUsfiora0Llz7eli58yZT9++J9OsWWd69NiXceOe36h7efvt6SxbtpwTT+xfva1Jk8058si+vPDCKxt1bhERSY9kE5xRwJ1mtk/083ZmdiZwG3BvRiKTvPPrX+9DgwYNePfdyLP+E088hzFjnuCKKwYzfvwYSkp25ZhjzuTDDz+pddzYsc8yefIb3Hff7dx++1AmTZrMOef8CYDdd9+Zv/zlOgD+/e/RvPXWRJ5++oFaxw8YcD5HHdWXp59+gO2378xpp53HggW/zPmxbt061q5dm/BVWfnLbAhffPEVRUVF7LBD51rX6d59Bz7//Kv0fWEiARo5cigjRw4NOwyRtEl2sc3bzGwL4CUiyzO8ClQAt7v7PRmMT/JIw4YNadFia8rKvmfy5DeYNOllXnnlaQ44YG8A+vY9kC+//Jqbb76DJ5+8v/q4VavKmTDhkeqBwk2abM6ZZ17IZ5/Npnv3rnTt2gWA3XbbmY4dt1vvuhddNIjf/e5UAHr33oU2bXZh4sSX+MMfzgRg2LAR1QOj4+nQoR3ffBMZrPHjjz/RtGmT9R6zbbnlFqxcuYrVq1ez2WaxVjYRyV533HE9ABdfPDTcQETSJNkxOLj7VWZ2E9CDSM/Pp+6+ImORSV7y6OI/kye/TuvWrdhnnz6sXbu2ev9BB+3HmDFP1DrmkEP2r1UFddxxh3PGGRfw3nsf0r17rCentfXte0D1+2222ZpWrVrw3XeLqredc84AjjjikITnaNiwYa3PFqO+uOreYu0TyXYXXXRd2CGIpFV9a1FtDvwFOIZIafjLwGB319A82WDl5eX88MOPFBe35LvvFukkh58AABpZSURBVFFaupiGDdfvcanbM9KqVe1JPRo3bkzTpk0oLV2c1HXrVlZtttmmlJdXVH9u3brVeteoq2bSstVWW7J8+QoqKytrxbp06TI237wxm266aaxTZIUWLRJXGuWy+u4tX+87XdRzI/mmvh6c64GzgEeBcuBUImNuTkzlYmZ2QfR8OwOPu/tZCdpeAgwBGgNPA+e5e0W89pL9Xn31LdauXcuee/bm1VffYttt2zBu3AP1Hrd4ce1/mVatWsWKFT+nrRx7Qx9R7bjj9lRWVvLVV3PYccftq9t8/vlXdOu2fbxTZIVMlER37hx7VW4z+Oab9B0D6S9z32MP6NQp9vlApeUiuay+BOc4YKC7jwUws0eAt8ysKMU1qBYCNxJZhbxxvEZm1g+4AjgoeswzRJKt7K2/lYR++mkpV155E9tv34lDDtkfM2PEiH/QtGkTunXbIeGxL7/8OitW/Fz9mGrcuEmYGSUlkfl0Ntss0mNSXl6eUmwb+ohq771LaN68Gf/+9wSuuuoSAFauXMnEif/lnHMGpBRDLouVqCTanuoxkP4yd5WW/+Ljj2cAKheX/FFfgrMd8EbVB3efZmZrgbbAtxt6MXcfB2BmJUC7BE3PBEa7+6xo+2FEepGU4OSAtWsrmTo18pfl8uUreP/9mfzjH2NYuXIVkyY9RlFREYceegB9+x5Iv36ncPnlf6RHjx1Ztmw5H300i/Lycm6++arq8zVu3Ij+/Qfwpz+dz6JFZQwZMoxjjjmMHj12BGDHHSODjEeN+hcnn3wMm2/emJ137p50vG3btqZt29ZJt2/UqBFDhlzAjTf+ja222rJ6or9169ZxwQUDkz6PSDY56qgSAObMqSfLFMkR9SU4RURWDa9pbRLHbaydgPE1Pn8EFJvZNu7+Q82GZjYIGATQvv22GQ5LkrF06TL22edIzIzmzZux/fYdOf3042st1WBmPP30aIYPv5M77rif+fO/Y+utt6RXr53WSxJOPvlomjVryjnnXMqKFT/Tv38//v73W6r3d+iwHX/5y3Xcddc/ufvuB2jXrk3146RMGTLkQtatW8ctt9zFDz/8SEnJLrz44hMUF7fM6HVFMqVnz93DDkEkrcwT9Amb2ToipeE1x74cBrwGrKza4O5HbdBFzW4E2sUbg2NmXwN/dPcXop83JZJodXL3ufHOW1LSy6dNe3FDQpEs17nzHhx//JHVc93koqKiNgBUVi5i3ryGmG0VckTpFWsMS5U5c9J3zMYcl8r5EknlWiKSGZ062Qx3L6m7vb6emDExtj2SnpASqrsERNX75QFcW0RERHJcwgTH3c8OKpA6ZgG9gCejn3sBZXUfT4lI+MziV0Sl8xhIf5m7SstF8lemx9LUYmYNotcsAorMrBGw1t3X1mn6MPCQmT0KLAKuBh4KMlbJDpkeSyMbL1FZdzqPgfSXZ6vc+xe/+lVbAN59d2E9LUVyQ7JrUaXL1cAqItVQA6Lvrzaz9ma2wszaA0TH3txGZEmIedFX7g7CEBHJcosXL2Lx4kX1NxTJEYH24Lj7UGBonN1N67QdAYzIcEgiIgJMnfpd2CGIpFWgCY6IiGSn4uK2YYcgklZBP6ISERERyTglOCIiwpVXDuLKKweFHYZI2ijBERERxo69n7Fj7w87DJG00RgcERHhppvuCzsEkbRSgiMiIpx2mh5PSX7RIyoRERHJO0pwRESEl1+ewMsvTwg7DJG00SMqERHhnHOOAmDOnBiLhInkICU4IiLCQQcdGXYIImmlBEdERBg9Wo+nJL9oDI6IiIjkHSU4IiIikneU4IiICJ06GZ06WdhhiKSNEhwRERHJOxpkLCIiKg+XvKMeHBEREck7SnBEREQk7yjBERERBg7sz8CB/cMOQyRtNAZHRER45ZWJYYcgklZKcCQUZWXjmDt3OBUV39Gw4bZ07HglxcXHhR2WSMG6//7nwg5BJK2U4EjgysrG8eWXf2bdulUAVFQs4Msv/wygJEckJIccosdTkl80BkcCN3fu8Orkpsq6dauYO3d4SBGJiEi+UYIjgauo+G6DtouE6Q9/OI6PPnoPgJEjh1JS0oojjtiNX/+6K0cfvQcPPngnlZWVG3WNBQvm8thjo2pt23ffjnzxxScpn/POO4fRt+9OHHZYL/r3781rr71YvW/48Mt57rnHa7V/7LFR68UgksuU4EjgGjbcdoO2i4Tlgw/eZeXKn+nVa4/qbcceewbPP/8Br746m7vueoKJE8cybNglG3WdBQvmMnZsepOLXr36MH78e/znPx9x660PcOGFJ1NeHuk5HTToMkaOHMq6deuq21911blcddW5aY1BJExKcCRwHTteySabNK61bZNNGtOx45UhRSQS29ixozj66NPi7m/fvjO33fYAjz56L8uWLQXg1VcnccIJ+9C/f2+OO24vPvhgKgBTp07hsMN6cdllZ3Pkkbtz9NF9+PLLTwG49to/8uWXn3L44bty3nknVJ//+eef5Ljj9mLffTsyZszdGxT7AQf0o3HjzQHo3n0XwPnxxx8A2GablrRv35m33ppc3f6UU87hlFPO2aBriGQzDTKWwFUNJFYVlWS7qVOnMGjQZQnbdOnSjcaNN+ebb75gq6224a67hjFmzIs0a9ac2bNncfbZh/HWW/MB+PzzmVx33Z3suecBPP30GP70pzN47rnp3HDDPdx885957rnptc69atVKxo17hwUL5tKvX09OOOEsmjRpytChg5k27fWY8dx779N06NCl1rZx4x6mffsutGnTrnrb7rvvxdtvT2a//Q4FYPhwPZ6S/BJogmNmWwOjgb7AEuBKd38sRruzou1qjkQ90t2nBBCmBKC4+DglNJL1SksX0KJFcdLtX3/9RebP/5qTT96/etvatWv5/vsyADp23J499zwAgGOP/S3/93+DWL58Wdzz9e9/CgDt2nVkiy22orR0AV26dGPo0DuTjmnq1NcYMeIaHn74pVrbW7RoHTdJEskHQffg3AOsBoqBXYHnzewjd58Vo+077r5voNFJVtAcOZItGjZsTEVFecI2X3/9BatWraRLl27MnPke++//G0aMeDhGu89SuH6j6vebbFLE2rVrAZLuwXn//Xe49NIBjBo1ni5ddqzVrqKinEaNfnlUXFa2EIDi4rYbHKdINgoswTGzJsDxQE93XwG8aWbPAb8FrggqDslumiNHssmOO+7MN998QatWbWLuX7BgLldcMZDTTz+PZs2as99+fbnzzuuZPXsWXbvuBMBHH71XPUh57tyvmDbtDfr02Y/x4x9jxx13plmz5jRt2pzly5cmHVcyPTgfffQeF154Mvfc82969tx9vf1ff/0Z3bv3qv68556RQf5aVVzyRZA9OF2BSnefXWPbR8ABcdrvZmZLgP8B/wKGu/vaDMcoIUs0R44SHAnab35zHK+//iJ77nlg9bZnnnmYt9+ezKpVK2nWrDlHH306Z555IQCdOu3AiBGPMGTIQMrLV7FmzWp6996nOsHp0WNXJkx4nGHDLmaTTYr4618jPT3duu1C58470q9fTzp37sa99/57o2O/9trzKS9fVasyasSIf9Gt2864O2+9NZnzz/+/6n3xkjiRXGXuwWTrZrYf8JS7t66x7RzgdHc/sE7bzoAD84CdgCeAf7n7ejPBmdkgYBBA+/bb9p4zZ3rdJpJDXn+9LZHf+rqM/fdfGHQ4aVFUFPmHo7JyEfPmNcRsq5AjkmQtX76ME0/cl2effbfW45xUTJ06JeZA4jC89tqLPPvsI/ztb/8KOxSRjdapk81w95K624MsE18BNK+zrTmwvG5Dd//G3ee4+zp3/xi4ATihbrto21HuXuLuJS1bbpP2oCVYmiNHskmzZs256qq/8u23c8IOJa1WrFjGFVfcGnYYIhkVZIIzG2hgZjvU2NYLiDXAuC4HLCNRSVbRHDmSbfbb71B22KHHRp9nzz0PzIreG4AjjjhRg4kl7wWW4Lj7z8A44AYza2Jm+wBHExlfU4uZHWZmxdH33YBrgPFBxSrhKS4+jh12uJ2GDdsBRsOG7dhhh9s1/kYkw/r3703//r3DDkMkbYIuEz8feABYDPwAnOfus8ysPfAp0MPd5wMHAw+ZWVOgDHgEuDngWKWGVEq3P/zwJJYte6P6c/Pm+7Hrrk+mfL5MxCgiEZ988n7YIYikVaAJjrv/Dzgmxvb5QNMan/8M/DnA0CSBVEq36yY3AMuWvcGHH55EmzanxD0fkFKZuMrLRTZOtjw+E0kXLdUg9UqldLtuclNze0XFnLjnq3q/IddKNUYR+cXOO+vxlOQXJThSr4qK7zZoeybOV9+10h2jiIjkNq0mLvVKd+l2ovOlei2Vl4tsnJEjhzJy5NCwwxBJGyU4Uq9USrebN98v7vZE50u1TFzl5SIb5447rueOO64POwyRtNEjKqlX1RiWDalQ2nXXJxNWUdV3vg2thkolRhH5xUUXXRd2CCJppQRHkrJ06TQqKhYBTkXFIpYunUZx8XEJk5g2bU6homJOdcLRps0p1e2Ki49Le/KRiXOKFIqLLx4adggiaaUER+o1e/YVlJaOqbGlktLSMSxZ8iJr15bWaptMKbjKvUVEJNM0BkfqVVr6SMztdZObKsuWvZGwbDuRVI8TkY3z8ccz+PjjGWGHIZI26sGRJFRu8BGplm2r3FskHEcdFVmMec4cDzkSkfRQgiNJKGJDk5yGDbelomJBzO2ZOE5ENk7PnruHHYJIWukRldSrdesBMbc3aNA65vb6SsETUbm3SDgmTJjBhAl6RCX5QwmO1Ktr11to3fpMIj05AEW0bn0me+/9wXrz3VRVUaW6KrhWExcRkXQw9/x53lpS0sunTXsx7DBEaikqagNAZeUi5s1riNlWIUckIpI/OnWyGe5eUne7xuDkobKycSlNeBcpB3+EyHibIlq3HkDXrrcAMG3agZSXf1HdtlGjHenTZwqvv74dsLbGWRqw//7fAvD6652A8hr7GrH//nMAePvt3WpVYTVo0Jq99/5go+JP9TgRgV/9qi0A7767MORIRNJDPTh5pu48MhAZw1LfY57157qJaN36TH76aWqt5KZ+DaKv8hj7GtGgwZYxS8wbNGhNly7XpBR/qvcdBPXgSC7o1MkAVVFJ7onXg6MxOHkm1Xlk4s11U1r6yAYmNxDp0YmV3ACUx50/Z+3aUs2fIxKSqVO/Y+pUTccg+UOPqPJM6vPIxCsD3/A5cDaG5s8RCUdxcduwQxBJK/Xg5Jl488XUP49M0QZuz4xU40/9vkVEJB8pwckzqc4jE2+um9atB9Co0Y4bGEUDoFGcfY3izp/ToEFrzZ8jEpIrrxzElVcOCjsMkbRRgpNnUp1HJt5cN1273kKfPlPWS3IaNdqR/fdfxPpPOSNVVJFqqbpJTqSKau+9P1gvyamqotL8OSLhGDv2fsaOvT/sMETSRlVUBSbdpdSJSssT7SskqqKSXPDYY6MAOO009eJIbtE8OLJeKXVFxQK+/PLPACklOeuXllfW+hxvXyEmOSLZTomN5Bs9oiog6S6lTlRanmifiIhIpqkHp4Ckv5Q6ldLyYMvORSQ5L788AYBDDukfciQi6aEEp4A0bLgtFRULYm5PTRGxE5aqgcqJ9olINjnnnKMAzWQs+UOPqApIukupE5WWJ9onItnnoIOO5KCDjgw7DJG0UQ9OAakaSJyuKqqqwcKJKqVURSWSG0aPnhB2CCJppTJxkQxTmbiISOZkxWKbZra1mT1jZj+b2TwzOy1B20vMrNTMlprZA2bWMMhYRUREJHcFPQbnHmA1UAycDtxrZjvVbWRm/YArgIOBjkBn4PrgwhQRKSydOhmdOlnYYYikTWAJjpk1AY4HrnH3Fe7+JvAc8NsYzc8ERrv7LHf/ERgGnBVUrCIiIpLbghxk3BWodPfZNbZ9BBwQo+1OwPg67YrNbBt3/6FmQzMbBFRNwVlRVNTmkzTGnOtaAEvCDiKLhPp9VI3FySL6+ahN3wfU7MXR97E+fSe1Zcv30SHWxiATnKbA0jrblgLNkmhb9b4ZUCvBcfdRwCgAM5sea6BRodL3UZu+j9r0fdSm76M2fR/r03dSW7Z/H0GOwVkBNK+zrTmwPIm2Ve9jtRURERGpJcgEZzbQwMx2qLGtFzArRttZ0X0125XVfTwlIiIiEktgCY67/wyMA24wsyZmtg9wNPCvGM0fBgaaWQ+LTBpyNfBQEpcZla5484S+j9r0fdSm76M2fR+16ftYn76T2rL6+wh0oj8z2xp4ADiUyFiaK9z9MTNrD3wK9HD3+dG2lwJDgMbA08Af3L0isGBFREQkZ+XVTMYiIiIioMU2RUREJA8pwREREZG8kxcJzoascVUIzOwCM5tuZhVm9lDY8YTJzBqa2ejoz8VyM/vAzA4LO64wmdkjZrbIzJaZ2Wwz+33YMWUDM9vBzMrN7JGwYwmbmU2Jfhcroq8vwo4pbGZ2ipl9Fv135msz2y/smMJQ42ei6lVpZneFHVcsQU70l0k117jaFXjezD5y91gl6IVgIXAj0I/IIO1C1gD4lsiM2fOBw4EnzWxnd58bZmAhGg4MdPcKM+sGTDGzD9x9RtiBhewe4L2wg8giF7j7P8MOIhuY2aHArcDJwDQg66YlD4q7N616H12CqQx4KryI4sv5HpwNXOOqILj7OHd/ljqzPhcid//Z3Ye6+1x3X+fuE4E5QO+wYwtLdI23qopEj766hBhS6MzsFOAnYHLYsUhWuh64wd2nRv8e+c7dvws7qCxwArAYeCPsQGLJ+QSH+GtcrbdKuYiZFRP5mSnU3j0AzOzvZrYS+BxYBEwKOaTQmFlz4AbgT2HHkmWGm9kSM3vLzA4MO5iwmFkRUAK0NLOvzGyBmd1tZoXeOw6RhbEf9iwtx86HBGdD1riSAmZmmwKPAmPc/fOw4wmTu59P5M/IfkQm4CzkOaaGAaPd/duwA8kiQ4DOwLZEJnObYGaF2stXDGxKpLdiPyLDIHYjMgFtwYrOX3cAMCbsWOLJhwRnQ9a4kgJlZpsQmTV7NXBByOFkBXevjD7SbQecF3Y8YTCzXYFDgL+FHUs2cfd33X25u1e4+xjgLSLj1wrRquivd7n7IndfAoygcL+PKmcAb7r7nLADiScfBhlXr3Hl7l9Gt8Vb40oKkJkZMJrI/8QOd/c1IYeUbRpQuGNwDgQ6AvMjPyY0BYrMrIe77x5iXNnGAQs7iDC4+49mtoDIdyC/OAO4JewgEsn5HpwNXOOqIJhZAzNrBBQR+cu6kZnlQzKbqnuB7kB/d19VX+N8ZmatouWuTc2syMz6AacCr4QdW0hGEUnudo2+/gE8T6QCsSCZ2ZZm1q/q7w0zOx3YH3gx7NhC9CBwYfTPz1bAxcDEkGMKjZntTeTxZVZWT1XJl3/0zieyxtViIpVD5xVwiThEng1fV+PzACJVAENDiSZEZtYBOJfIGJPS6P/SAc5190dDCyw8TuRx1D+I/AdnHnCxu48PNaqQuPtKYGXVZzNbAZS7+/fhRRW6TYlMM9ENqCQyEP0Ydy/kuXCGAS2IPDEoB54Ebgo1onCdCYxz96weCqK1qERERCTv5PwjKhEREZG6lOCIiIhI3lGCIyIiInlHCY6IiIjkHSU4IiIikneU4IiIiEjeUYIjIlnLzOaa2Z8T7D8rOndNVjCzh8ysYCeAE8kmSnBEJKHoP9oefa0xs2/M7HYza5Lk8R2jx5ZkOtag5OM9ieSbfJnJWEQy62Xgt0Rmud0P+CfQhAJdpFNEsp96cEQkGRXuXuru37r7Y8CjwDEQWczUzC43s6/NbJWZfWxmA2ocW7Xa8HvRXo8p0eP2MLP/mtkSM1tmZm+a2V4bG6iZ9TezGWZWbmZzzOwmM9usxv65Zna1md0Xve4CM7uszjm6mtlr0XN8YWaHm9kKMzsr0T3VOP4iM/vOzH40swfNbPONvS8R2TBKcEQkFauI9OZAZN2igcAfgR7AcOA+Mzsiur9P9NffAG2A46KfmxFZFHe/aJsPgUlm1iLVoKKLhz4K3A3sBPwOOAG4uU7TS4CPgd2BW4HbqpIrM9sEeAZYC+wJnEVkbbeGNY6Pd09E76cncAhwMnAscFGq9yQiqdEjKhHZIGbWBzgNmBwdh3Mp0Nfd34g2mRNt80ciK3NXLVz5g7uXVp3H3WutYG5mFwLHE0kaHkkxvKuAv7j7g9HPX5vZEOARM7vMf1l877/ufnf0/V1mNhg4GHgHOBTYMXpP30VjuwR4q8Z1Yt5T1DIiC/6uBT4zs6ei5x6e4j2JSAqU4IhIMn4TrVZqQKTnZjxwIZEem0bAC2ZWc+XeTYG5iU5oZq2IrNL8a6AYKAIaA+03Is7eQJ9oUlNlk+h5WwOLottm1jluIdAq+r4bsLAquYl6D1iXZAyfRpObmuf+VZLHikiaKMERkWS8DgwC1hD5x38NgJl1iu7vD8yvc8yaes45hkhicwmRZKgCmAxsluCY+mwCXA88FWPf9zXe143N+eWRvUU/pyrRuUUkIEpwRCQZK939qxjbPyWSmHSo+8iphtXRX4vqbN8XGOzuzwOYWTGR8Swb432gW5xYk/UZsK2ZtXX3hdFtJdROUuLdk4hkCSU4IpIyd19uZrcDt5uZEenpaUpkcO46dx8FLCYyKLmfmc0Fyt19KTAbGGBm7xIpOb+NXxKHVN0ATDSzecCTRAYK9wT6uPvlSZ7jJeALYEx0ksHGwIjouap6duLdk4hkCXWbisjGugYYCvwZmEUkQTieaCl1dDzKYOD3RMajjI8e9zsiydAMYCzwAPWM26mPu78IHEFkXM+06OsK1n98lugc64hUPjWMHj8GuIlIclNezz2JSJawX4oKREQkFjPrRaSMvcTdZ4Qdj4jUTwmOiEgdZnYs8DPwJdCRyCMqA3Zz/aUpkhM0BkdEZH3NiEwAuB3wIzAFuETJjUjuUA+OiIiI5B0NMhYREZG8owRHRERE8o4SHBEREck7SnBEREQk7yjBERERkbzz/75RTNdiPpzDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\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], 100)\n",
    "    x2s = np.linspace(axes[2], axes[3], 100)\n",
    "    x1, x2 = np.meshgrid(x1s, x2s)\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\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",
    "\n",
    "save_fig(\"decision_tree_decision_boundaries_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating Class Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.90740741, 0.09259259]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict_proba([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization Hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've seen that small changes in the dataset (such as a rotation) may produce a very different Decision Tree.\n",
    "Now let's show that training the same model on the same data may produce a very different model every time, since the CART training algorithm used by Scikit-Learn is stochastic. To show this, we will set `random_state` to a different value than earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=40)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
    "tree_clf_tweaked.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 6–8. Sensitivity to training set details:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure decision_tree_instability_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plot_decision_boundary(tree_clf_tweaked, X, y, legend=False)\n",
    "plt.plot([0, 7.5], [0.8, 0.8], \"k-\", linewidth=2)\n",
    "plt.plot([0, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
    "plt.text(1.0, 0.9, \"Depth=0\", fontsize=15)\n",
    "plt.text(1.0, 1.80, \"Depth=1\", fontsize=13)\n",
    "\n",
    "save_fig(\"decision_tree_instability_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 6–3. Regularization using min_samples_leaf:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure min_samples_leaf_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
    "\n",
    "deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
    "deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
    "deep_tree_clf1.fit(Xm, ym)\n",
    "deep_tree_clf2.fit(Xm, ym)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
    "plt.title(\"No restrictions\", fontsize=16)\n",
    "plt.sca(axes[1])\n",
    "plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.4, -1, 1.5], iris=False)\n",
    "plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"min_samples_leaf_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rotating the dataset also leads to completely different decision boundaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TZT+WlfzipJX3opN48OddQW5e/uvAD8nNze80s/b89r7tAO7+KLmVAn8JbM0/bkio5sRUmnYnEoWwL0yiuJDI4sVJPeHYCHPctQ1vtNLQ1Y+7vwWcW+Z4D7kb+oqP3QLcElNpqdTf/2pNx8eimVfyk+RE0ZMR9mfWOvwS9iyAE2fMKLt+/7S2ATZu21Z3l30jzHHXynvRSkXwS23imnbX7Cv5iVRSa1iHPQsgaNOewvFmDsdG6JVoZAr+BjRz5rJhgQzVp909++wFvPvuY0PPp05dyLx5DwaeD82/kp9IOWlfTKmgmcOxEXolGpmCvwHVOu2uNPQB3n33MZ599oKK4R/nkIJkW6OEbZooHKVeCv6IpWHaXWnoVzteoJX8RESaj4I/QlGMkXd1Xcv27T8ABoDxTJ++mNmzbxpTjUEXJvUMKYg0s3I9E2nbC0CkGgV/hMIeI8+F/sqiIwNDz+sN/0oXJlrJT9KgXNiawYsvxl9LOYWb98JehGla20DgXf0iY6Hgj1DYY+S5ln754++//2LgzXtTpy4M6NY/oOqFiVbyawyzZkG5jTbTFJBhqndT0ah+T1H0BGzclo4dMTu3d3LBPRew6pJVsW/zm3VR7KAI6VnApykFjYXXP0YedKU/EHjzHsC8eQ8yderCYa/nnu8r+2n9/a/S27uaJ59cwLp1M3jyyQVaFTDlgoIw4V23hyxYkAvH0seCBfHWEefvKarNfOLWsaaDvv6+qpsFSfiKl2MOk1r8EapnjLzyGP54gsN/pOKLgXJ37z/55IKyN++1tBys+fsSqih2uotqCCBoFkGlmQfNqnN7J907cnsIjGaLYAlPlHsVqMUfoba28zj22G/T2nokYLS2Hsmxx347MDw/GMMvhHtuDL+r61oApk9fXHMNlVruQdvwuqMlgSVW5ZYDrmcaX1p6OJpF6VbBxa1+bZk7evX8rqLcq0DBH7G2tvM4+eQNLFq0jZNP3lCxxVxpDB9yN/BNn34JuZY/5HoELqn451fazCfowmRg4J2yn6X5+xKFNAwBjEbSa/6fOGMG7eOPGvE4ccaMSP684tZ+QaHVD9F1QzejWn9XUe9VoK7+VAkew1+//jT27Pnd0JGJEz/GSSetBRhxY98H6rt5L3cXv+bvN7O03wxYGAIwq70VH+YQwGjW349rCKDaEr5hK23tDx1f3cF9i+/TlrmjVE+XfdTLMSv4UyV4DL849AvP168/jZNOWsu8eQ+WXZL33XcfL/tZ1Vrumr/feIIC0qz8+WHf5Bb2BjUFQWFda9gWvletv6fR3JtQ6zS+JUsW4H7SsGN3AjbO2bpv5AV3Unre6Sl7fOs7W8t2Qzf6/gBRqed3FfVyzAr+GFVbxW/69MUl8/QrK74YqOXmvWot93rm72sXv2Ql3UqvFpBB4RinqBbfqfX97uWvMnww4OojId3Lusse7+3r5dTbTq15V8BmFjTtLq07KGqMPyaFVfyCxtuhvjH8deuOYt26I4oeRw29FnTz3mha7rXcmzCa7ybZtmFDODfvFQS10Gs11lkFpY9Zs8KpK80qdUNX0sw3AwaN4df7u4qagj8mlVbxKzZ79k0sWvQKixa9xqJFr4xiRb79I54Xwr/WWQVQeRbAWL+bZFOYN+8V1gMo7a4/7LB0bOYTxqyCtAdkvd3QYd8MmJbfU+kYfnE9ad1BUV39MRnLKn4TJ35sxBh/ZR9cDNSy8l69ewtoFz+pVb3L3Ma1HkCS6+8XB2TxWHBalvCtpxs6ijnpQb+netW7Sl6lMfy07qCoFn9MxrKK30knrWXixI8NO1b6vNS6dceUDAFUvxuq3pZ7+CsUStSCusqDjke18l7QEEC9oRv2EEA9U/jG8nuq1HrcuG0bPQMvj3ikZWnfSsKek17p9zSWGmvtkYh62l1UFPwxGct4O+TCP9f9n3sUpvIF2zPiebXwr7flPtbvJvF78cXygRt0k+Bobt5Lg6DvVa+w7k0o/v2ZlR8PsHFeMSDT0rVdqyjCMS0XEmkdw69GwR+Tesbbq6t1pKb0YmC4elvu0Xw3aSRh37wXtrhb7pXcffcGvvCF77Kk45tc9vdLuezWP+Nfnn+U9W9trBiQjbpgTtjhmKYLibSO4VejMf4Yhb3T3aJFL+dv5Cu+wa+FkTf8fWDduiM+OLNlOqec8szQ87HM39cufpJmQUMHta4HMJaFhaqpFJAdCzsadsGcesMxaMw97MVt6p1yB+kdw69Gwd/gFi16ecSx4nCvZP/+7fzqV58YCv965u+LVBL2HvW1fl7aFxYqVikgG3nBnHrDMejmvbBb2VGvkpdGCv4GVHkHP4CJVOvWL9i/f7sW35HIhH1nfK2f1wgLCxUEBWQWF8ypNAsg7FZ2o3bXj4WCv8F8sINfwcDQ80L4L1r0Uv5GvtGFv7bglWrCbrmnRVhDAAVR/J6y2CKNs4ejUbvrx0LB32Cq7eA3vCfgkqGLgUrd/9U28hFJak57o4ni95S1FulYxtzToN71AOKk4G84wTv4VeoJaGmZzv7920f9p2jxHYlCVGPuYUtTD0fWWqSN3sMR9sJCUdB0voZT2xachZ6AU055hpaW6cNea2mZnp+CN5IW35EoRLHyXhTCXlhIRq+ReziiWFgoCmrxN5had/Ar7iEonrpXULpML2jxHWkeaWq5y+g0cg9Ho8y+UIu/wQTv4BfUE1C5hyCujXxEqmmEJYGl8cS14mEjLd+rFn8Dmj37prK79pXrCZg+fXHVz4tjIx+ReqRlCKBR7k1Ik7Tc5BbXmHsj3ZugFn+TCOoJqL6tb220Ba9kUaPcm5AmaVhiOM4x90a6NyHRFr+ZfRi4CzgT2AEsc/f7A85dkj+3OHX+0N3XRlxmwwjqCQiTtuCVsUjTgjkSnSi24a2H1gMoL+kW/+3AXqANuBi4w8zmVDj/3919StFjbRxFyge0Ba+MRdo385FwhL17Xj0aacw9bokFv5lNBs4Hrnf3Xe7+OPAT4E+Sqkmq0xa8IlJJWgK3UbfMjUOSLf7ZwIC7dxUd2wRUavF/wsx2mFmXmV1vZoFDFWa21MyeMrOn3njjzbBqzjxtwStRCJpep2l3jSctgdtIY+5xS3KMfwqws+TYTuCggPPXAScAW8ldHDxAbv/ZsneVufsKYAXA/PlzQ95AM9u0Ba+ELe13xms9gNFLS+A20ph73CILfjNbC3w64OUngKuAqSXHpwJ95d7g7sUbYT5nZsuBvyYg+EVEwpL2C5M0UeCmX2TB7+6nVXo9P8bfYmbHunt3/vBcoHO0fwRg9VcoIiKSPYmN8bv7e8BqYLmZTTazTwHnAPeWO9/MzjaztvzPxwHXAz+Oq14REZFmkPR0viuAScDrwA+By929E8DM2s1sl5m15889A/iNmb0HPELuouFvE6hZRKSpxbXMrSQj0eB397fc/Vx3n+zu7cWL97h7T36ufk/++ZfdvS1/7ix3/xt335dc9SIizSkNq+5JdJJu8YuISIo0ytayUj8Fv4iIDEnDqnsSLQW/iIgA6Vl1T6Kl4BcRESA9q+5JtBT8IiICpGfVPYlWotvyiohIemjVvWxQi19ERCRDFPwiIiIZouAXERHJEAW/iIhIhij4RUREMkTBLyIikiEKfhERkQxR8IuIiGSIgl9ERCRDFPwiIiIZouAXERHJEAW/iIhIhij4RUREMkTBLyIikiEKfhERkQxR8IuIiGSIgl9ERCRDFPwiIiIZouAXERHJEAW/iIhIhij4RUREMkTBLyIikiEKfhERkQxR8IuIiGRIosFvZlea2VNm1m9md4/i/C+Z2XYz22lm3zez1hjKFBERaRpJt/i3Ad8Avl/tRDM7C7gWOAOYCcwCvhZlcSIiIs0m0eB399Xu/hDw5ihOvwS4y9073f1t4OvAkijrExERaTYtSRdQgznAj4uebwLazOxQdx/NhYMIEyYcwJQpk9i//0DgkKTLkQyaMuVADj74EMYfOo6WQz6MjXMmTZyUdFmSIebuSdeAmX0DONLdl1Q45wXgL9z90fzzCcBe4Bh331Lm/KXA0vzTjwG/C7nsWhwG7Ejwz0+avn92v3+Wvzvo++v7J/v9j3b3aaUHI2vxm9la4NMBLz/h7qfW+JG7gKlFzws/95U72d1XACtq/DMiYWZPufv8pOtIir5/dr9/lr876Pvr+6fz+0cW/O5+Wsgf2QnMBR7MP58L9KqbX0REZPSSns7XYmYTgfHAeDObaGZBFyP3AJea2fFmdghwHXB3TKWKiIg0haSn810H7CY3TW9x/ufrAMys3cx2mVk7QH5s/2bgl8DW/OOGJIquQyqGHBKk759dWf7uoO+v759Cqbi5T0REROKRdItfREREYqTgFxERyRAFf4Rq3YugmZhZq5ndZWZbzazPzJ4xs7OTritOZvYDM3vNzN41sy4zuyzpmuJmZsea2R4z+0HStcTJzNbmv/eu/CPJdUQSYWafN7Pnzew9M3vBzBYmXVMciv6dFx4DZva9pOsq1kgr9zWiwl4EZwFZW5qrBXiZ3FoOPcBngQfN7PfKLbjUpG4ELnX3fjM7DlhrZs+4+8akC4vR7cCGpItIyJXufmfSRSTBzP4H8C3gj4H1wBHJVhQfd59S+NnMJgO9wKrkKhpJLf4I1bgXQVNx9/fc/avuvsXdB939p8BLwIlJ1xaX/L4S/YWn+cd/SbCkWJnZ54F3gF8kXYvE7mvAcnf/df7v/6vu/mrSRSXgj4DXgceSLqSYgl9iYWZtwGxyCzFlhpn9g5m9D/wWeA14JOGSYmFmU4HlwF8lXUuCbjSzHWb2hJmdlnQxcTGz8cB8YJqZ/aeZvWJmt5lZ1no9Ibe53D2esulzCn6JXH5fhfuAle7+26TriZO7XwEcBCwEVgP9ld/RNL5ObjfNl5MuJCHXkNs6/CPk5nL/s5llpbenDZhArrW7EJgHfIL8Gi1ZkV+D5tPAyqRrKaXgl0iZ2TjgXnIbKl2ZcDmJcPcBd38cOBK4POl6omZm84A/AL6TdC1Jcfcn3b3P3fvdfSXwBLn7XLJgd/6f33P319x9B3AL2fn+BV8AHnf3l5IupJRu7pPImJkBd5FrAXzW3fclXFLSWsjGGP9pwEygJ/efAFPILcl9vLt/MsG6kuSAJV1EHNz9bTN7hdx3zrIvADclXUQ5avFHqMa9CJrRHcDHgf/l7rurndxMzOzw/HSmKWY23szOAi4E/l/StcVgBbkLnHn5xz8CD5Ob3dL0zOxgMzur8PfdzC4GFgE/S7q2GP0f4Kr834NDgC8CP024ptiY2SnkhnlSdTd/QZZCKAnXMXw/gcXk7nb9aiLVxMjMjgb+nNyY9vZ8yw/gz939vsQKi4+T69b/R3IX2FuBL7r7jxOtKgbu/j7wfuG5me0C9rj7G8lVFasJ5KbxHgcMkLux81x3z9Jc/q+T24u+C9hDblfVbyZaUbwuAVa7e9lt45OmtfpFREQyRF39IiIiGaLgFxERyRAFv4iISIYo+EVERDJEwS8iIpIhCn4REZEMUfCLiIhkiIJfREQkQxT8IiIiGaLgF5G6mdmk/H7rPWbWWvLanWY2YGafT6o+ERlJwS8idctvvnQDcBRwReG4md0IXApc5e4/Sqg8ESlDa/WLyJiY2XhgE3A4MAu4DPgOcIO7L0+yNhEZScEvImNmZn8I/DPwC+B04DZ370i2KhEpR139IjJm7v5T4GngDOAB4OrSc8zsL8xsvZntMbO1MZcoInktSRcgIo3PzC4A5uWf9nn5rsTXgJuABcB/j6s2ERlOwS8iY2JmZwL3AmuAfcCfmtl33P354vPcfXX+/Pb4qxSRAnX1i0jdzOxkYDXwBHAxcB0wCNyYZF0iEkzBLyJ1MbOPAw8DXcC57t7v7i8AdwHnmNmnEi1QRMpS8ItIzfLd9T8HdgJnu/u7RS8vB3YDNydRm4hUpjF+EamZu/eQW7Sn3GuvAQfGW5GIjJaCX0RiYWYt5P6f0wKMM7OJwKC77022MpFsUfCLSFyuI7e8b8Fu4N+A0xKpRiSjtHKfiIhIhujmPhERkQxR8IuIiGSIgl9ERCRDFPwiIiIZouAXERHJEAW/iIhIhij4RUREMkTBLyIikiH/H7NYpBsqGX0jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "angle = np.pi / 180 * 20\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xr = X.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_r = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_r.fit(Xr, y)\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "plot_decision_boundary(tree_clf_r, Xr, y, axes=[0.5, 7.5, -1.0, 1], iris=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 6–7. Sensitivity to training set rotation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure sensitivity_to_rotation_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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dxHJsci8VYS22IpKn1sWZk3ONo4g08aJGCXLBDdIGkGZiHeJkp0Gr1p2S+lgnXcTQqtBLRViLrYjkrdX6bvZm3v3uv+ET1/8e17zjm5Wag9wtTbyoiSJBNrPFZnavme01s+fM7MoW133YzJ40s1fMbKuZ3WZmld5o2G8bQKvEetOmG/tOmvM+2WnQqnWnpD5EVbyTWFoVeqkIx77YxlKRF+lVDJ8ediNEnM3W91NO+RDu32Xu3L21w0Lm7GPFL32P3RP3Zh5PkWg8Zk0syeUa4BAwBrwRuN/M1rv7hobr5gI3AI8BS4D7gE8Cn80x1qj02wbQKrFOTnXodlRbKINOwKg/Rqvr03j8tDVLTPPeXdyqInz9Rdc3nU4R+2Jb9Z3aZVeWSTSNv8eiRRezffvf9DRas9NzkcVz1e8I0DQ0ru+PPXY+tVTjmJGhSXZuvo0FY/8u01iKpN/xmGWbVBQ8QTazecDlwOvdfQ/wiJndB1wN3Ji81t3vTPzr82b2ZeDduQUbodHR06erwLNvb6fdBoakbka1hdTrXMzYHr8XvSamWWlXEW6WYMY8i7ixIp/3cynZCpmcpanZ7zFzRGVNu/W603OR1XPV7wjQLLR63Zs8+EKucZRV2YoNMbRYnA1MuXuyEWg9cG4X970IaKwyV0q/bQC99NEePLg12o/uqiSWVoU0K8Kh2xu0ebDcyjKJpvnIMm96basksNNzkdVzFdNm51aveyOjp+UcSfnE0v6XphgS5PnArobbdgEL2t3JzK4BVgC3t/j+ajNbZ2brdux4KZVAY9Tv5rhmiTVYy+s1/ze8XhPTrJLPB1c/yJabt8z6GnQQfd60efCYsq6XMSVng+gl3lZJYKfnIqvnKqbNzrXC0fEzbps8MsLi8f+UeyxlU8ZiQwwJ8h5gYcNtC4Hdre5gZpdR6zu+xN1fbHaNu9/l7ivcfcWSJeUe69LP5rhmifWpp/5mk6S5Ju2qS1E2l8Sk18Q0hmkX7YSuOMRSkY9BWdfLmJKzQbSOd2ZRo92nh52ei6yeq5g2O4+NrWJk5HfYt29e7bCQ/XNZ9/N3qf94QGUtNsSQIG8CRsxseeK282jROmFm7wP+DPh1d/9xDvGVVmNiffbZn2X58qYFeSC9qotOqGsvjcpv6OSzG6ErDrFvHpTBxZScDaLV73Hqqb/Z9aeHnZ6LrJ6rECNA2xkZuYjvfvdD/NEd/w93P/x+naSXgrIWG4Jv0nP3vWa2FrjFzD5KbYrFpcDbGq81s/cAXwb+nbs/nm+k1TA2tmp6F3PvG/+6FdOmjRilsdEhhmkX7cSw4TDmzYPSXrfTFmKcRNOPtCb2tHuMLJ+rmDY7S/rKWmwIniBPuxb4C2A78BLwMXffYGbLgJ8Ar3P3LcDNwInAN82OfrT0sLtfEiDm0hofv2nGbmZIt+pSlr7ALKQxVSGG5LOTXidhZKVsY4mqoNdpC2VJztL4PTo9RlmeK8lXWYsNMbRY4O473f0yd5/n7svc/SvTt29x9/nTyTHu/m53H5m+rf6l5DhljR+JDQ8vYmjoBDZuvC6VfuGy9AVmIY22gyJ83BVLxSH2Pm2ZrSyTKUQkbrFUkKVB6OH29UpCvVozNZXebMysK9RFlVblN5bks50YKg6agVxM+gRKRPKgBDlC3Qx0zyt5zqJfuCx9gWlLq+0ghuSzCGLv05bm+j0cSUSkF1G0WMhM7ZLSvCdAZFWt6Wc0XdkVofJbFmUdS1QFZZlMIZKm0IculZEqyBFql5TmPQEi9mpN6FaUZnGcc858Xnnl7eyc6O10JlV+8xPLJkHpnT6BEpmtbMc8x0AV5Ai128SWd/9dzNWaWOYpN8Zx/PG7eetbv8XZZzybaxxlkUclRNX6YtMnUCLHhJ57X9bqtRLkCLU6BrqWgDX/T5ZVRTe2Ie9JsexmbxbHyMgkF75+Xa5xlEUekyXSPC5bRCSk0IculXUakBLkCM1MSqF2nGj94+CpWddnXdGNtVoTy272Vj9vwdy9ucZRBqErISIxmphYy2OPnc/3v39aKqM2pTxC76co85qtBDlS9aS0liR7kyuG6beiW5bFNpZ5yq1+3u5983KNowxCV0IkPkVar7KINZZWspgV6W8kbaHn3pd5zVaCHLnW1dAjfVV0y7TYxtIf3SyOyckRHv3nFbnGUXShKyESnyKtV1nFGksrWayK9DeShZD7Kcq+ZitBjlzaVdIyLbax9Ec3xnHo0AJ++MNfZdPPzsrsZ5ZxU0ToSojEp0jrVVaxxtJKFqvY/kbyXptD7qco+5qtBDlyaVdJy7bYxtIfnYzj6aevYfPmc2Zdk+bCWcZNEZosIY2KtF5lFWssrWSxyup573e9LuPa3ErZ1+yu5iCb2RzgGeAIsNzdDya+9wXgGuAqd/9qJlFWWC8zP7uZCRz7XOMyS2tOZVmPSNYECWlUpPUqq1jHx2+acbIqxDNqMwZZPe/9rNdlXZtbKfua3VUF2d33A58GzgCurd9uZrcCvw18XMlxdrqpknbbhxVL327VpLnTt8ybIkSSirReZRVrLK1kscriee93vdbanK2821d6abH4S2ADcJOZzTezG4AbgU+7+59mEZx0r9s+rGaL7SmnfIjNm2+t5A7gvKS1cJZ9U0QzZey3LqMsJgkUKTnMMtZYWslilMXz3s96XcW1OW95t690fdS0u0+Z2Y3A3wN/C7wH+Ly735JVcNK9XvqwxsZWHV086pXnenJdrzzXr5PBvXL4Fb6+YfbC2c/Hb1U8IllHqMYvy3UkuV7Frkix9uIXv9jJCy/8LHQYbZzJggVfYMGC2r+99BK89NL6plfu2LGt7SO1SnQ7rddVXJvzFKJ9pesEGcDdv2FmPwIuBr4KXJ/8vpmNAn8y/f1TgJ8Da9z9j9MJt3q66SuG/vuw2lWe81rou/0di+qhiYdSWzjLvimiUdV6+ooqhnVE0nfkyBF+8IPvct99/8iuXceHDic1L+06GVu2BYaOMHfu3Bnf6zfRrdranLdmVf2s33j0lCCb2YeAN07/625v/CuqPd424NeAfwXeAPwPM/u5u39t0GCrppeqTL8bOULvEq9CBXvL3i2pLZx5bYqY2D3BdWuvY83la4ImpCEWReld6HVE0rdnzyt8+ct384Mf7mfX8HHY/D2hQ2rpgO3jyQXf582738kJPqfj9bZ4O0PHTXHa6afzkUs/MuN7/Sa6Zd+wFlK/Vf1BdZ0gm9mvAV8E7gUOA79lZn/k7k/Xr3H3vcDNibv9k5ndD1wIKEHuUS9VmV6mXSSF3iVehcrTDa+9gSs+cEXoMHoSQ1tDqEVRehd6HZH0ffWrd/P44/vY5c7QadsYHol3Kuwz9iNeYoJnFq/jTf5/dLx+aGiYS975Pi5++8UMDc38vZToxidU+0q3Y95WAmuBR4GrgKXA5cCtwGVt7jcCvB24beBIK6jXqkw//W+hRwip8hSfWNoa1NNXHKHXEUnf3r17OHx4FDthL0Mjxqr3XcYbznlD6LBm2bF3B++/+/0wBS+MPM+ff+QLvGreq9reZ/T4Ueac0LnSLHEI1b7SMUE2s3OA+4FNwGXTM5B/amZ/DvyOmV3o7o+2uPvngF3AX6cVcJXkUZXpt/Lcr8Z+45GRk5icfHnWdao8hRNLW4N6+ooj73VE8nfSiSdx0sKTQocxy22P3IZTW6/cnbv/193B30DH0qJWFqGq+m0TZDNbBvwDtST3End/JfHtW4APU6sOX9jkvn9ArXr8Hnc/1Ph96SyvqkxeO6+b9RvDcZgdh/vho9ep8hROTG0N+qizWMo6wSELZd+YnJeY1qukGFrUZHBtm4rcfYu7n+HuZ7r7RMP3fu7uc929WXL8x9Q26l3s7i+mG3J1FGkGaDea9RvDYYaG5pfmdyy6dm0NIjK4bg91ks5iXK/SPBSqimKae596172ZfQ54L7XK8Y4u77PYzO41s71m9pyZXdnm2k+Y2TYz22VmfzE9Wq60yjQgvlVf8dTUL0rzOxad2hpEstXtoU7SWYzrlU7TG0zeh4G009OYt07M7NXAx4GDwP9vZvVvPezul7S56xrgEDBGbYzc/Wa23t03NDz+v6V2et97gBeoTdT4L9O3SeS00z1+vbQ1qM9OpHfamJye2NqwYm35KIpYNojXpVpBdvfn3N3c/QR3n5/4apkcm9k8ahMxbnb3Pe7+CHAfcHWTyz8M/Lm7b3D3l4H/Cnwkzd9B+tfpqNnx8ZsYGpq5c1j9xsUV0zt9kaJoVRBQoSCMND/Sj7Hlo0hiq77HMNjwbGDK3TclblsPnNvk2nOnv5e8bszMTs4wPulCN311Zeup7uTA0D7+8biH2H14d+hQUqc+O5H+qFBwTAz9pmm+0c+j5SOG5ywLrarvIX/PGBLk+dSmZCTtAhZ0cW39n2dda2arzWydma3bseOlVAKV1rrtqytTT3Un/zL3f/GS7eChiYcy/TkhFszY3unLYLRe5qdqhYJ2Qn8KlfYb/QdXP8iWm7fM+kqzFST0c5aVGKvvMSTIe4CFDbctBJqV3Rqvrf/zrGvd/S53X+HuK5YsUYE5C8mWima9xVDdvrp9Q3vYcsIzYLBu57pMk9e8F8wY3+nLYLRe5qtKhYJWYvgUqmhv9GN4zrIS44bLGBLkTcCImS1P3HYesKHJtRumv5e8bsLdVfLIWWNLRStV7av7pxP/59Hh9VkuvCEWzBjf6YtIsYROTov4Rj/0c5alPKrvvQqeILv7XmrHWN9iZvPM7ELgUuCLTS7/a+C3zex1ZrYI+BTwl7kFK0c1n2k8U1X76nYd2csz8zbgdgSAKaYyW3hDLJgxvtMXkeKIITkt2hv9GJ6zVsraFx08QZ52LTAH2A7cA3zM3TeY2TIz2zN9oh/u/iC1k/u+Czw3/fXpQDFXWvvWiWr31f3Dgcdxm3lbFgtvqAUzxnf6IlIcMSSnRXuj389zllfiWta+6FTnIPfL3XcClzW5fQu1jXnJ2/4Q+MOcQpMWWs80XsrKlU8EiCgemyd/zhGbmnFbFgtvuwVTx5uKSD/qx2C/8Y1bOeusBTy8/i08e/ikVH9GDMlp0d7Q9/Oc5XHkdWyzi9MURYIsxTM+fhPPPPPJGW0WVW2paPR/L7ySp54aYeecXQwt3McFb3kTV3zgitR/TgwvMiJSHvW9JUeO7McMFizYza9e8I8MbXxTqj+naMlpDHp9zvJKXJu1+ZWlQKMEWfpSb53YvPlWDh58ntHR0xkfv6mSLRWh6EVGRNLUbG/JcSOTvO01/xwoIulXHolr2U8OjKUHWQpIo4pERMqj1d6SBSe035Atcclrf0oMveRZUoIsIiKp63T0fN6PI521Gsu5+8Ccprd3q6xTDmKVV+Ja9jY/tViI5Gxi9wTXrb2ONZevKcXHUCKNkr2swNGj54GePmlK63GaPa7aw2Zrtrfk8OQIP/jX1/OOV/f/uHlsFpNj8kpcy97mpwRZJGd6sajRG4Xyanf0fC+JaFqPk9Qs6d648QZ++tNPMTn5i0onzMm9JQcObGXPnmNTLN7R52OWecpBrMqeuOZFLRYSRFU/Ni3zUaG9ynp2pj7WDadVL2uvR8+n9ThJzQ85Oszk5MuAH61SV2VNalTfW/JP/3QdX/vaajZuXt75Tm2U+fQ3KTclyJK7xmOqq/SCVJUXi07JaR5vFMo6vL4IWvWy9nr0fFqPk9RNcl2vUstg8tgspjfCx+i5SJcSZMldu49Nyyzmo0LT1ik5zfqNgir1YY2P38TQ0MyNXf3MSU/rcZK6Ta4HqVJLTR6bxfRG+JhYnouyJOpKkCV3WXxsWgRlH4lT1yk5zeONQlUq9bEaG1vF8uW3Mzq6lEGOnk/rcZKaJd3NDFKlbqVqrWVZbxbTG+FjYnouYknUB6UEWXKXxcemRVD2kTh1nZLTrN8oVKlSH7O05qSnPW+9MekeHl6E2XEzrsniVNCit5bt3L+z56rgg6sfZMvNW2Z9pbWJLK83wkWoiMZSFIgpUR+UEmTJXRYfm2YtjcpP1i8WMegmOc36jUJVKvXSv2TSfeGFP+Hss/841Sp1M7G1lvW6pt3zk3s6VgXzTCTzfCMce0U0pqJALIl6GpQgS+6y+Ng0S1lXfopQnehWN8lp1i00snsAABhDSURBVG8UqlKpl/TkcSpoN61lebVg9LqmHWA/39787Y5VwTwTybzeCBehIhpLUSCmRD0NSpAliCIdU5115Sf26kQvYkhOq1Cpl+Lp1FqWZwtGr2vav9jTHOFI7boWiVfeiWRea00RKqIxrLsQT6KeFh0UItJBlpsKyzZEP9YktNOhJDq0pJiKdCJes1Pqkq1lWRyK0kova9qBob08x3McOVJLkOtVwca1qlkimeVBSHmsNa0qorGt07Gsu7Ek6mlRBVmkgyw3FRahOlEG3YydK0sVvyqKtumtU2tZntN9elnTNi2cndw0rlVl+2i9rmwV0ayV7dM7JcgiHaSxqbBZn3FZX1Ri0+3YuZh7DGV2f+6zz34qqk1v3WjXWpbndJ9e1rSdoxMcsSMzbmusCpY1kSxbRVR6oxYLkQ7qL2KDfJSbrFDWP3Zs96KS5UeTVdPpo9+8PxqW3tWrxfWEuFY1bq6o89Q7tWCkqZc17V0TH2T49B389r+/hjf88huaPl5ZE8nYKp9qBcuXEmTpW5H6/wY1Nraq79/tlcOv8PUNs/uMy/qikqdueovb9RAWpcew6pr157bSrOJahLUqjTfivf68tB47tkSyrJoVWiQ7arGQvhSt/y+khyYeatpnXLZ+rRB6OdK6LvnfoKwfDZdNt1XhZhXXIq1VRZruUyUxjOJUK1j+lCAXUAzHlcY29D5W+9nPup3r1GecgW5eMDpV6VXFL4ZWfbgjI4s6zlPXWiWDimETb0wbumN4w5AHtVgUTLNevGee+SRArtWGPHdcF9m/DP+z+owz0k3vcKdqvKr1xdCqP/fMMz/Tcd3TWiWDiGEUZ2ytYFVp9QhaQTazxWZ2r5ntNbPnzOzKNtd+2MyeNLNXzGyrmd1mZpVL8GOphmSx4zqGyni/krGfc87djI8/DcDOoReZYmrGtapQDk4TQKplkNM385wOIeUTQ+U2plawKrV6hE4w1wCHgDHgjcD9Zrbe3Tc0uXYucAPwGLAEuA/4JPDZnGKNQizVkLR3XMdSGe9HY+zHH7+bt771W+x58kKGdl3CBW95E1d84IrAUZaLJoBUT7+byvKcDiHlEkvlNqZWsCpN/QmWIJvZPOBy4PXuvgd4xMzuA64Gbmy83t3vTPzr82b2ZeDduQQbkdHR05uOOMq7GpL2jus8T5FKW7PYR0YmufD163j20Q8EiqrcYnrBkLjlPR2irgiTM6S9WN6Ix9IKFssbhryErCCfDUy5+6bEbeuBd3Z5/4uAZpXmUoupGpLmmKBYKuP9aBXjgrl7c44krDxndMbygiHF0GmtSjuZLfInYnJMqDfisc47juUNQ15C9iDPB3Y13LYLWNDpjmZ2DbACuL3NNavNbJ2Zrdux46WBAo3JIL14MStyn2CrGHfvm5dzJGH1u9O7KjuiY1bW9bIbWYyBi2WviAwm1CjOkFMz2q3HVfvkLrME2cy+Z2be4usRYA+wsOFuC4HdHR73Mmp9x5e4+4utrnP3u9x9hbuvWLLk5EF/naiUcVZmGsc5h9Is9snJER795xWBImovi4R0kI0bMYxQqroyr5edZJHMFvkTMQkr9Ca4dutx1Wb3Z5Ygu/u73N1afL0d2ASMmNnyxN3Oo03bhJm9D/gz4Nfd/cdZxS75K3JlvDH2Q4cW8MMf/iqbfnZW6NCayiIh7Xend+gXA5EsktkifyImYYWcmqH1eKZgLRbuvhdYC9xiZvPM7ELgUuCLza43s/cAXwYud/fH84tU8lLkyngy9qefvobNm88JHVJTWSyAg4xci2GEklRbFslsq0/EFi26uLCjLCV7ocdXaj2eKfRJetcCc4DtwD3Ax+oj3sxsmZntMbNl09feDJwIfHP69j1m9kCQqAMo8oxgiUcWC2C/MzpDvxiIQDbtXc0+ETvllA+xffvfFOLIawkj5LxjrcezBU2Q3X2nu1/m7vPcfZm7fyXxvS3uPt/dt0z/+7vdfWT6tvrXJeGiz08Wm0ikerJaAPvduBHqxUCbAiUpq/auxk/EXn7529q4J22F3AQX02EksQh9UIh0ocgzgiUeWY3o6XeDRqgXg6ockyrdS3NkZSvauCedhNzsVrUJFd1QglwAWlglDbEtgCFeDBp7sMs64F7iE8shT1IT66zhUMo6iWIQSpALQAurpEELYLWOSe3X4cOHmZj4eegwSmfRot9hYuIzuB84epvZCdO3x/d8N37aVDb6JEk6UYJcADGdnidSVFU7JrVf27bt4NZb7wwdRiktXfpWzj33CebO3cO+ffPZsOF8tm59Dojv+T58eJjdh8B+6WUMY/S40dAhpUafJEk3lCAXQL03Ls2jUKUm7SNmJV5VOya1XwePwOYDna+T3m1+9iweeTbO+eizDB9i6KwdDA3D2978Ns4a7y/uGFsZ9EmSdEMJckHksYmkaurTQeqV+fp0EEDPdQnF1oMdKztukpGxah03Lc2NnjCH3/rQh/nlM3+578eIrZWh20+SYkzsJV9KkKWyNB2kWtSD3Z2xV43xu//XJ0KHIRE4dcmpjI7231oRYytDt58kxZbYS/6UIEtpdWqf0HQQkdmOP/54Xr301aHDkBKIsZWhm0+SYkzsJX9KkEtCvbQzddM+oekgIiLZiHVTbDefJMWY2Ev+Qh81LSnQSXuztWufqMviiFkRESnuyWw6clnqlCCXQDfJYNV00z6R1RGz0h8dAS3Svdj/fynqptiiJvaSPrVYlIB6aWfrtn1C00HioU0xIt2L/f+Xom6KLWpiL+lTglwC6qWdTYerFIs2xYh0T/+/ZKeoib2kTy0WJaBe2tnUPlEszTbFiEhzVfv/JfZ2EiknJcgloGSwubGxVaxc+QQXXfQCK1c+UfnnI1adNsXoxVHkmCpuIku2k4jkRQlySSgZlKLqtClGL44ix1RtE1ljO0mZ3whIXJQgS3ATE2t57LHz+f73T+Oxx86v9Hi6Kmq3KUYvjiIzVW0TWdXaSSQe2qQnQXVzoIeUW7tNMf/5m/9ZA/tFEqq0iSzWw0akGlRBltwlK8YbN/5HzXCWpqrYaykixxS5nUR7J4pPCbLkqvHUP5hqel2IGc5q9YhLkV8cRWRwRW4n0d6J4lOLheSq2al/zeQ9w1mtHvEp8oujiAyuqO0kmlNdDkqQJVfdVIZDzHBud1x3MkGemFjL5s23cvDg84yOns74+E2ZJNATuye4bu11rLl8TWUX1qK+OIpI/mJaM5ttLNTeieJRi4XkqnVleJiQM5y7Oa67sT2kXmXOohVDH8+JiHQvzTVzkP5h7Z0oj6AJspktNrN7zWyvmT1nZld2eb/vmJmbWekr4GXri2116t9rX/u5oDOcWyXuydvbVZnTpNFmIpKkDV/tpb1mDpJsa+9EeYSuIK8BDgFjwFXAnWZ2brs7mNlVVKQ1JM+KZV5iPfWvm+O6u6kyp0FzP0UkSZ8otZfmmjlosq29E+URLEE2s3nA5cDN7r7H3R8B7gOubnOfE4FPA/8pnyjDyqtimbcYT/3rJnHvpso8KH08JyJJ+kSpvbTXzF6S7WaV/QdXP8iWm7fM+tKeiuIJWUE+G5hy902J29YD7SrIvw/cCWzr9OBmttrM1pnZuh07Xhos0kDyqlhKTafEvZsq86Bi+XhOH+lWS3K93LljZ+hwJEGfKLWX5prZa7Ktyn65hUyQ5wO7Gm7bBSxodrGZrQAuBD7fzYO7+13uvsLdVyxZcvJAgYaSR8VSupdHe0gsH89p4a+W5Hq5eMni0OHINH2i1Fmaa2YvybYq++WXWS+vmX0PeGeLbz8KfBxY2HD7QmB3k8caAv4UuN7dJ80sxUjjNT5+04zZvBBmBFqjvEadxWhsbFWmv2sMH8NVZYZnTGOhRJppl7BpbFhNmmtmL8m2RrmVX2YJsru/q933p3uQR8xsubs/M33zecCGJpcvBFYAX5tOjoenb99qZh9094fTiTou9UQspmQ07wM1qpyMh1KVhT9ZJS/j7yfFF8snSlXRbbLdqrKfLCboDXjxBZsG4e57zWwtcIuZfRR4I3Ap8LYml+8CTkv8+xnA48CbgR1ZxxpS1hXLXnV7oEYadLpd/rpZ+MugKlVyKbY8PlFSIte7bir7egNefKHHvF0LzAG2A/cAH3P3DQBmtszM9pjZMq/ZVv/iWFI84e6Hmj+0ZCHPjYP9TvEo2+zoPMWySTBr2vgkUqP9Br3rVNlXf3I5BE2Q3X2nu1/m7vPcfZm7fyXxvS3uPt/dtzS532Z3N3efzDdiSWPjYLcJbD/JeBlnR7eSxaSJPD/SDTUpQxufRGqUyPWn0yg3vQEvh9AVZCmYQUed9ZLA9pOMl3V2dDNZVH7ynOEZqnJVlSq5SCcxJnJFHzGpN+DloQRZejLoqLNeEth+kvGqzI4ueuUnZPza+CQSbyJX9JYPvQEvj0oc2SzpGmTjYC8JbD9TPEZHT5+uTs++vUyKPmkiZPwxjNITCS3GEXJl2DyrN+DloQRZclNroxgCpmZ9r1UC22syHuvs6DQVfdJE0eMXKYMYE7miv/EHvQEvEyXIksus4XrvcbPkOM0ENsbZ0WmLsfLTi6LHL1IGsSVyeuMssVEPcsXlNfWhWe9xzXDqxzWPja1i5conuOiiF1i58olSJccQZ+WnF0WPX0TSF7J3t+gbAyUbqiBXXF4Hf7TeJHekdAls1mKr/PSq6PGLSPpCvnHWoR7SjCrIFZfX1Ic05ieLiEg51UdM/sabfwMz4+o3X53ZiMmkok8EkuwoQa64vBLXQecni4hIuYVIVmOcBS1xUIJccXklroPOTxYRkXLLO1mNdRa0xEEJcsXlmbiWffOciIj0J0SyqkM9pB1t0pOBDv4QEREZVIjxj5qoI+0oQRYREZGgQiSrmqgj7ShBFhERkaCUrEps1IMsIiIiHelADakSJcgiIiLSUfJADZGyU4IsIiIibelADakaJcgiIiLSlg7UkKpRgiwiIiIt6UANqSIlyCIiItKSDtSQKlKCLCIiIi3pQA2pIs1BFhERkZY0o1iqKGgF2cwWm9m9ZrbXzJ4zsys7XP8aM/uGme02sxfN7La8YhURERGRagjdYrEGOASMAVcBd5rZuc0uNLPjgW8B3wFOBZYCX8opThERERGpiGAJspnNAy4Hbnb3Pe7+CHAfcHWLu3wEeMHd/9Dd97r7AXd/KqdwRURERKQiQlaQzwam3H1T4rb1QNMKMnABsNnMHphur/iemf1Kqwc3s9Vmts7M1u3Y8VKKYYuIlEtyvdy5Y2focEREgguZIM8HdjXctgtY0OL6pcAVwOeA04D7gb+bbr2Yxd3vcvcV7r5iyZKTUwpZRKR8kuvl4iWLQ4cjIhJcZgnydIXXW3w9AuwBFjbcbSGwu8VD7gcecfcH3P0QcDtwMnBOVr+DiIiI5Gti9wQf/KsP6iASCSqzBNnd3+Xu1uLr7cAmYMTMlifudh6wocVDPgV4i++JiIhICdzx8B08/rPHdRCJBBWsxcLd9wJrgVvMbJ6ZXQhcCnyxxV2+BFxgZu81s2HgBuBF4OlcAhYREZFM1Y+1dncdZy1BhR7zdi0wB9gO3AN8zN03AJjZMjPbY2bLANx9I/AbwH8DXqaWTH9gut1CRERECi55rLWOs5aQgibI7r7T3S9z93nuvszdv5L43hZ3n+/uWxK3rXX3s9x94XQLR6t2DBERESmQevW4fqz1oalDqiJLMKEryCIiIiIzqsd1qiJLKEqQRUREJLgfbf3R0epx3aGpQzy59clAEUmVjYQOQEREROTB1Q+GDkHkKFWQRUREREQSlCCLiIiIiCQoQRYRERERSVCCLCIiIiKSoARZRERERCRBCbKIiIiISII1DuUuIzPbATyX0cO/Cngxo8fOimLOTxHjVsz5ySruV7v7kn7umOF6WcT/RkWMGYoZt2LOTxHjzjLmputlJRLkLJnZOndfETqOXijm/BQxbsWcn6LG3Y8i/q5FjBmKGbdizk8R4w4Rs1osREREREQSlCCLiIiIiCQoQR7cXaED6INizk8R41bM+Slq3P0o4u9axJihmHEr5vwUMe7cY1YPsoiIiIhIgirIIiIiIiIJSpBFRERERBKUIIuIiIiIJChB7pGZLTaze81sr5k9Z2ZXdrj+NWb2DTPbbWYvmtltecWaiKGnmBP3+46ZuZmNZB1jk5/ddcxm9mEze9LMXjGzrWZ2W14x9xjnJ8xsm5ntMrO/MLPRPGJsEkdXMYd8XlvE0/Pfcci/4emf38vfR/C1Ik1aK/NThPWyiGvldCyFWy+1VqZDCXLv1gCHgDHgKuBOMzu32YVmdjzwLeA7wKnAUuBLOcWZ1HXMdWZ2FRAsGaK3mOcCN1A7aWclcDHwyTyCpMs4zezfAjdOxzYOvAb4LznF2Kjb5zbk89pMT3/HEfwNQ/d/H7GsFWnSWpmfIqyXRVwroZjrpdbKNLi7vrr8AuZN/wc8O3HbF4HPtrh+NfBwkWKe/v6JwCbgAsCBkdhjbrj/7wJ/H1OcwFeA30/8+8XAtiL8PeT9vKYRd+i/4T7+PoKvFYH/ewX//Yu4VvYbd8P9M///uohr5aDPbaj1Umtlel+qIPfmbGDK3TclblsPtHpndgGw2cwemP4Y4Htm9iuZRzlTrzED/D5wJ7Aty8Da6CfmpIuADalHNVsvcZ47/b3kdWNmdnKG8TUzyHOb1/PaTK9xh/4bht5ijmGtSJPWyvwUYb0s4loJxVwvtVamRAlyb+YDuxpu2wUsaHH9UuAK4HPAacD9wN9Nf0SQl55iNrMVwIXA5zOOq51en+ejzOwaYAVwewZxNeolzsZr6//c8XdKWV/Pbc7PazNdxx3J3zD09lzHsFakSWtlfoqwXhZxrWwWC8S/XmqtTIkS5ITpdyLe4usRYA+wsOFuC4HdLR5yP/CIuz/g7oeo/c9yMnBOjDGb2RDwp8D17j6ZVoxZxtzwuJcBnwUucfcXs4l+hl7ibLy2/s9tf6cM9PzcBnhem+kq7rz+hrvUy3Od+VqRJq2V+f2dlWS9LOJa2SwWiH+91FqZEiXICe7+Lne3Fl9vp9anM2JmyxN3O4/WH6M8Ra2npygxL6T2rvdrZrYNeGL69q1m9o5IYwbAzN4H/Bnw6+7+47Ri7aCXODdMfy953YS7v5RhfM309NwGel6b6TbuXP6Gu9TLc535WpEmrZX5/Z2VZL0s4loJxVwvtVamJY9G5zJ9AV8F7qHWVH4htY8Bzm1x7WuBfcB7gWHgE8BPgeNjjBkwartC61/nT/8hnh5rzNPXvgd4Cbgo1r8H4H3UerxeByyitgO3q000AWMO9rz2G3dMf8M9PtdRrBUhfveYfv8irpV9PNdB/r8u4lrZY9zRrJdaK1OKKfR/yKJ9AYuBvwX2AluAKxPfW0bto4JlidtWAc8CrwDfa7VoxRRz4nvjhNvV2nXMwHeByenb6l8PhIyzxd/C7wIT038LdwOjMf0Nx/S8Dvpcx/A33MffR/C1Io/fPebfv4hrZa9xh/r/uohrZS9xx7Reaq1M58umf5iIiIiIiKAeZBERERGRGZQgi4iIiIgkKEEWEREREUlQgiwiIiIikqAEWUREREQkQQmyiIiIiEiCEmQRERERkQQlyCIiIiIiCUqQRUREREQSlCCLDMDM5pjZVjPbYmajDd/7gplNmdkVoeITEYmB1kopGiXIIgNw9/3Ap4EzgGvrt5vZrcBvAx93968GCk9EJApaK6VozN1DxyBSaGY2DKwHTgFeA3wU+CPg0+5+S8jYRERiobVSikQJskgKzOz/BP4e+DbwHuBP3P0/ho1KRCQuWiulKNRiIZICd/8G8CPgYuBrwPWN15jZfzCzx83sgJl9L+cQRUSC01opRTESOgCRMjCzDwFvnP7X3d78o5mfA58FzgfemldsIiKx0FopRaEEWWRAZvZrwBeBe4HDwG+Z2R+5+9PJ69x97fT1y/KPUkQkLK2VUiRqsRAZgJmtBNYCjwJXAZ8CjgC3hoxLRCQmWiulaJQgi/TJzM4B7gc2AZe5+0F3/ynw58ClZnZh0ABFRCKgtVKKSAmySB+mP/r7B2AXcIm7v5L49i3AfuC2ELGJiMRCa6UUlXqQRfrg7luoDbxv9r2fA3PzjUhEJD5aK6WolCCL5MTMRqj9PzcCDJnZCcARdz8UNjIRkXhorZQYKEEWyc+nqB21Wrcf+EfgXUGiERGJk9ZKCU4n6YmIiIiIJGiTnoiIiIhIghJkEREREZEEJcgiIiIiIglKkEVEREREEpQgi4iIiIgkKEEWEREREUlQgiwiIiIikvC/AYWXKiKe+SveAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(6)\n",
    "Xs = np.random.rand(100, 2) - 0.5\n",
    "ys = (Xs[:, 0] > 0).astype(np.float32) * 2\n",
    "\n",
    "angle = np.pi / 4\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
    "Xsr = Xs.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_s = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_s.fit(Xs, ys)\n",
    "tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_sr.fit(Xsr, ys)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "plt.sca(axes[1])\n",
    "plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"sensitivity_to_rotation_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's prepare a simple linear dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quadratic training set + noise\n",
    "np.random.seed(42)\n",
    "m = 200\n",
    "X = np.random.rand(m, 1)\n",
    "y = 4 * (X - 0.5) ** 2\n",
    "y = y + np.random.randn(m, 1) / 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 6–5. Predictions of two Decision Tree regression models:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure tree_regression_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "    if ylabel:\n",
    "        plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_regression_predictions(tree_reg1, X, y)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
    "plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
    "plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"max_depth=2\", fontsize=14)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
    "for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
    "    plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
    "for split in (0.0458, 0.1298, 0.2873, 0.9040):\n",
    "    plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n",
    "plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\", fontsize=14)\n",
    "\n",
    "save_fig(\"tree_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 6-4. A Decision Tree for regression:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(\n",
    "        tree_reg1,\n",
    "        out_file=os.path.join(IMAGES_PATH, \"regression_tree.dot\"),\n",
    "        feature_names=[\"x1\"],\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.49.0 (20210828.1703)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"472pt\" height=\"269pt\"\n",
       " viewBox=\"0.00 0.00 472.00 269.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 265)\">\n",
       "<title>Tree</title>\n",
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       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
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       "<text text-anchor=\"middle\" x=\"229.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= 0.197</text>\n",
       "<text text-anchor=\"middle\" x=\"229.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.098</text>\n",
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       "<text text-anchor=\"middle\" x=\"229.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.354</text>\n",
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       "<title>1</title>\n",
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       "<text text-anchor=\"middle\" x=\"168.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= 0.092</text>\n",
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       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
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       "<polygon fill=\"black\" stroke=\"black\" points=\"196.55,-164.09 188.41,-157.3 190.54,-167.68 196.55,-164.09\"/>\n",
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       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
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       "<text text-anchor=\"middle\" x=\"291.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.074</text>\n",
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       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.036</text>\n",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f9004ac6850>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Source.from_file(os.path.join(IMAGES_PATH, \"regression_tree.dot\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code to generate Figure 6–6. Regularizing a Decision Tree regressor:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure tree_regression_regularization_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
    "tree_reg1.fit(X, y)\n",
    "tree_reg2.fit(X, y)\n",
    "\n",
    "x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n",
    "y_pred1 = tree_reg1.predict(x1)\n",
    "y_pred2 = tree_reg2.predict(x1)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
    "plt.legend(loc=\"upper center\", fontsize=18)\n",
    "plt.title(\"No restrictions\", fontsize=14)\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X, y, \"b.\")\n",
    "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([0, 1, -0.2, 1.1])\n",
    "plt.xlabel(\"$x_1$\", fontsize=18)\n",
    "plt.title(\"min_samples_leaf={}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n",
    "\n",
    "save_fig(\"tree_regression_regularization_plot\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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