ensemble_learning_and_random_forests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": "## Set up",
   "metadata": {
    "cell_id": "04d60f406b2c49d8b604ea6831267e5a",
    "tags": [],
    "owner_user_id": "eef6e4ef-1a38-4b30-b585-608e4b9b9b7f",
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 70
   }
  },
  {
   "cell_type": "code",
   "source": "# Python >= 3.5 is required\nimport sys\nassert sys.version_info >= (3, 5)\n\n# Scikit-Learn ≥0.20 is required\nimport sklearn\nassert sklearn.__version__ >= \"0.20\"\n\n# Common imports\nimport numpy as np\nimport os\n\n# to make this notebook's output stable across runs\nnp.random.seed(42)\n\n# To plot pretty figures\n%matplotlib inline\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rc('axes', labelsize=14)\nmpl.rc('xtick', labelsize=12)\nmpl.rc('ytick', labelsize=12)\n\n# Where to save the figures\nPROJECT_ROOT_DIR = \".\"\nCHAPTER_ID = \"ensembles\"\nIMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\nos.makedirs(IMAGES_PATH, exist_ok=True)\n\ndef 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)",
   "metadata": {
    "cell_id": "7abe797095504cce9b68ef57432310d3",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3fc1d046",
    "execution_start": 1658458295836,
    "execution_millis": 1907,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 688
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Voting classifiers",
   "metadata": {
    "cell_id": "fb75326b55a04020a42b00a6a9b869ef",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 70
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "4c68117e-930c-4190-b3df-7fc17159e024",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c7454edb",
    "execution_start": 1658458297749,
    "execution_millis": 3,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 117
   },
   "source": "heads_proba = 0.51\ncoin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\ncumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### The law of large numbers",
   "metadata": {
    "cell_id": "6aca92a3def6409395f3a08a2779a6c6",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 62
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "3b8d44e04bee48739421e0d5fa57bfc8",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "11ec68de",
    "execution_start": 1658458297810,
    "execution_millis": 1340,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 551.1875
   },
   "source": "plt.figure(figsize=(8,3.5))\nplt.plot(cumulative_heads_ratio)\nplt.plot([0, 10000], [0.51, 0.51], \"k--\", linewidth=2, label=\"51%\")\nplt.plot([0, 10000], [0.5, 0.5], \"k-\", label=\"50%\")\nplt.xlabel(\"Number of coin tosses\")\nplt.ylabel(\"Heads ratio\")\nplt.legend(loc=\"lower right\")\nplt.axis([0, 10000, 0.42, 0.58])\nsave_fig(\"law_of_large_numbers_plot\")\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Saving figure law_of_large_numbers_plot\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 576x252 with 1 Axes>",
      "image/png": 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cv+CN1RzKr6C40up98imi0+lo2bIlAJMnT/YpM2n2ZqSUdH3yF7o++QsjXl5O0kMLaP/4Qr/z/pCWSdJDC1i8W0sNP5RX3uA6pJTcOXsz13+wvkE5hUKhUCjONmczxXofYBBCpEgp99eMdaVxgbkSt+NnJDBUCDGmZj8K6C6E6CalvPvEU3maYkLt/tWYEqfkuaHDKNtn4VGbg9yXXvI4vj9XUwDszoZUoT/OjTfe6HPcWcec9OQPu1zbh/K1eJ0qm4Nqm4MAo3fczHurPOKj2ZheRKvYEL9r+G5rhmvb7nC6XF4KhUKhUPzZnLUnkpSyApgLPCWECBZCDAQmAJ/WlxVCTBBCRAqNPsBk4PuawzeguZq61fxsBJ4EHm3UQuq5k+KrNAXk/Exvd0xxzcvzU6IBp9XbczUow7sho6PUijWjYevGH6FjRy10qCqsBVV1dM+Pfkv3KX84v8JrrMJiJ+14icfYA99uw+7w75V74nu3jrn+cOHJLFmhUCgUijPK2f5aPQkIBHKBL4A7pZQ7hRCDhRB1n/xXAgfQXE2fAC9IKT8GkFIWSymza3/QUrZLpZSeT2c/NK/M8RpLqHKi92FMycnN1TakVsW3Po9u0PSvFqXZFLz/AQBZz64jd8YWHBUnF6NyIi677DKmTp3KuV1bEixs3BCwge6G49wQsIEbAjYwwbTdQ/7ymb97zXH7p5t8zr1ge5YrVRwgq6SKpIcWkPTQAlf2FcDV7607TXejUCgUCsWpc1aVGClloZTyQillsJSyhZTy85rxVVLKkDpyV0kpo2vqw7STUk5vYM5hUsr3GruGW458T1yVWyFpX+pAJ8FRz0JzVOduFCkFHL/Ld1wKwGsrZ1D09TosdSwVxXP3I+Wpu5mc1dXIOtWC6/Zh6mrIcm1H6qq5pndTpl/VHQCzQcdXG45y1+zNbD1WjJSS1Qe0e7qoe1OW/98w17n3frmVjlMWUVqtKV53fLbZYw0dErz7QSkUCoVC8WfzjwtwCHDa6F3oqLMPegn2ekrML6Y0V0CvE6jetcdrLmEOZTVhxI2bQUC3a8mb6Xa9VO0soOjb/V7n1KessNqvspM7bRp7u3Un8+GH3fNWVfmda1K/WM5rH0O7+FDyy608+O12FmzP4sI31/DCwr0uuWcv6kxSTDB7ano91dJl6i8ApB0r9hj/7Ja+ru3jRZU4zlAMkEKhUCgUJ8M/TokRAgz1lAaDlJ5drGtw6rSXxykEQu/dryjk/FcavFblxhyyX95IZZrv9OTM/cV88shv7Pk9y+fxgre1Mjil839wjfXp08fv9d59912effZZ9mR7JXzxzoqDANw6OJlAkxbw6yvw12p34gw1Uj26KZa+scgAPYXCSfXopjiD9Ax6YRkdpyzE1kAcjUKhUCgUZ4N/nBIDAn2956/ehzsJwCFqlRhA98cSuez5VRR+sQdp937o5xzWyuYs/cTbyuO0+O6RFBISwuOPP+4xFhsb2+j1/Gd0O4/9Fy/tQnJMsGs/9bGfsQ7Qiu3JCBOWofEMWqetzzo4HoDyxCAuXb+PX3flUGV1sO5QAftzyrD6uEeFQqFQKM4UJ/1kFkKEALIm2+hvSf0gXi0mxq3FxN3XA95ZgkOnWSoygnSIP6jE1OK0ONAbPHXGzAPFPmWzDpaQte04gYBEIIWnhqXX65k6dSoLFiwgNzeXK664ghdffNF1fHBKDKv2a/EvYzrH89P2bNcxU701XN6rOZf3ak5RhZUub63E3qbh+Bdrrxic0WbWVVezdcEmhI8U9eSYYLq3iODu4W0aTN9WKBQKheJUaPSTWQhxF/Ag0LRm/zha1pB3k5+/MEII6jtRai0xdhzYcbLnsNYZwaFzP/DtBpMrO3vS8H/z1rJX/V4jZFAs5as9XUiy2g7BRiyHDmNMiEcXGEj6NnfwcMa+IlbN2U/BcXeS1hC9mb2pV5ET15vkrDxsm9dTMnceFWvWkPzdPPplZxN9881e13/72p5sOlLEoDYx6HWC0mobXab+wmU9m/ldc2SwCVuvGL/Ha3FGu5tV2pNCMB7wdl0dzq/gcH4FczdncPi5MQhxai0ZFAqFQqHwRaPcSUKIR4DngfeBc2t+PgSeF0L8keaNfxoC4VWl1+DUlJiFpq18FrCSbxdpJWnsOre689wlF7m2D4cnsjk21ef8zoo8sv7vIu/xagf2oiIOjRnD3u49cNSLKfnu1S0eCgxAXkxXcuJ6A/Dhk9vZ//hLVKxZo63hwovIfellKjdvoXLTJu695lrXeft2bqNLrMHVzykswEj682N56bKuHvPbnJIyuxbkHL9sq8exEVGhZA/vxrzubbg6IYqFPb3v19E6DAnEhZnpmOjbgtPqkZ+w2B0+jykUCoVCcSo0NibmDuA2KeWTUsolNT9TgTtrfv42RJi9myvqkDgEZOuKPcbrWmJ+7dLZ49iGOHdsidNSRtXmj31eTxeiBQQ7q+3s7z8AgGpzBO/ctfyEa93dfqLH/qHk8VQGesa/HJk4kSPXXEv2+PF0idEsKfPnz2fatGknTPFusSKNlFXbOW/jPq9jH3duBUD/iBBebdeCbmFBbB/o3avTMrop6x4ZxYLJg/n6jv4AXNyjqeu4lND2sYUkPbSA8jq1aOrz/vE8ztmwl40lFRyt8h0PpFAoFApFXRqrxDQBNvgYXw/Enb7lnHn0Qu+yxAzI0x6qeolHdlJ4eDaDh3xK02TPjgiGhO7M7qlZPHZFJyGddqz7f6Hi5/uRFb4zkIJ7a8Gwssr9AN/RwdsF1BjyYruztu9Uz0Gbu6heytueTb0bSscGd9+orWWVrrF3OrTkm26tMfroyh1rMpI9vBvHhnTxGL9w836WF5YyvbSYd+7sy+akAH5+ZLjX+Z2mLOLRedupsnpbZh7dn8H28irGbd5Pn7W7eXDvMS7dcqDB9SsUCoXin01jlZh9wNU+xq8G9voY/8tSNz5jQH6NEuMEe53xqCitX5DO7Fl1N7DvndzRfAihJj3jkzoidAb0TToAIO2eCoMs+YrIS1MI7qXpeAWf7Qa9CRCUhrc6pXvwZ18xODyVg/fe818DsMLhrUgcGNyZC+MiGRTpq7G4G6NeR9Ywt2tqbUkFV6Yd4teCUm44dJw9FdV8nF1I+vNj+e6ugR7nzl53lPZPLOTfu49yVdpBlhaU8luRd5uGjzMLWF1czpdZBQ2uRaFQKBT/XBqrxEwFnhBCLBZCPFnzsxh4DJhyxlZ3hqivBOjxTLE2m7XEKxuetWGKm64AIG94PM/0C6PQJNCHa8Gy0u7pAilftoTgXvHoAt2x06Hj3yCgxw0ecoNX/99Jr780NMnvsfHfz6dZM21NhYWFFM/7DuvRo15yzxz0rk0TYvCuG+MPIQTZw7v5Pf5Rhha03K15BOnPj6Vny0gkYO0UiTPEwOfZhSwrLOPqbYe4eKtmcfmpRwq3NvMMLr5vzzFeTc+uP71CoVAoFI1TYqSUc4G+QDYwruYnG+gjpfzujK3uLKF3ut1JQjiIbXIEAGs9JSan44dI4XYLnTs8BOuxtdqOw6r9V3LcdfzwZZdjPXbYYw5ji/5EFGuVfK99uBNGexUjlt9Fv3VT6bFFy3gyWktd8oOvSGHw6gcYtOZB19imnv+h4MnvcBpMrrG4h7X46qCqKm655RbXeNbDD3Pw3NE4K90uI4CNJZqi9nPPVBb3Sm1QIWmIRb18BziDFiycb9Ver2/vHMDDk3rjbBqEdaBvD2SX0CCeatPUa/zFw9kkr9hG/LKtxC/byt27jlBs8x9fo1AoFIp/Bo0udiel3CSlvFZK2bPm51op5ZYzubgzgRCCdqWaKyWpQssQ0kstsNdgqKZlS3dnahsmr/P3nXOLx/55V2vuEkNcGNE3d8IQesh1rHr7dg5PmOA1h80YQmzeVjJHD3WNBVXlEVFykBHL7+LKywNc412GN8dor8BkK2fUvmdd42nLMuCN+QAEDxlM1MSJmJKSXMcH9euHcDpx1rjJiufO81jDtnLN/dUtNJBOoUH8UTo69Rzv2o6sYV0ZWuOGGh7ldkd1WrMDaXeyeNp6ph7M9Dj3ycQmNDUbOSc6jPnd22DQCYQQHB/alWNDu3ooVlVOdzbXNzlFdP3NM15JoVAoFP88/NaJEUJESSkLa7cbmqRW7u/C+Ew7jqwS+klNh9Oh9U5KTf2d6Bi3JaW+Owm8XVGlNQ0ZE597jsCUSMypyZQvqXeOtQJhclfFlaZQjCUHXftNX3+d0qVLqVyfTuTFgwg77zzG2w5SXaOAJDz3HFkPP0ziY4/AHPe8Vpug+57d7v30dG3dOblUz5iB7N2b8pAQwsrKyHz2edaWdKDfUL1HrRd/NVyqtm/HEBODtFg8lKO65L2/Acv+agBMyeF8eVtnhBCUb8+jDe76Mf/9fDNvdfFWCCuOlNHyQDGvXteG2FD3mgx1goqzh3dj4NrdHKyXsWRxSl45nM39yfE+1/ZP5c1lB3hpkTtMLf35sR7HKyx2Ok5ZBMDOJ0cTbDbw/dYMckstnN85nmaRf1yhVSgUirNNQ8Xu8oQQCVLKXCAf3/Gkoma88cEUfzoSAZiL7exJMNOuUmKocSfpDZ6BvL4sMXY/L5mhJr055pZbXD2Pain/6d+EXjjTtT8sJoLMwJ5wcC44bRiio0A3jqB+ENArGafVgW5NJkEA41oRcdGFRFx0oXbynKWueX779gCJbSKIaR6C3qAjsHt3qrZs4dCEiUT2mQBk8/PYMVy3qYRSYaZgVxHf7XAw63wLhDbjjma+2xVIu530yy73GGu9aCGmli0BcJSV4aysdCkwANbDJWQ8vNq1vx7oM1qzyLzV3PM1W7m4jEq94BprBhVA72cWs/GxUUQFmRACDuSWExZoJCbEjF4nWNOvPflWO4F6QbBez4Wb97O2pIKX0rN5KT2bdf3a0yLA9I8uqldSZSPtWLGHAgOQ9NACnru4Mw/P3e51Tq0yU8szP+1m+lXdOVZYSWGFlXtGtCEiyPs9cNZZcD9s/QIeOgr6U6ucrVAo/rdo6BNhBFBYZ/uUWxfXWHRqC+blAw9LKT/3ITcVeBSo+/W7i5TykBAiFXgJGICmPG0AJkspG5UlJWrq7toqFmPVjwNkTcVe7wfgOgZ4jVkxe40B6GosMrrgYNpuS2Nvl7qF5TxfOqMQtAwJg/EzsB1bS9FP7tsUQkfxPP+pxW37xbN3rTvQ9ZsXNgJw1zsjiL3vPo5OnEjwsEeoFKVoYUug63gBwXoDLQJ0XDHIbREatHsdB2ztaNOhA7nTprmUr4AuninUAAdHn0f7GqvP4QsvwpaRQfB5L6ELCPf9egA/rihn3FB324HZFYG0O1iBqXUkQfuKWEQY4ymjCEmvpxczsl0T9uaUcbzInel1Td8WPHNRZ2JM7j/V2V1a0XqV+6Hcd622roxhXdH/DysyNoeTwgor5RY7rWNDeHnRXvZkl7F4d46H3IuXdGFYu1j6PKOZBH0pMP6Y/IXbQ/z+ai2eK+2JcymtthFiNnD7Z5toGhHIvC0ZPHdRJ8Z2SSAs8DQqOk4npK+CFv1g5zyYd7v72Puj4NIPIaIFFB6CmJTGzSklHN8ATXuC7m/0fUuhUJwQv0qMlHJFne3lp+l6bwJWtNoy3YAFQog0KaWvAIevpJTX+hiPAOYDNwJlwBPA90A7H7JeyJpnnNN2wNVGwOKUWOs8+9bRn0AqKRXaA3q8nMtPXABAtS8l5vqJ6KPcHjedqfEf6sbm/ZBVbiWn+PuDDUjDyOvbM/zadrxz93KvY8F93R2uY6Q7LuXjgBW8M/RCL/nVm7exevM2Hn/8cQ/rUfW2bT6vLaUEhwNbRgb6qDboAsKx7J4POgOm5CEIk6awiEADxvgg4g+XuhSZ84pyGHnxaACcVgeZT/wGwA+EMoJSrMCSPble15y97ij3jkqhSag7TijYoGdD/w5M2LyfTIvbetZ0eRrLerelfUigz/X/FSmssBIRaETnoy4PgNMpuejt3zheWElBhdU1fuvgZN5dddhLPjLIyCU9m6HXCQ4+O4bWj/wEQPOoQAIMeiYNb815HRMINOn59Pd0Kq0ObhncCr1O8NbyA7y40Pu7QNenfnFtR4hKtksjJgR7f/6QvT97rzkxMZHhw4eTkpKCwymx2B2MemUFZdV2Pr2lL/uyyzi3Y5zbyrPvF/j8shO/WJlbYHo39373a2HCmw2f890k2Drbvf9/ByCk8Q1TFQrFX5tG2WaFEA6g1rVUdzwayJVSnvDrjRAiGLgE6CSlLAdWCyHmA9cBjW5dIKVcj+atqJ33NeAxIUS0lPKERUV0rm/qknKjtl1ll1QhEQ4tBma68Ex7NmPBIQw4pA4LAdQn6v77EbpGx0ifFNLuRNRp2ih0Ar1OcMeMYbxzz3LXeEWJhcBQE62XriN3ehoCwVBrB1aYdvk0oQ044P52vmbadFpdOAuAgp8mo7dZWTX4VQZd2Y52HQPIef55yn5eiC0jg7xp0wEIGvKAtj5bEbZDa7Du/g5Di4GYkprQctazSIeDI7e+S3yTjiy+6xp0TieVyZ8T1L07OpMeY9MQbBlafZilhHEz5SSh53ECuZcKdElhbEgvAmDBtixuHJjssf7mASY2D+jI8sJSnjqQya4KzbX15tFc3ujQ8tRe9NOAxe5gxpIDTByQhE5AVLD2wHZK0OsE24+XcNfnmzlaqGWN7fnveQQYtbdRtc1BucVOaICBR+ftIO1Ysdf89RWY167oyoXdmnq41PQ64RUTU5fr+id57E8a1oZJw9p43EPbxxa69hN1JZxr8q7uXJ/MzExmz57N59Xdsdb7iLnwTa1txgPfavvbH+xNaEMKTPJQuHI22K3wUr36Sls+gzbnQMcLwW4BWyXMvwdaDoTASE8rTi0v19xfzxsheQgsngJXfQVxHU54XwqF4q9HYx3M/mz0ZjTLSmNIBexSyrqfgmnAUD/y44UQhUAW8IaU8m0/ckOA7MYoMHWRSJY0MWI+ZsHiMOIU3j2VaqnUolMoJ8SnO6na6fQaM7drh2XPHqzGENb3eoTzT7Sg2uiiemQ8toaER/uiDzUhnZKypUcJ6hVHxdpsJoxpyfc/aenga6f8TpGEfoFufXJ3+jxIbcvMelaYa39fSIjVHc+ytLSEVkCRKGfexVo2VWy2kdXfHqLziOEEdOtB2c8LOTjqHACMbc5xnZv85Vvow0w4ysvZ16s3tqPw4R39sFmdDFr7JjpDAELqsJpCOXLV1S6XVNw93bFlV5Dz+mYA3sftdnqzQ3Pir++I3eGkzaM/8+QPu7yUmFqGRYUxrE8YhystjNiwhzzrn5t6nV1STXSIiclfbGHRzhzeWNa4qsNfbTjGxAFJfLD6ME/9uMuv3Jzb+/PZ2iPMT8skMsjI61d2p3lk4BnpFm426Dnw9Gh27NjBsmXLKCkpcR2zCx1hTZoww9ie5pYCSo9LrNLAheYdBAnNOnZ1wBaWWNuQ6wzBgoEArB6WzGG6LYRO02povma7hBmOi3jTOI3jHSexfNs+Hhjdlg6DJqATAr1ZwNQSTZnJ36uVNHh3BHw9Eb6ut/DdP3juX/UVpI6GGT2hsMbSuelD7Qfg7f5u2XvTIDLpdLx8CoXiLNCgEiOE+HfNpgTuEELULa2qBwYDexp5rRCgtN5YCeCrPOwcYBaQg1af5lshRLGU8ot662uG5qL6t/cULpnbgNsAYlPdmSxSSiIDDpCpC0QnA3EK/0E/Pwntwf4O97BN9ADgHpuZGUYtlqXC4a3EJH31Jc7KSuY8sAirPpwlpTYCdIKBIb5f8pibO5H/3g6fx8p/yyR8dBL2/CpKFx+ldLG7eN24G9uj9xFDs6v4d6ocZWSHJLjGhh3fyK0Hm7EKTYEZYGvLb0bNffBewBKvOZwOydqZ28jd2YyybvfRY+vrAAR00r45hwxMRB+mWRj0IdpDND+mC5WEgAmWDZlGt7TpbO062TXnuhu/ouWorgy8pA2m+GCMzUKw1Wt8ad9ViHRIDHrNAmUCRj/0M01Soli1P591j4wkLiwAaXdiOViMuVU4yUFmLouPYl5OEb/kl9AswESHRriVjhRUMPSl5QBMu7IbE7ppdWpyS6vZk11GQngAKXG+KxjvyykjyKTnWGEVV7271jXuSAhElNsQAtALzfzi9PwmIHUgzXqSg8wcKahkyvydTJnvO238pUu7cFmv5q79PslRTL+q+wnvrZaysjK2bNnC4MGDvYKf8/LyiIiIwGj0zsT78ccf2bhRi7my6fS878MlCXCIcA7ekISVIFbu78VTP+xivGMNEohso6NbwSGutM/np2aDmXroTQx9buPDPXpuKnnDNcc0xyUA3Gn7F859epzJA1i9uAIW/oyoeWM+f3Fn+rWKJjayPcFmA1zzLcy+xO99p41+i7J2FzAwIkS778mbwVYFK1+CVa9oQk17QsYm90nTusKNP0PLAWCtgLJsMAZCWKJbprYnWdFhmF7/91Dn28jj+bD6dVj2NNyzGaJb+13rGaO6BN4bBZd/CtIB0W3A4DuuT6H4OyIaahIohKi1WbcEjgN1a9VbgXTgCSnluhNeSIjuwBopZVCdsfuBYVLK8Sc49yGgt5TykjpjscAKYLaU8pkTXR+gSdsE+cv/XUnMwYv5Nv1VOt22DafdxPT0D9newsRL5fcSFZXJNeJb1zkPffQW70y8kuJ6WeafVwXxdkwOqypCGRwZwtfd2tS/HABv3rHUY39ChPawiLioDZZDJVSlaT2XEp/oR/YrG3FW2F3H6wb4BvdLoGKtu8pu7W/Nn4nsq8MvoAsdywvXuL9l3r7iO5f8UGsHUpwJPpUXgLCi9pgtsa717qpykLTjA/TBsZg7XAhAzOP9qK6wERoVwLHdhex59BUOJp7jc776SJxc/nAfdq3JIrJJIIl2B5blxz1k4v/Ti9EfruPjfM269DRVLMTGFZh49skRZE3R4moMcUHE/6snszMLuH/vMa9r7RnUiQijb+Wx19OLyS9vuOFkq9hg9uudYHNy0ahWzCku5b3EBO7+cCPVozWlx5hWCFYHzqgAHK19Kz0moJvJTNqPh7Cc6y7qF1tip2ytOzj3sbHt+WVXDusPF7L+kZE0CfN2YR47doyffvqJsWPHuio013LgwAE+++wz7rjjDnJzc5k7d67H8auuugqr1cqhQ4fYssUdyBsdHU1paSljx47l2LFjbNqkPdyLA4P5sk/Dv9cehfv5uH8nYhPbc7CymoHrfH+3aWIp4L20JwitqqQ8LJD72k6lrz2BSYNakRhqJvXDNdhTvYPFRZkNUWbDuL3I9Tc8sE0097Sv4tdNO3Amduc/Q5phjmnBt7nFTN7tXaX6giYR3Nsyjo4hgezNOkyGLoQSqeO8qr0EtugNc2/VgonPFKOmQrdr3TE5Zdmw9GnNqrTtK7dc2zFw2cdgLYegOp87eXu1oOfkYZpiFV6vMGRWGnx+BZR5V+P2YMRj0G4cNGl/Gm5KofCNEGKTlLLXGb/OiTod1yxmGXCxlLLoD19Ii4kpAjpKKffXjH0CZEopG4yJEUI8CPSVUl5csx8JLAUWnejcutRVYr5Jf4XOt2lxIdP3zWZzKwOvlPyb6OgMDyXmsfdnUH1TKS+LRz3mWhCXwKzsN/heXIIOyPRT8ba+EjM+3IBOCJo+N0gr7PbQKgCaPT8Yp9WBs9wGOoEhwkzR3P1UrPcuuW8TMGZoMNem25iY7u3Nm1XwAWUhTfjigqtcY9f9/jPBVvfD+qbq4ejQ8ZF5OXbh3UcJIDFnMGPCfQcp765ysM+iWaC6jmpO2mJv5cEfFSHpVIZoD5nY7CGu8T7jk+ncLpL8d0+cTVOlg8B6BrC8K1pzfrF3cHAti3ul0ik0iMIKK0a9IMRsIPnhn054LUdCILYu3qWSjFsLsHWLPuH5jSGgxIbclE/H8a3YXFHFnK6tSQ4yYxQCk81CRFAQb/z8K+d3as/sDz/wO89tt93GrFmzTsuanMBFF1/M+QXuF7p7kIkJvyygLK8XQVaJBJ6+osEyUqcV/YFS9AUWHHEBOJIa7vHlj1iTwcvt+GByPNcmRtN5jWYN27nmAqLtJb5O96TbtTD6Gdj1naZkrH0LmvfVLDe1ClFECyj2Vqr+cox6Egbd92evQvE/wtlSYhoVEyOl9G5JfJJIKSuEEHOBp4QQt6BlJ00A7zxmIcQEYCVQDPQGJgOP1BwLAxahWXUarcDUWUnNv27lTSclTj/ZIVajkVBrGfVDYXSxJQRma6X7vZ1J/lleZic2UE+zGrN+s+cHu+c06dFFuWNaIi5s41OJ2R+qo8isY0Zbs0uJmX/0TfrGjmN7ye/Mmniv1zl1FRgAXU2x5gnW3nxrXoupxAHmOMaL1nxr1gxrmXGrWG9vSR+7ZmXKFSWUiEpSnAkuBQbwUmDa9GrCiOvaU1FsYeWXeynOqeLSh3ph0DmY9X+/uRQYcCs05qpY1v8Avccmows2uCxSdTmiy2OfPosQaaaPPQWEzsMHGPvVQZ5MMLDcaWVtfgVBTQIp6BLpOj5q4z7iV+VSXKnFbFzaU7NgDGgdzZtX96D7f38F3FP+/tAI+j+/1KcCA/hVYC6Pj+TahGgu2HKAu1s0oXWQmX/tOUawXufhepzWrgUvHM4i02KjOtwIIxLYXKGll1+e5iNLLSiOZw8VMjS+Je2zj/i8ti8FpmvXrgwYMICVK1eyc6eny+oSfuJYyg3sTc+kxOb+OPii9yhKgkKYVaPAhFU4uPdH7aHuoCdBNa+SACYuKeXjkWEe8zYtsHPDklJ0Eo51ria5dwJbV61kXg9/IXBu+h/YTtcM7f6btGjJU8lul42jTRgO30ZPz+sfruCluBi6tYjk5aoyjmWX8WuNzuMrbuqFw9m8cNj9Xus4cD43U8aDYSkEhxyheEMgVTtKiBwTSUCXFlSmlVDyczpB9jjCHUFYDGMJHD0Rcd5z7kkv+8i9nbsH3urrf8GXfwLVpdB+PLxwCoHprYbBBW9oylS3qyEgAiJqXJFOJ2z+CH78l+9zF0+BXd/D+GmQ4F1mwQMpwelQdXsUfzqN/gusqc9yKdACPKvASSlvauQ0k4APgFygALhTSrlTCDEY+FlKWRudeGWNnBnNjfWClPLjmmMXoSk2HYUQN9SZu4OUsvFfd+pYoKT1OJCMTnirIxajiZSXnVrVmjrsPfAc57COL7meW5rGeJ3njzInXPJEv0bJCp3A1DwU67Eyj/Hr+7trvTiAw6WbqXKUszz7S34c6ZnpEY6TW2ZOpSK1G9RkUOmlk++KKrkwMohIGczE6qHMzXyFgMh/k2HWeShs2wxH6B7fgaKMPOabtfiIQxU2IAFf6AyCURM7oDfqiIgL4oJ7tQfQoUOH+OSTT+jZqxn5dbxGtQqNJTAPWZKKpcpO4uP9qUzLpTwtl5JdOQRiosnjfXjvJfcDYqdBm+RIdT8eIIjQGifD2Cw7Y9EBoZAF42wF5CaFuKoUF0QYiCyzk2zXsWJ9Bk2k4NXLu/HE0SyXa6iWKXszaD6yOQdq1NSpzeNYlF9K0O4SliS53wJrFxXzU8hRkhyRJNnCCbwxjI9ff5lDDzxAUE39oKsSNIVnbk4RU3YdZtyan5nQ/34uFkl8kHaYKabGBySvaNudQHNz/rvXRI4oJjOhis2Fuz1knnjiCWbOnEn37t3p10/7e7vsvMFcttOzbYaUsHJVO0y0o7U+m2x9Aq9cFEl9Jv3sbZW4+ZXBBAQbkVIS+MAKqs2aUhle6SCsTtmA5tsDsG8vohOdaZ69HpMtnMwoA0URubQo1NxoVr2RDweNJayqnC4ZbgUu9+gR7jh6hHvvmcwP5Q4eOJThOnZtZARzS0r5oHMSLQPM/LDpGGN+ziKg9m28L5dKcplUs/sckBEomDAkhHv2WpiYbkUC/+kWwPI4zW0aU+2k3CCoNgjeJ5T3S7OZssbJ+Ezt/gt/KuLwihJMTonZLIjdmEPlxhpXYE3EXkDbSIL7JVDwsWeAduxtRzE7NlAkWxIeH4kuKAwMJkqXHUUU6rEcKKZ6znYC2q4hqFccxoQQiubsJbhHOPqIEArnHMAQF4T1cClhI5oQav+I8tIBVGcYiJkQiGg1BKfFqTWdratM1aLTQa+bmFY8mO/TMnh+RAQ9K1Zii25LQEgkvH8OZG6GmYNBZ4BhD0GTjrBmGpzzJCR0BUMAHFjiGYvUrA/c/AuUZoLeiPzmRmSPG9GZAikLaolpzUuY934Hse2h5w1aynv2NjKbjWFJh2eZ8sMunBKGpsZSZXVwToc4bh3SivlpmaTnV9A8KpDeSVE0jQhk1spDfLr2CDcNTGZY21iSY4L/0UUuTxdSyr/t69hYd9JY4FtgC9ATrcBca7TH3Sop5QVncpGniyZtE+QPTw6k6dZ7mXP4Bbrern3wv5t+KytansMbxZOJiMz2cCc9/t40Uo6lc/2Tr3nM9UXAYzirdnMTs7koLJMnU1sTHtbVQ8ZmdTBrslZup9OQpuxYqX0A3/HGMPR10qYbompPIQUfad+ef0g0kFzh5IZ+biXmvs/fwlCaiQBMYTfyzNWewYOb+rbjyxsvxWEOpDoxGWdAEEEHd6C3VhNjbsrIxGv5Nv017NLK5LbrsOgisbT5jn279rHItNU1T4A9hGqDOwC3ZVRHrr3jYtf9BYaZqC63MfG5AQSHe5qtnE4nTz31lMdY6/0HOJji+ZVaZw+gVWA/bImHGTNmDG+/7S8hzU2mI5Ll1jYIYInQrAGyxs6mqxMxNNBcjmWYpnj9Z24RQkpevKTxbpBvFhWSVNOGorq/IKNVS+4+nse8VRXMNa6mUviPq3nkkUcwmUysXLmSpUs93Ys9ba3YZDyExWDkYGwi7bOOIID84DCCrBaMDjs7k3qwtnlTzsmyEmy18V1L9+9/w6IyBJB4M+jiWkNoghZwqjdT9EMGFTssxIS8RFXgGIJL3sEmm7Lb+BDh9nwCrZry/XWFne97B7Onuafr8L7vi8iONNA8386VIY8SM/wi9MP+hc6g96prY6m0UVFsJSoxWAuedVghIJzinEpmT3EHPV/yQE/iksPY/MsRfl2yCKu5gD6tRpORnk9M0zCOb65CIqkIPQRSR1WIp5UvKyyKNrl6QovbUhGQj7k6hiGXphC5+IirkKU/7qMCJIwvMzEyzDuQ2YLkKE6EBEeYkWsGBPuYxZNR2TaeS6s+wZW9WVlmJ1wv6Bp0ZorvZV2UTI9OceiD3fdZm+3nj7UPDCb6m4swZm485etbMPIJl5Dh48vOTXxJFCWEoJUXKJQhPGG7kR+dWvxeEFZSDXmk6PPIdoay0x6PDkmBDK6pt+7Nd3cNpFvzCN+LkRL+Cg/okgzI2QFJgyDtSzi+EQ4uBUsZmIKgQouPpOeN0G8SxPpvrnuqSCkp/Gw3VTt9J/XG3tYZU/NQhPGP/33+1WJiNgHfSCmfE0KUAV2BTOBT4Hcp5atndpmnhyZtE+QXbwfRevl05u5/m663akrMt1zOXHEFn8lLkOi4TrhzNhdOvp7ikDCufPYNj7m+Md2NxZLFnbxPL9ZxM7MYOcLTBfDO3ctx2J0Eh5u44YVBvHXnUqTUquv648LN+ymyO1jRpx3ri8upcjhp8/p2ws5pSYoj3+c5QZVlXDf3HXIGjOS7Vj08jmUO6wpfXctr83z793uNv5jCjGMc2ryBJuZyrkhKwyj0ZFpmssGwm60G/xaCKVOmUJhZgcGkJzzWOxNISondbufHH38kLS3N49j1mzazsFlTktLTGT5nDi+88orf69TlnCHj+P2HvZSH73eNhRW1R0gj35gC+PbxEbzz2vPauDOQc21diZDBxP93IM2XbkWa/CuPUWUOrltWSpnZyQej3a6i8zNt/Hd7tZf8Lv1xV3bXibj//vt5pZH3eJ61G9v1Rxlu60QARoJ6xhF5SQoZj2htHZ7pYGZec9+xSgCfb/iJ1MLBXuPTU018kuxWMKMtTgrMvl+POeueYEj1Cop6P0/kqOvA7D+Fu3xtJsXfHcTUPJSYmzqhCzTgtDgoWXCIivXZGOKCcAxvjvxyL+Y2EcTe0hkAe5UNe0YFVTvyPYLWC+xOjlqdOKSmkKZHbqREV+nv8i5GWbtgqo4mw+Yk9bIUln9+4po2wTqodELv8cn0GpPET29vJ32b+322JdnEj30al77e95dcIiJNPFyiJ8QBnzoslFgkHex6Wpv1HGtq5HCIju+aGnlyRzXtSuzki1KayHB0CCxOSa5d0rzO32ih3UlUzReeudJCC30eh2UI42QQJgzoECdU3uqyBBuvU03P9rE1lZ61c5N1BQw1HfKQvYE5NCObefIc+oo0imUEu2hPd5nOFNs4glIHs3xvDvNNj9Fal0UxYcRQyEvyVqzizBScXGbqx4C28WxOLyA7v4gSGUDd9Iae4eU80dNC+O4vaFKxlyCr9pDOkNFsm7CYcyNLyf9gHzabp3IVc0tnzC3DEEYdtc/EP2KZcFbbyX1jK/Z8zS1sDCkhusMmDNu0R6RDRpBl+RDq9OUziHTsMsljHqPYh1NGIISVSOMMzLqdMPYVKk0XYA814jDosC09jiOzHKfNCQ4nwqQnpF8CTqsDe341xvggAtpFYYwPRmfS8+QPO9m2O49phY1XTESAAWl3gv1kgiY0mr8w5C+lxJTjLvtfCAyRUu4QQnQGFkgpW5zphZ4OapWY+B03s2jtarreoj2A5nEp34ir+ERehgM9N4ovXecsu/MqikNCueglz1iDL8S1OJ1V3MdbpLKHSUz3UmLevGMpRcE6QnU67n1pCHabFkBr8KfdFqUTv7UYgOxhXYlfrj34f+/bnqOLvueKCLdm3ubwbg4k+84uaF9+kN0hmkUme3g3mDmU3UWR/LRFU0g6DB7OrlXLABh1yyScTidLP9Aq9rYJzSfaVMm6ghZIoaO8nadShBS4cl4BnU7H448/jhCCdevW8fPPP3P//ffz7rvvUlpaP6PezdSpU9nTpSvSakUYjcy5cAJOvf83V3R0NDfddBMf3e9OhMuLX+klF1TWkspQz3iRQbZ2WLGzJjSA9/v6DqjoeXg/g1fPw1ihrTl44KP8FmPglwQji5eWE2HzfJ9YsfNJgKuoNeacY+grSol0BtO7+WVEyRAWWH4lJ9zTY5vUtAVXnXcpmW9u4uM6519s6ctGw0F62JNp3rU1ln1FhJ3TkpD+Nam9ljKkKYSSBYcJ6hlHWqBk/JaGa9AMy7GxKtaAQycYkW1jaby39aEuz5vDOL9VLLFNgtE1snhjwWe7qNrh+W1OF2LUAtRPI3a9E+d1zdn96Ro2GP1XtQ4sb0ZweRIC/+s3BxmISw7j6M7G9a216+C1CRG0PW5jzKYKDDWf56WBgmkXeLveAJLy8hmzKZPwsiYcanaUr/r38SlXS8/0PRQFhzLgwHYSs1MxWcM9lBMpHBRHbsdu8v+e+snSnlwZwjkYmIJnI08HTvQ1r4kNu8ff3h9BAg5jCMJegUOnxykEeaERBFotbEjuQKDVwiiHle/Sw8mvsPHjPYM4mFfOU1+tYbzZfx2kWpI4Rld2sYo+FBKJQ+jQS98P0vaJQ0g55KCt8U0EkkLb/6EnFwdNEFQSZviCSsdwbLKVz/MbRCcI6ZeAdfcegss+BGHHFtCTQMvPmMReqp09KLA9Qaj+a8ocF4KPpsF/VbZg52mqyPWIEIVoBA8QwMBTvJezpcQ0NiamDFylarOANsCOmvN9v4v/wthNJR7WRV1N5rgDPQ4fvSzNNu8MIKdT07QDqPZZxbeWN8ZFECkF99KA8lLLtK4wVPtwqVVgAO5I20PPhT/DlW4l5tIuHXm+zPebOtZWRKElH4fQwW9vQNZW2icPIWD0VMJimxDdrIVLiTEFBBIc6bY6HCiLofbRKKST4fnfk9n1BgwRiZxzzjmYTCZefPHFOq+Dk507d5KamsrPP2uman8WhyeeeILjx4/TooWm8yZ/9x2HxoxB2mxc+vU3zLnyCg/54OBgQjO7E98qnAl39yL7oNualNKrCXLjIPLjV3ucU1+BAVht1NJ9jdXQ/0AVv7fRLAFjt60h0GohuqIUAVS3SCX6cAZ2aWNcpp1xmXauXL6IhSKd8oRmBFhCCcqPITRsF0dC3d80g/dvQ2fX/kbK9TYiw8LQlcB482gKLGXMM7sKTDPiYBvy3kzDiIFbqkcSdXU7HE0FjmILiZZ2xHRIhq9vgICV0P8QbJujpf4C4oI3iBh6LsWZx1j/ZCa3h+mZeX44IVVOEovs9DpiJS/JzK8J2odPbZwH4FJgBu6qYsjOKn7pHsSmNtrf7dAcG69srQbKsJFBZs05iU8NQBh0CH9B75nlXgoM4KHABHaLpWprns/z6xI+thUBKRFYsyoo+srTuhVxURtC+mrfnFs904rRdrt27bU5LN+2hhJRSVyfVmzYtJ6qkONUhWjxUgadEbtTW8vAvkM5ciADYXLg1FnpPX48YyZ1Zvv27STGJPH101rRRYnkosfakdhUUx73b8yhutyO8ev9SKenIhtWJXn8q0IcAj4fEkJ6vNs6lh4bw1vn1cbKeXZajy4rpiA0wmNsU5LWNeVQrBaXFVFRRmJRIWM2l1Mevp9qg5G1rTqyJyEJgNTso4zYW7tmjTHm3ehSO9Mp1MTx8BjaduvAqzPeQG+rcF3HIQQ66e2UkcD51m7EOSNwCMFu3RE2Gd2WGScCKQQJjlBydCX82GUAGZFNaIg9wJDOIVwXEcqs0mLmFBXBuamM6nY+gyK1KOuiaguBSJ47VsDM49rfiR44LyqI5fn7WajzjFNrVphDTHkJ3Y/uw+zQvpTtzlzJ7gDQckUAvVY2YqS1M8nOJpTYb/aYo0j/OdmY6BC0mFh7NtLRkjzrNK/123GwV2TS/PdKwmQYVu7FCehsUM4wD9kyhzsWMcLwBsH6hVQ7e7HP0Ydo5xgP2bepZg12JBCFoAk61mGnuMZZJoEIBD3QMxwjw/0oEyH6bwnSr0BiotrRE7NuN0V2LQJMYENHOVbp+UVXUIFouZlViZ3Zm1PGLUd/oLPuMD+Yx9P3/GvontSEwS8u40Gq0FHF/QSwJ0iwxWYjXq8j3+YgNTWap8YkMWflNlro8uh48FPalK3GKQzo2p2PDIpGF98JXvC57NNOYy0x3wE/SSlnCSFeRGsf8AlakG2ulPLcM7rK00StJSZ63yUsW7OdLjdq5uYFXMDnYiLvyWtwYOB28bHrnGV3XoVDp2PUm7M95ppdU7JmCs9yQLTlUfkE94yY7zrudErenrSM/9akn2b7ScGu5VClhU0fXcs97R9tUM4lP6QLrVb67nF0bOUIJAKJwCxrHigBEfCQ+wGffzSdFbM/ZPy/HsIUEMiaOZ+x9tsvfc4H8O8JIYiL3oHACOx2O08//XSj1mk0GjGZTNxxxx2EhnqnxO5u536TSSCnSSqrhwzAYagioqAbRluY1znn3tyRlN5xAKxduIuDW/JoPTyAhQvd/v6Q0tYYLVEUxW7wuS6bTo/R6Tu1vJa7u0azudLEb/v9190I37+VDoOHs2PZr3QZeR7n3HY3TquD7Jc34CzVXnsbdqzYCa6n7Ibd05ZvX5lCYYZn3Ef/2Dx+z4vFFHY9HULS2F2puR/NohyLDEaIE1tJvh4Q4hXjctnqMtpl2LhqSl8cNiexLbTfh5TSowN5YwjsGkv1rgKkzUnsbV0wtwrHUW4l62nNUhZ5RVuCu7sfck6rQ2ufIcCeW0nOa5uJvqEjge38xyU5q+1IqwN9WOOKs/3+++8sWrToxII+GDZ0ODk7bezO93wdbrzxRrKysqiqqqJ79+5EREQAcHBLLsERZuKSwli2bBkrV7qtgsWBIXzZZ5THPN2wMSXAwerVq2jatCnJycmkpqYiwsJ582gu7xzNpeG/xpOjeWEOKTnHSM09jk2npzgohG97upNML917hO9TWmDzo6CaheD5LEHvXaWMGRVGqY+q5P7oUOKgwCTICTwzrVgaIqyqnNJAT/dfz/Q9xJQX07woF4fQ49Dp+GSA7xrqRqeNYQc2UWiKoCAknPQY3wkMLQqyeWSfkcAKHR+EOGkSbWBhQgBdczfTJy+NRQH9OJATS2J4AAWVNv7TMpaJo1MxNdPec/nlFs55dQXhgUZeuKQL3VpEoANmrTrMsj25bMgpxRlkwNY7BuxOjNuLcEabSWgexh32I3Qq3k+XkVdwYOdGDhpTSDtewpaMCjKzc0jRHeewM4HXQz4mQeYSL/MwBUdCWabHPUgpcGCgyhnOpvAUruz5vOvY1fnrGZ0xl8r8ELKKL8HuiOTCiGfRh2TxcOq/iKsspVd+NpbqeILKwGKLIMpeSLqlJzHGw+TaUrh75qi/lDupFRAipdwmhAgCXgEGAvuAf59UVtCfSK0SE7PvUpb9to3ON2hKzELG8qm4iZlyIg70TBLuOhzL7ryKiec+wtGLOrvGdNLBp1wOwLNMYafQ0hFrFZVyu4Ppqw4R+M0xXri0cUpM8vItVMnG+WA/CDrKmL4XsLywlF8XvMT7zTSF6vrM73nx6ie8swcAJv6g9YppgB9efY5967TeNkOuuZGVsz90Hbuq5VYSg2qypKZqFpGCggJmzJjhd77AwEAefPDBBq9Z+PnnlP36Ky3efZe8N96g4J2ZFIW3ZkOvOzA4gnye4y+mqLq6mu3bt9OzZ09yDpUSnxyCbdXrHFsyh3Wl1xEUGkqcM8IjYPnm6hFIJBbszA5Y1eBa6xLgtPGgeBNRx7VGu3Fw4Vsg9FCRy4EX1xFAM+YeeZ1gQ7gWSH3NleQV7WXuV/5rvbjQhRIQfivVRd4hZ8bg84ly/kDL0Tew/rvP0JsSMAa76wLd9c4I7E6Jwc9DyhfS4cR6rAzrsXJKFhw68QloRRgjL2xEzvNZpLy8nPz8fD766COio6Pp3Lkzy5cvdx03m81YLA0XOPTHoEGDGDFiBA6Hg48//pi8vDzXXLGxsdx+++1IKTEajVQ4HORZbCQF+bfU1sfidLLq6FKuPxSJU3haboNkOf/Hc4zq8ixDtp/4c/tMIoAjQ7tQtTWNoGZNMURHYy8oQB8egS2zgryZOygyCib1DuRokI5mlU4e3G3B5JQeiQm1DMizc99eCw4B85sa+SLJRITVyS/LKqjWg07CJwMisaaE81HmSXWY+UuQGhTAHS1iuTohmhyLDZ2Ab7KLmJNdyO4K73i7P8JdLZrwQDMjOp0Zo7EmyUFKNpdUcqCqmt1lVUQKK0sO7GZ9gGbhMtkkVuOJPyOCq5wYnFZKgn3/LeulnaYcp4xQbmImP3EBy0ded1aUGKSUDf6guYzGANEnkv2r/6B92ff5E/rvx+Q3S3rIr5b0kqH/fsyvHCC//P12uXhJK7l4SStpTknxK5c84AIZt3SLjFu6RW7cuLHBOaPeme2SDRx7sV+5LolGKV9sI2VFgZROZ4NzzhwXIOWUMCmnhMmZ015qULaWytIS2b17N79yt/YwumRPdE8bN250yd56661+5Xr06CGllNLpdMpdbds1OOd1I++X0lopZUmGnHnnsIbvqebe5ZQw2SNB51fuok7D5SfXT5LHn2jT4DoBeeutt8rpdyyUb9y+RA5oN9b/PSXopJwSJrMfSJBvXXVOg3M+NTJGlj0SLTfdO1he2rNzg7IvXz7W9dM0MqzBdZ7u31OXlh1kwRe7Ze7MNHnswZUN/+3NnOmac+bMmY3625NSyh49epzVe+rRo4dcuHChnDJlipwyZUqDc44bN84lN27cuNNyT2PGhsrMzG/l4iWt5FtvN21wzrfebur63BkzNtSvXEBKG9dnSdzSLQ3Oec+/4uV3S7rIX5a0lvf9K6ZB2fLiKllps8v0a6+THcxmv3KXhYfLXW3byX3Dr5YLJr7b4Jw/TnxXHntwpTz24Ep5ddfxDf6eajlwoOHPsscSEuVvPXvLTZ27yvta+/98BuSUKVPkE1OmyLteeFk2j/F//z06dZHT7pwkvxs/Xr48YFCDczZ9/f1GfZYbUto3+vcU+u/HZNzSLXLw59+f8PlUd05DSnu/coFjL3bJRb0zu8E56z6fksf2a9Q9ARvPxnP9hDExUkp7TZG6dmi1Xf5n0WJiThwm1NqQT210RkPy2ZEGvJ0hp4ZBOqAiF15MhrEnyHbpdRPIz7Rtc+OqmwaGhp3YXVGWDaHxDcv8AYQQBA8ZDHv9t+Ma0K8Ynqm59rET9x6VTqgqMEFCN8ja7FMms/IAFzbbTmjKUG4ffxvvvvuu3/luv/12qg6FkbakcRWK4wIruDN1natWiU+Z5gl8Wvw6TgcYgn4E/FcsvvPd2bx96zUnvG7mvj0semcaO5b9yvHCE1eeldKJ09mwdcIQHUDUle3cA2fJ530mkdKOwTiJwUOgU6cZPPmkf9mBAwdisVQg5elNi961+z+Nkuva9X2MxpmUlqY1KNeco7wtL8GKiQWM580GZCMMNxJTNprq0BOX+lq7qSNOuwndjVasvwvwE1+d17Y1m0ZeTsKoWRzZtwI+9i0HWqp5eoDmdj1e5T92quL4Tla90wZrqjzhOhFGAoOqqRphx3C02O86AcYf/pzADTpwwrKyMr9yrY8d4Zyl2ueNrbphy8n1uV8SsWEblZUR/JCVju9PHTDZbFy98DvuGn8Ony7+lQb+9Bh9dBVDNpfSrvMifpIFvN7gChpHR7bRSi7hBt4lXZY3+Bk1Wb5MljyfnmzgO3Zy2I9cSHUlATYLqTnH+PU0rLExNNadtA54VEq5+Mwv6cxR1520Yv1WOl6rhbCuYijviMm8JidhR89/xAya/7afTz59gql9b2RdQkeCxrWg0OYge3g3lix112KpW1OmIbKHd2PGkRyeOZTF8aFdMegEi/JLmLjd+89h7a77OTd1KsMXz6fJ6GLeFXcxrnIe72143VOw5SA4spr4mmDg7OHd2LvvKY4f1z41hnf6Ed1bNYX1pjaihLofXrlinPuSwUVcOvFCGNq4D16f2K1aMS0fGTDVe/dyeMKFADSdPo2wc8+FqeF/6DJOu2DvNwlI4HhUKHubx9H5vAk0OdiEI3k72FWs9V6654PPKZvzLbkvaAHLgT170vzTj3FIB6XWUrLKs+gc29lj7vr1TwAi44MoytZSgSOamLn6se4IQwBs+RTn95OZcWwq9kot6NAc8S9XCmeP0S3ZvEiLV0rpHce5N3fEYbfz1q1XkjhoPwG6roy54WOPlE8pJUuXaW6c1q0fIH2VYNMP8/FFx+v2YQxyUHw4lNy0KKRDUJWvBSbfN/s79AaD62964MA1BJhPv4J6MpRYSsipzCEpLAm90KMTOvKq8th8/CfObXMdOp0eh6MKIfTodO64H6u1gOrqDMLC3NVmHY4qlq/o9IfWERSUTOvW/0GvC2Jr2g0ex2JiRmE0RtAq+T4slhyCg1uj1wdjt5djNIbVfEu0sWfv42RlfeN57nMG8h+2U7JRT9IWQeEtDkoCujG842MY9WZCQto2Ku5J1ilQ5nBYWL1mAHZ7MQCdOk5nx87JHvLhojPdh3yNXm+kcM5XyNIydKFhBA8ciC4yigUPzibkYh9F8k4TlXltCIr1zKizlkdzbMX9tB77yEnNdejn/5I8egpCd/Kpv42huqgZhfvOoaqgNbaKGJA6TKE5GE0xODhCi2Evo9M3vkBlXl4L9uzW3PlCOAmwRqCLOEJQcDFNm+6hvDyKooKmNDEaqMjqRGlZDKbAYtqOetnnfLaKKIzBWnZdZW4qWRsnog/Ko7A8nPzo4zj0gpBqCwaDBbvdDEg67Utjd6tOOAwGWuYcIGXrQfQ42JvSniB9KfoOJWTYW9N54X4iq6sJaBeJVV9A3pUFBG7QEbhZh3mvQNg93U9B/fvR/O23yc/K4tfPP2e/zUZwcDAPPPDAXyom5nzgeWAKsAmoqHtcStm4XMU/mVolpvzgUPbvtdJ9zO8A/MYg3hT/4iU5GTt6HhavkbRqDx9+/iSP9r+VzXFtSX9+rGueP6LEZA7rSmJNxlGzACMb+3dkfm4xt+1M9ymb+WALvjrSlc6372UhYxnJL4xdmYFHl9wazhvxM9HBem4tvxWHw/2rSUy4nMysOXSIuZWELr47NOzI30HbyLb8euRX+if2JzLAO9ks59ABPnv4Ptf+Pam/YTr/KbCUQuuR0KKBcur1ObYB3h8FnS6BSz8AKWHr51q59YAwKDlO0WcfkT1DC6Ru8ebL5E2ZTHzPYgIi631oXDsXbJXQYgC8pKVPypTRlMqRZD79uktsVWozygK9g0PPue0eOo84l329++AsL/c6/uANeg4naG/YSd0mcXnq5ewr2sexsmOcl3we9nw9v3930KOuSH2ETnhktbToGEVqn3jKy45QrLuayrw25KZdjiGwiC7nldKhy92YzU2QUrLmt0FYLO5S+J06TiMuTlMo6/4N1uKsDkUX4P42WZETQHCc/2+Ne79JJiy8M92vDiE773MAwsN6UFa+k359f2XDxgux2QoJD+9Jp46vk5Z2C/EJF9OyxS1+52yIt7a+xdtpb7P5us0YdVrGRXF1MTO3zSTIGMRd3e5iQ/YGbvnFe/5LIqwMDvX90DCb4rBYc3we80VoSEfKyj3bL0RFDaawsPExUX+UuAeN6Mv8xyCYfvmS1i26AlBQVcCwOcN8yt3X4z6uancVQUYtbsxRXk75scPYWyZQ/eRLHM05hD67hMAjR7G3kxgPC7Do0Nd83kt8N4+VQNGoEBzBOuRvLYkq3I8+SBL0yCXIdnEc3F5M4fFYAsMlTmxk74zCFi3I7fQoQ+v9flb8fiUybjvDWvnuzn6yVOa3JnvDRKxl8fVWLwlO2EbzwZ61vKqLm1JdmER1URKRKYsxBJSiN1VRerQ32ZuvIabDD0Sl+m6A21iOrZxMRbb2BUcYqkns+z6hTbee0pz+MJvjiQgbSNPkO9hefBx2RbJ9bh59LoqiS/sosh99jMoNWiKDr99vQSgEV0OAj+oHwc89QcKAkawp3kTPoHaENk/2OC7tdmzZ2VRtTcPYNJHALl1wlpejCwkhqyqHh1c9zOZczebUKrwVh0oOseOGHX8pJaauulv3BAFIebrtq2eIWiXm8OHu5Oe1pHef7wBYRz+mi//wjLyf4uz2vJRwCynLdjBrzjM8NPB20mJT/CoxUwK+5IDlxPn0Wwd0pNtv7jfzjjaRrDCGcledbrud9myi3cEdvPHya5r1Q0i63eZ2rQwJvg/Dz49RFaiVdw+qrvm1XP89S9I90wjrU7+GDUBaXhrX/nSta793fG/eGPGG64OxLpUlxbx9m1v2/vZ1PvBv/hWaN1wHAwBbNTwT5//4zYs1BQfY/WWi1+H2V2aCzgiT1kJonMtFZk1P5+B55xM/dSrZU6d6nFNhMrCifUuvudpFxRE9rCudzrmCYwOGaYOD+yC370EUu2txXPWAHofe90Nn+0TN7bNvz1F+fV37hllpLGPMdd1Z/sEB0NmJ6/YVhXvPwWEJxWkPZMnIt/lvhxZk53zn/3VogI4dXmPnLnfvm7CwbpSWbj3hed26fuRlTaiPXh+Cw+GtzNWnRfObSUi8jABzPAZDw27KpUeXsrNgJwsOLSCj3N0yYNv12+jySRei9U4uirTxY4mRbJun9WFIiI2LI0+93kyAOZGoqEFERPQmIeHiBmUdDgsWSw65uT9y6PB0ZE12X75dMC0ngHInDAu1MyGicesSFRA1y4B5v459ifDpSD23LHTQssZzUjq4M2GrvN2H992mJylHct/32nv8hz6CL4fqsBncf4t6h6RJkY4bVzeh1eE8rAY961t7v2/8YbLZ6X04i9AqKzrAlJQEocHodHpazJwJQqALC0MIQbW9Gqd08nvW7xwsPsiMLb4D+puFNKPSXsl9Pe7jid+ecI1/MOQ/hFNOZFB/omLao9OZcDgqWPPbMOz2Ylq3up+WLe9ECIHNVoxeH0LmoQMs+mA1TRJ60fvSply07ALKbeWuJ7TBYWTowatIKejJ7y2+J63pUkxCogeqnILoUhMts4PoctDTkjv8//5Nk8jOrPn6EHmH87GUvE1YyzLM4VbydkQRFF1NZEoJIU0rCIzydlnr9SG0bvVv4ppczaov91OQVY6xhZWjmVk4rRLT0RjASXTTYJq0jKBZ+0gqA16jIO8br7mqi0zkHwjD1KaUJpFWjpYaSAy1U/trXrM7nKyDYWRHV3M4oRKJxNnYp+2fXKn4r6bEDG3ouJTy1ConnSXcSkw3DHobzVtoSsXH3MwvQsvlvy/7E16Pv54pb7/KsG0b+L/BdxHcsydz7ujvmqeuEtOi704Gr/cfw+GPUSvns3iIZ7eGQet/pf9m90sZ1zOPhF7+v+WPXKkdq755Pmv2N+zTHjniIHZ7OXZ7KQEB2gdd5487+5StfTjXZ9UXH7P+O62a8Z0pvxNUv5rviVxWH46BI2salqlBOmHPHM8P5PiexYTd8xL6fteS+cij2DIzaTbtdfb19d2Lyq4T/NJZs9B0PX8cnaSJ2QvnklBURvejWrfrZZ0Fw7dLPjhHx8Je2kM0ulTy9ptawuvv7QQrRsezOcjtr79/roO+e7X3TZP/u5/hhteRNR8WWmI7PNu0igAfHoEKBwSfhMrfvv0LJMRfwt59T5CR8bnHsX59FxEQkEhZ2S6KSzZx8OCLGAyh2O1ua0xISHv69P4BIQR5eb/icFZjt5exd+/jHnNlro3FVmmk5QjPNMxAMYAq+Zvf9bVt+1+MxghiokdQVX0Mm62Y/LxfCQpuze7Cffx701dIBJ0D7dwc03AM0/I94bQtM2KNttCylXd8Qo/uXyBMzbntx5GUOgT5dsELzbR6TbPyzOyq1jGp6yTOjwCTKZYjR2eRmvIElYbmPLvhBXrH9+by1MsJMXmm4Fbbq9mRv4MPdnzAqgz/1phF531H6eU3InO0v4X1XQLpENuR6dGbePhrJ5a2EtMRQVV3J+bdOgxF2t9EzNzPie2g9RCrsFVg1puRWTkYEhNd7iCn1crGO64h9LcdPq9t0+nIiAxhV7PYBl/DUyE/zILJruP7wVn8t+X9tIxtzT3L7+WCNVqacVZUNVtSi8mLtBBUrSe00kBOlIUmAU34asIcogO1elNVZaX8/OarjLjhdsKbxGGtruaNGy/3ec0Bl19DxyEjefdu7fOrda++DLziOopzsmjeoTNrvvqUrYsW+F2zTq/H6dDeq6NuuYv9638jqmlztvzs27X6x/G2baS1LuF4k0osRidtjodQbXKQnliJXe/EYNdxTadr6e5ow2fzXyP1eB1lX0hoZCbqyXIooYK1nQqxGpwgQO8QmKWBLy74isjgaLYs+oE5x79nJ+nYwg0EB4RiyymiMsCBxfTHXXMxgTFc3e5qRrYcycHig6RGppIUnvTXUWL+V6irxCQnb3WNz+BfrBWDPGRfef1peuzdyb1DJ7MvsoWHJSY9/S0OHnqFpJaTaN36fu7edYRvcooavHbb4AD2niCVbtLHzxFc5XYHNR+SSXR7/4pB2/3l7E3xLok+aODvrF7T32Osb9+FrFt3HgDDh+1GpzP5VWJmjprJgKZezcUB+OGVp9m3XosFad2xLV1Kv6VVSJ17f6IQdH6e0rWxLQPv1ZrKgZb2fdi78m4tmevDKTnkmZJpTknBsn+/nzNg1nk6FncT3PBzkmssbsqV/HrkVzbnbGLO894VOa54SO9SRACGhnTnrke9a8xEfvg2RTfe6dqXQmJtLTkw2YGIuYyI4jl+11WXLwtNrK0woEfiBGrLj80+5zVKD0/FYsliyOAtrlRJgCVL21BrCB08aB0mk2fjUSmdjYqlqKW8fB/pW7ej08HP0z70K3f+Xf+m/eDhCCE4nvG5lwJ0JklocgMJ8Vfw/StTKDikKTZ3f/gVm4rSuH3x7X7P6xjdkZ0Fvt0Yn5z/CU7p5IaFN3gdiyyTGO2QFw6jtkpuXXRyH+zlARBS523edstmdIGB2K1WdHq966Grq1Od2lJZyc7lvxIcGU1gUBCLn55Cke6PfS4Pn3grPcZMAOBo6VG+3juHiR1vwKg3Mm//PEJNoUz9fSomq46xx9sRvqfiBDM2Hr3BQHL3XhzYsPbEwmeJiPhEYpOSyR4cQb8WA3ln+auELsmgSbHbvbwjuYSN7Yrre6jQOwXJmUHYDBKjQ9CkyEzqscYlSPhj4stvUiGr+TB9Nr2b9SXEHkDH0LZ8PeUhAoJDKMw87iHfskt3DCYzBzee3dc0vk0qQ667ibjk1uSUZFNZXERiizYUHTvG/DdfxGiF8gLfeT79L72ahJS2tOreSykxp5u67qTk5C2u8beYzJp6xqbpL0+l88G93D3qJg6GdPBQYpxOC8ePf0azZhPR6Qz0X7uLw1Xat8xn5P0kkc79RV+RHdX4NvWji44zZvtqigrXk9AnlwM/tqTbrZqFJyHhSrKyvAvR1Tf/d2j/MgkJFwGw5rchVFdneJ0DkJh4Ba1SptD7s97c3PlmYgJjeH79815yW6/bil6np6rqGFVVR4mKGkhpfi7v3uVp9bmv3Wr0tfVSet8CXa+GJu21pmbHN8J7I93CQdFw3w54NgGCY+E/NYF+Tid8PRFMwXDROx7BvFZTCgc/afjDNmH66xye/R4z43axpqOOxLwAzt2gua7mD8qkMMxt/tc5JYkF8Op7mjJTMbQHvWbOZt7+efx0+Cce7vswrcJbcWDUOdiOH/d5PQBLipOCf/kP7jMeEYR9q6fg33bMOwTSDNYUyeYKPRXRV/Pt/objqV4Z+grrs9fzr57/ItioKXK179fT3XH20JYNVJWW0nHoSLIO7OXzR+/3kolObc0rrZdyY8fruKHzLaxdNxazI7fR1zCZYmnR/FbKjkayfeNzZG+MxWnXlK6EPrnEdXd/KOZsiSZ7cwzS3rBSdtFDU4hs3oJCQzll1jKu+/k6TwFZ88VXB5GlRgwOQV6kp0Uo0CIJsMLMN05cbq6wVQv2h5qJbNWGVt8vxOCUVBkNNL/1NmLuuB27QWBz2AgyBiGdTha9M42dK3zHXVz99Ct8/pj369wQXYaOonO33pQ57Wxc9CP9L7mSpG49T2qO+lQUF7Fh4XccytlHxZYDWKuqPI63HD2Unj1GkPbrTxzcuM7PLJ4YTGbsVne2W/9Lr6L/pVcDmrJdcOwoBzetJ+2XBZQXFTL8http0akLP7/5KrmH3a5vozmASx55isS27SnNyyU3/SBZB/bR/+Ir2bZkESV52bTp1Z+CjKMs/eAdOg4dyc4VS7j6mVdIaNO2wTXarBa2Zm9h2o43SMtLo0tsF9pHtWfl8ZW8OORFusR2QVfzpWDJ0SXct+w+17njAoYQO/840q79zQSFR6AzGCgv8LacN7v8HCacdzMBwY3rwdVYnA4HUkr2/r4Ka1UVbXr15bOH76OiuOEv1GeL/5uzQCkxpxt/Ssx73MEycY6H7FvPP0b7Iwf5100B7Cl82kOJqU/8sq2u7U/lpeiQ7Fr8X545p4OXbPt9W9md2s1jLLy0kEWdW9KsWXOWr+yAqPctbFPIrcicD+gV3PCHbN24F6s1n+MZX2C15nq5ISIi+lJcrH0YFTV9nkvbaiWzbU4bPT7VeiWZhMQuYdHYN9m5TVNa+vb5iZCQtrxy5XjN31qH29qsI9RYz13w7z3wajvPsUveh86XNngfAOTsgrdrrEm9boJxr1G+ahXHbr3NJbKllaD7Icm7o3WEX3k53+z7BpNVx4TVCQRXawrkNa9M5+KV11Jm077Ft49qz+7C3ay/Zj3Gsmr0YWGIej2bHI5q7I5ydIV2DgwbTn2qOzkpnORfeQmbqydgmw5DrreiETP5HmJuuQXrkSMc/OJ9SgZ25PZjWq5ygiGa4ooCKgM8zwuwSFplS8y9evDe6Pcpt5UTFdD4Dtz1kU4nVWlpGGJiMERFYTXruWnRTWzPd7sRJ3WbxGWpl/HK9HuI2ezt2rHpnfw4MIuSkNrXobZkuuD+8EvQ7S0mzBBFaPePqc6PYO+8eIbfcKerR1ddWlkhobSC8IxsTG1aYzlwkANxkeyP97zHic9PY/FHM8nY4917x+SUjIhPInrIEDbaDvBh0CbG5namaMmmBl+L5nYHnWuC6+s6DNKjwzgaE86Ac8fTqt8Adnz2MaszGy4AOPmTbzCa3cXAvn76MY5u39rgOb4IjYnlptdmYq2uIiAkBKfdUdM5/K8Veuiw2xBCh06vRzqdpC1eyNIP3uGWGe8RFttwS4K/Kw6nA/1f7PfQWKTTiThBTzSH3Y5Op2PZx++y9ZcFSKfTSyFtP3g4XUaOpmnbDlrMjZQ4nQ7Wzv2KziPOZdE70zm6fev/phIjhIgC3gfOBfKBh6WUn/uQmwo8CtQtXNFFSnmo5ni3mnnaA7uBm6WUW090fX/upGM05yHxuofsrGceJuV4OvfepmfyqF8Y18V/wFxdJaa2HQHA3j1fM/z69gzf4O4FM2L1jywdNK7u6fznnce4efp7RMTF+8w6+aC6H9vytvH60JfQO4oJLZnvUkJqadP6AVq29DavZ2T/yJ5d9/pdu6Pli5zb2r3mqb9NZd7+b3i1eZVP+WFDd1Ccnc9H/77TY7z9wKGMKazXiqDbtbD1M/d+y0Fw1RdaFlJjKDkOa6bDuf8Fg2b+lVIyf8FrvHDsA5wC2h2XbErR3pjjVscTU+qZhXT/Vz9ic9q4YN4FBBuD+WrcVyf8ENqadjMFBcsB6NrlPQIPaOnIwf36sW/7kxzL+8QlGxLSjpA7DiF1koJ/2YieZkRfKmj21pscn3QXLWd/RvbTzxAz6U4y7pns63JePDApGKelmqIQGLlVcvUKt0vj8of0rmC97yd8T6uIEze1c1qt6EzuVOS6rR5cMgKufFDvMxAwuErPJcuaojtBt2SdToce6HQoky1JDadpJ6S2I6ZZC1r+vgnn2vUNytp0Oow1Ze9j751M9vQZZESGUBZg4khsRIPnngmSuvUkfWvDylFdbpnxHuFNtNfDWl2F0WRm+7Jf+XXWDJp16MQlj/wXg/Hv0zhQoWgMQoi/VAPI08WbgBWIQ+vWtUAIkSal9OW8/kpKeW39QSGECfgeeB14C7gd+F4IkSKlPHH1M20Wj70AvGNVDDXNxcoCITSg4Q+YB5PjeeFwttd4WJaF9iGeLenbH9jmocS0Pah9+w0MDcNi9TZFdu78FkeW/BeA+1ZotVl+uuBrirdoliG9IYwhg9aj0/le47ysdL7MDGBIiJ1tVXr+FedZ0CzV4OnXfLTvo1wan0hOuu9KZjk5P5LY9DImvfc5dquVWZNuAGD3mhWM6t8WU7FbYdtTPo/4MAMR574LnRrOCvFJeDMY86LHkBCCxwo+hCDtd7gpRftf58RDgUnu3osRN94BgFFn5OdLfqYxOBxVLgUGIG3bLbRr9yxGQzhr6ymYzZvfRGrKo8j1dmzZOZQt/pWodRNdrp72e3YD0GreXAAKOnSgeteJO/i++JZ/19mc5x1c/rD2tp3wvRb7cFuX25i1bRZjW43luUHPebiaahWWuMcfI+qaa7Dl+E5F1kltbmkykhcm+e8lkiALpGZIVnSWbOh0hP9e9wkR246x+cXn2NU0xmsOp9OJE3wqMK1zijgYF4nR4WTUjsOINM1qWDfiJGzcOBzFxZhTU4m9+y5smZmU/vQT+W+97ZLJmzYdPdCiULMOdcwsQJpM/Ny+uc/7ap+Rj1MIhJQk55fgFNq9FlxxMVu3b8Jq9P8RGJvUirx0t/VlzD3/R2q/QegN2jlFWZq7Nig80m/g6t0fzsEc5M72MwVonwddRo6my8jRfq+tUCgaxx9SYoQQgWi9k/ZLKb3bBvs+JxitcWQnKWU5sFoIMR+4DvBdxMQ3w9DW/brUzEjThRD/B4wAFjZqLfXqrOh9tF4z2DUlpjxIoD9B/MFdLZrwwuFsbm0WQ1jOtZRaNetDfGsjDkcVXUMDSSur4vqv32ToeWNdFTT/885jAKT2G4Q5KIjSMm9zdXh4T0osnsG9pXUaF35clsowHwpMiaWE2btnM3PbTEDHDyUmNl+7mcd/6MQ5YW5XyMGDLxAfN541vw3ymqOWB48HurJA9hfuIDHxMgJDNWvKmLvv56c3tMrBM35vwv2Xd8E+6jFKPupFRmIgGYmBDEsZ6aM3uG/2Fe3jkvmX8MXYL+gU412krO5rcW37a7m/1/28v/h1yt9f7hrvc+FlDL5qYiOvCMXFG9m0+QoG9F/Ojh3e1pI9ezwLcYWGdqZP7+9c+8JgwNSsKdE33NDgdVp++QXpl12OOTWVkCFDCDv/PCz79pH/9jtE33Iz1qNHyfzPA17nmdu2peUnH7uysD7OHMvERHfGxqxtswConvsDe279HoDg80djTHA3r8v579PI6mpyX9KKZ02+3UB+mOTd6Q6C6+i1wmqjST7MmOkeu7mm9KZz9jUUAklAUn4JVr2OTUnxSCEoDg4guqyS4qAAHHodkQmJ9Fu4ipywIGLLKtFLaJvtu5xU0tdfE9jZ+3dtbtOG2MmTiZ08GSklJd9+S9ZjjxN+ycUkPvOMh2yt49ZaVYnBZCZ32nSKZs3ymlMvIWzMGDo8+QyD0TJpNi34jkOb1jP8htuIb53qct3Umt7tVitCJ9AbPN9nkQnuDsv3f/UjABl7dmGtqmT7sl8Yd++DHgG8CoXi9NPYFOuPgPVSyrdqLCGbgI5oVpWLpJQn/JorhOgOrJFSBtUZ+z9gqJRyfD3ZqcC/AAeQBbwhpXy75ti/gHOllOfXkf8RWCalbLAOvynILNunevsE7RjYj2cQWIfDBzDZrOxsIUgN70Z4YMPWGIeU6IUgc38hgbGemTPBod0pLC3GlpVDSEQUZZHRWHMzMFdUumSad+iMzVZMecVeKh0QpAe7LoyIkDZszd3qdT0BGATYpFbfpT4bsj0za2plSq2lHCrai1kniTc2/LsPCGhKma2aIxVaoFgrs5MypyDPJkiJTMEhHYSZwqjIy6eyPAekoCRCEG/wdEWZTLEEB53Y7VG77vAKI4EWPfoAM4mtUj2Ob8zeiETSNqqtdu3iIo+I/qbtOqI7gd+3PkXF3sGKkRG9kVJSXLLRYzzAnEhgoO9v/acD6XBQvWsXAe3bY9mzB2d1NUG9aiyyTieVG7X1BPbqydHy4+RW5qJ3QrN8CKn2/fsUkRHIomKPsZ0tNMW8Z1xPV/CitNqo2rql/ulemFq1whDjbYnxhbOyCl1gAAiB5cABHIWFmNu2xVFQgKOkFHNqCrpg74aACoXi782KFSv+Uu6k0cD0mu0LgFAgHrgJmAo0xlYfApTWGyupmas+c4BZQA7QF/hWCFEspfyiZp76ecf+5kEIcRtwG4Ax0ORLxMsyA4CEfU0bnwGiF4LCzAqk9H6AVpRtwSzAaQrAbrXQQu/A5sghu2bJ+hp/uMWuKTV5dh16BxgNgkCH7342Ek2BAbA77Rh0BpzSqZVor/TfgyTMFEa3OE2h8fXwriXAHE9gQDMCA6BJqFZLo6o8jXC9JM8m2F/kVtRizKFERGvrrKjWef1VWa15CH0ogaYIhDBypPQIuZW5tAxrSVRAFDoJOr2B8uoyIstMmGuKnjmqLVRUlpJZlU1gOTicdmSgdtOhplBslmoPBSYoLPykFRiLxTu7JiiwJaBDCAgL7YTDUY3NVkhAQFP0et+dtU8XQq8nsLOW+h7QqZ51QqfDmJCALSuL6m07iA8MJLbE+2+3LFAQWqWNFwcLMkJLaF2uVeosCxQcq9E/6mZfAAiTkaA+PooW1lZ5tdsRBsNJFdDSBbndqeY27m7X+vA/1kpCoVAo6tJYJSYSqP20Pw/4VkqZK4T4Ei0AtzGUg1c/xDDAK/VBSlk3cOA3IcQ04FLgi5OZp2auWWgKEU3aJshXX/V+COVVJHBfiGfJ6qnPTObx6zQLxIwB3zMopeFvnjarg1mTtUJ1Tbo9SFSqt+k8fUki/c6dSsf9z1F6OJx3D2jl+sc/cCsJqW1Yv+ECIJH/HA/EVlMMqVl4MywlmoIQYgzRKlbWo2/Lvrw09CW6ftKVK9teyfyD82lld1s+2ke1Z8547/olBw6+xJEj7myRDu1fJjNrDsXF6+nY4TXi4z2L8dUGHa8qM/BtsaYQCiSvNa8CvL9Nf59vYoKrwFk1lpIC5q9vRkG7EEJkCDcvaYYhyI7FVbMhBFomeM0D3kHVzTt0piQvl9IW2u+l1pzfGKR0snXrDRQW1RbeSyQwsCVVVUfo1HEaTZqMPe0pzKcL6XSyp0NH90AdZaDpq68QNmYMBVUFWBwWVh5fyTPrnqEVgFMSXgwT+l/Fo/0a+5ZVKBSKP8bZ+gxtrBKTDXQSQmShWWVq81xDgMbWBd8HGGoCcGu/xncFGtNYo272407gfiGEkG5fWBdosFlrg+jwVdDK/Q23wJLFroJcOkR7p0zXknfEbWQ6tirWpxJjCraR2ncgpBUQatAe7sZgG8dK/s2xOt6fWgVmQOIAfsvUqqX+cskvxAbF0v3T7l7z/nLkFx6q0sKKvtzrWU/mm/HfkBSe5HPNcXHjXUpMbQG8hISLqKg4RFBQspd8926fsGXr9QwOtdcoMZKnE31nMS2b3ZqW5SaOpBTTckQWAOZwGxcOPEphViADf4un0437ANjxSQr2qpMLzzq2y50OfMuM913bUjqpqjritf7y8r2sWz/G51yBAS3o32/xSRWK+7MQOh0tPnifozdpbSaaTp9GcP/+6EPdhsjayqlXtruSK9tdCUB+VT77ivYxINF3EUOFQqH4O9LYJ8cHwFdAJlqcSm31pr5Ao2ruSykrhBBzgaeEELegZSdNALw+VYUQE4CVQDHQG5gM1EZXLq9Zw2QhxDvArTXjSxt5L14IX3FBdYYe26QVaaotx//mHdql7npnhEumIEPLKHHaM5F2HblpUTTp6qnIJPbLw2A2Q+fLESueZ3jcQbZFedf7uLPrnaTlpVFudVtdEkI0C8WU/lPQCR0TWk/gePlxxs3TMp1uWuTddmDVFauICIjwe9+hIe0YOGA1ZnO8h9YcHOw7fiUqaqBre8MVi9hz4EUKcn8A4K1cM5OaaBaj6mITkeWapaboQDghCVVEty8GwBjk4PbW5VSGud1Ana7XdNqcLdF8XaVj9uRFBIWF8/qkq3AUlNF2wBD2HtoC2d7Gtq7nnE94E62oncNhYfkKTdGMjTmHLl00BW3b9knk5S3y+zoMGLDM77G/IsEDBtB68a8Ikwljk8bV44gJjCEmsHFxLAqFQvF3oVFKjJTyKSHETqAF8HWdVGY74DsX1zeT0BSiXKAAuFNKuVMIMRj4WUpZW9Lwyho5M3AceEFK+XHNWqxCiAuB99A6a+8GLmx8erU3BbnNvSJqKkNSAO+y83U7EjsdTnR67dv7is+1DtXWcq1nh8PinZVgKTFiteaDLQ+z3kSXh7+gaPclXnKTuk3i/G/P53i59qC/o+sdrmOXproLxbUMczc2TC9N95hj+vDpDSowtQQE+HLf+MdgiMBuL2bNb4NdY4GBSbx34TfYKspY+M4V5G13K2apfQezb+VqCveFkzLBncgWFOud1h7XvYB3ei8nKFRzkdz31heuY+OAitIMfpr5OJ3HtSIl9T/o9e6UaimdLgUGIC//V9LT38ZmL/FSYPT6YLp2mUVwcAp6/d8zqNTUrNmfvQSFQqH402m0DV9K6VUjvVaxOIk5CoELfYyvQnNN1e5fdYJ5tgCnVmu7ht9/uxyr3QT1asyVxozFlxKzZu4B13ZxTh4FGYdI6d0fS8nbHnKhzb1jV8zhNlav6QcGSG5moCjrRS+Z9yu6MhJcCgxATID/b9BXtr3Sy4UE0Dehr99zToUund9i85arPca6d/uIwIBICIgkudUdJMZVkPbrTwCMu/cBZmzZSEU2VFf0INKcQJXBnR7cof2L7NrtTiu2FC+G0Bs95rfby9i2/U6Kin4nqidkZK0gI+vDmvO1Vgt5eb96rfXgoZdd21GRg+jY8TVMpj9e6VahUCgUfy38KjFCiOsbO4mU8pMTS/01sdvNPuuQSp33S1NpqyRt8THX/jfPPExZfi4X3D/NS1Zvarg+xOGWQejL3B1rN1bo+azQREqktv9o30d5Zp1WC6OuQlOf+3re56HEbLx2IwVVBQQZz0wWTWRkX8zmBCwWLc5l0KB1mOs0Iqytz9L1nPMpyctF6HTcOeszdq5cStdR54EQLF+2GiclDBywioCARIQwUlV1lEOHX2P//qdp1vRqdDozdnsZGzZeTGWl/3Lvu3b/H1VVRzl6TIuLadXq3yQn3cVvv4+kqiodgJiYkXTt4l0zRKFQKBR/bxqyxNQPlDUBRtxFNnVoQb0W4G+rxED9+r0a0sdLszHHs2ZIWb6WsPVrnUJrTdt1IGPPLvoP+ZitO71dRXWJjOxPfv5iAD4r1Fwjx0o1JWlsq7EuJeaiNhf5naO2MaBe6Nl6/VYAEkP8t0g4HfTru4gVK7sAYDJG+5SJbZlMbEstuNYYEEC3c91Btf36f09JyRYCArR11mZBHTr8GgDFxZuIiOjF7j2P+lRgajOJajmcPt21nZx0FwAD+i+hquo4FRX7iYoa7DWHQqFQKP7++FVipJSuKBEhxFi0ejD3AbXFRfoCrwL/PXPL+3Potb/apyXGUadSrnS6s3IiYgupKta2M/bsIjgikrAoLV4ld1sUmb/H0eX6fHSBnvVbios1pejNXHdsR7VDixUJNYXyUJ+H2Fe074T9cdZdvQ6j/uz1XjEYghk+bC9S2v5QGl1gYHOfBeP69l3IunXnsWXrdQQFJVNZedh1rGOH14iI7EOA2V3SvqLiIGvXnevab9H85nrXaUZgoIodUSgUiv9VGhsT8zJwk5Ty9zpja4QQ9wEfAY0v0vEXJ7CqkvM3V4OooxRI6JgziAh9FFCA056NtczdtzJr/wqPOSqKizAaI+nfdyVvz9Ky0YPC4qi2eSoxdnsxAPvrBAFf38Htxbum/TWNWvOZch01hE5n4HS33goOcgcm1SowgYFJDOi/xLd8cGt695pHSEg7bLYizOa407oehUKhUPy1aWxhjCTAV1e6SrSMpb894ZW1gbha9pHOXsKofRNBCu5YO43Bhy/jt+c1JcRe7b/SbV2Cgpsy4oY7CAgJpUvX51zjPbcWe8n2T+jPluu28H+9/u+U7uPvjBA6evX8xrVvMIT5VWBqCQvrgk5nUgqMQqFQ/ANprBKzDq3RoqvjWc32a8DaM7Gws82FW1e6tqUtB6chnDYFPTA461hkasrhO20HG5yry8jzXNvdzxvPXe9/QUhoe9dYiM77gTu0+VAMOsNftlLs2SI8vDuxsVp33149vRLiFAqFQqFw0Vgl5mYgGkgXQqQLIdKBdKAJ7mJzf2tMdq3w8MCNSxBOd3q0TnpmGUnpq7qvRvtBwwAwmLx7NNVVTvTFGa7tmXlaPMyR0kY1A/9H0KXzW4wccdBv0T2FQqFQKKCRSoyU8iBaaf+xaMG8rwJjgM5SygMNnft3QS8ld6z4ju4710OdAN5z93pWwnVYtvqdY/fq5QDs+W2lbwEJoWU2RDN31+nd1ZqS1Cmmk+9zFAqFQqFQ+ORkit1J4Jean/9pdNKOQzqwFE8jpjgAIlJdxxzW3T7PEbpoeo0bxob53xLfOsWnzPDV+QgJjBgNdq0k/tuj3sasN9Mr7ox3LFcoFAqF4n+KRisxQohI4Hy0QF4Pf4mU8qnTvK4/FeG0YymuKWAnPcvjS0eOa1tnbIvTthcAU9h1dJvQgxUbf0Q3IhUvyrLR1XQsOFCZwxN5gQgheaNTCN2adDsTt6FQKBQKxf80jXInCSH6AQfQUq3/C9wEPAr8H3BpA6f+LamwLfY5Lus1ijSFjHVtC6Hj2S0vMqfbPh7b9SwARdVFDP1qKDvyd7BnRic6J7egc3ILVhkFpU5BiUNHx+iOZ+5GFAqFQqH4H6axlpiXgNnAvUApMAIt5foL4P0zs7Szj67Su99RXaSzyO+x7zpOI7jAM7PogZUPUFhdyFULroKm7kaLrx5fCEC/hH5ntUidQqFQKBT/SzQ2O6kL8EZNXIwDMEspc4AH0Sr5/k9gKC/2e0xKC0i7a7/SrFllzOG3YA6/jeywQ9icNtfxhekLWZvVcPb5fwf+zxU7VigUCoXirNFYJcZaZzsHaFmzXQ6c2UY9fwEc1t1Yit/E6chyjW1L0V4SoQtD6LQG3EfLjrqOf7D9gxPOGxekCrQpFAqFQvFHaawSsxmozQteDjwthJgITAe2NfZiQogoIcQ8IUSFEOKIEOLqE8ibhBC7hRDH642PEEJsFkKUCiEOCSFua+wa6pKV1cZzQPqWs1dvAcBpdysxe5vnurY/7PWw1zm7C3cTFRDV4PX/6YXtFAqFQqE4FRqrxDwKZNZsPwbkATOASOBkFIg30aw6ccA1wNtCiIYiW/9Tcy0XQggjMA+YCYQDVwCvCiG6nsQ6ADiwv7/Hfsehw33KSUd2zf/F7jHh5OOej7GkzSdYjJU+zyusLjzZJSkUCoVCoWgkjS12t1FKuaxmO09Keb6UMkxK2UtKub0xcwghgoFLgMellOVSytXAfOA6P/LJwLXAc/UORQFhwKdSYwOwG+jQmHU0hDkouMHj0qHpcSu6lQBQZSpjf+ymRs//ejN3NtOn53/6B1aoUCgUCoWilsZaYgAQQvQSQlxRo5AghAgWQjQ2wykVsEsp99UZSwP8WWJmAI8AVXUHawKKvwBuFELohRD90WJ0Vp/ErfgkLKZJo+SKQjx7YX4w2jP+paXNHeBrqJOW3R93t2lVG0ahUCgUilOjsXVi4oQQa4H1wOdo7iDQ2g+80shrhaClZ9elBAj1cb2LAL2Ucp6fub4AngAswCrgUSnlMT9rv00IsVEIsfFEC2xsjEpxqM1jPzYw1rW97OhxjhjdadM3lLhvOTAzrVHzKxQKhUKhODGNtcS8hpaVFA3UDQD5Gji3kXOUo7mB6hIGlNUdqLHyvAhM9jWJEKId8CVwPVrl4I7AA0KIsb7kpZSzatxeJ6zr36xZsxOJIHSR3N39bo8xg85tjIp2eDaI1EtoY7VyfnkF4qrPTzi/QqFQKBSKxtFYV9BIYKSUsqieteIgWhuCxrAPMAghUqSU+2vGugI768mlAEnAqpprmYBwIUQ20A/oBOyTUi6qkd8rhFiA1hJhQSPX4pPmzZufWEgYCDW5jUcmnYlmoc24v+f9jGgxAvFqZ64sLePLME1Gj2Re9DC46B0Qgi/HfUl0QPSpLFOhUCgUCgWNt8QE4lkrppZYoNrHuBdSygpgLvBUTSzNQGACUD/CdQfQHOhW83MLmhWoG3AM2AKk1KRZCyFEa2AcJ5HqXZ9zFy7igsoqr3FD4BCvsfDYUC5scyFtI9vyw4U/sOk6LbD3hk430CJUU4LqZmp3vHQ2XDwTapS/jtEdiQ+O/6NLVSgUCoVCUUNjlZiVwA119qUQQo9WsXfJSVxvEppClIsW13KnlHKnEGKwEKIcQEppl1Jm1/4AhYCzZt8hpTyI1rtpOlqMzQrgW+C9k1iHB5HFxcTXi4eJiG/LFU/c7iWrNxoJMgbxzQXfkBSe5HnQbgFgUKWm1/WtqmZIM29FSKFQKBQKxanTWHfSA8AKIURvwIwWzNsRrU7LwMZeTEpZCFzoY3wVWuCvr3OWA83qjc0B5jT2uo3CoPfYTeqaQnyrcC8xvcHkNebCooX3DKuqYnVlKOFDnz6tS1QoFAqFQuGmsXVidgGdgd+AX4AAtKDe7jWWkb89Qq/pcx2GjND2dZplpklyaw+5oLAg/GKtiVG+aCbhd/4GHSac/oUqFAqFQqEATqJOTI07Z4qUcpyUcoyU8jEpZdaJz/yboNdeitiWyQDodNr+qFsmeYgFhgb6n+PYeu1/g/n0r0+hUCgUCoUHDbqThBCNyjySUh49sdRfm1pLjKwtTic0JcZo8lRIDKYG3EnzamJoiv/2L4dCoVAoFH95ThQTk47ftogAiJrj+gZk/tKElWgtBIReuwXp1Oq81KaSC52nscpg8mNl+eZm97axAZeTQqFQKBSK08KJlJjedbYFWibQ1cBx3+J/P0St5aVeYG+tEhMel0DrXv04uHEtAE6Hw3uS6T2gsE5oUJcrzshaFQqFQqFQuGkwJkZKuanOz0bACWyvN974Doh/QWqVmFp3Un0MRiMX/ucx137arz95CxXWi20OqF+YWKFQKBQKxenmpBpA/i9Sq8TYMrUO1bEtkgDvrKQGGfaIezu+y+lamkKhUCgUigZQSkyNElP6448AJHfvxY2vzaTdAM8idUHhEf4nWf6sezv7DxcOVigUCoVCcRL8ESWmoUDfvx2umJg6RCU29R5rqtXba9FJWVoUCoVCofgrcKIU6/n1hgKAd4UQdTtZI6W84HQv7GzhS4nxRVCoVr23y6jzz+RyFAqFQqFQNJITWWIK6v18htaEsf7435bGKjFxrVMACI6I1AaObwIpIXu7p+DQB0/n8hQKhUKhUPihQUuMlPLGs7WQvxQLH4G1b8LUEtdQ7/EX07xDZxJS2sLehfDFFTDudfjxPs9zVbVehUKhUCjOCiqw15clZu2b2v91jgmdTlNgAIrStf9zdsLIJzzPbXPO6V+kQqFQKBQKLxrbxfp/jspKrZZLg+4kh9W3ZUVXUxhPOiA0QdsefL+3QqNQKBQKheKM8Y+1xBw62Mtj35Sc7C3ksPo+uVaJ2fgBWCu07b53nsbVKRQKhUKhOBFnVYkRQkQJIeYJISqEEEeEEFefQN4khNgthDheb1wvhHhaCJEphCgTQmwRQkSczFokWlsBk1VTVOIee9RbyO5HiRF1WhTYahK1TKpfkkKhUCgUZ5Oz7U56E7ACcUA3YMH/t3fvcVJUd97HP9+BweGOoCAMBjCLoIAZFbPGFcPz4LBILkpc1BBvu1Ew8cYSsys+8nhZonhbjRp1STAQjDEmirpqIlmDEbwEMQsi6oIkQUdhQO4wgIz89o9TDTVNX2aG6W5m+vd+veo13edSdep0zfRvqk7VkbTEzJalKf99YB3QMSn9JuAU4EvAB8AgYGeDWmIhiBn6xiIA2vQOz4Fh80f7ytSmWWX8DM2nURDTum2DNu+cc865A5O3MzGS2gNnA1PMbJuZLQCeAS5IU74fcD5wa1L6ocBE4FIzW2XB22bWsCAm0mb37vCzT5+Q8Mn/7MtMF8Ts3rHv9YK7w6zVJUV7Zc4555wriHx+8x4N1JrZ8ljaEsJZlFTuA64DdiSlDwFqgX+QtEbSckmXp9uopPGSFklaFE+36ExMv6fm0POWW+I19r1MNyYmHsR8tisEMc4555zLq3xeTuoAbElK28z+l4qQNAZoZWZzJA1Pyu4NdCYERf2A/sCLkpab2e+S12Vm04HpAN0H9IzdihSClbKBAykbOHBf8p7afa/Tnompqfu+5pPU5ZxzzjmXM/k8E7MN6JSU1gnYGk+ILjvdDlyVZj2J0yA3m9kOM3sLeAwY3SStfOKSfa/TDez98I9NsinnnHPONV4+z8QsB1pL6m9mK6K0LwDJg3r7A32B+ZIA2gCdJa0BTgYS00THH/By4JNS/vsg6PMl2LlpX1riTIwZrF4MZV2gaz/44LUD3pxzzjnnDkzezsSY2XbgSeBmSe0l/R1wJjA7qejbwJGEu5cqgEuA6uj1h2a2EpgP/D9Jh0g6BjgPeLZB7UlO2FIFS39VNy0xJmb+XTB9ONxbAR//d0M245xzzrkcyfctNd8F2gJrgV8A3zGzZZKGSdoGYGa1ZrYmsQAbgD3R+8+i9XwT6EOYfPI5wh1PLzaoJfU5d7Mlut06HtxMH96gzTjnnHMuN/L6nBgz2wCclSJ9PmHgb6o6LxEG88bTPgJGNX0Lk/zn1XDixXUfbpdQfiJ89GZ4fWJxzpPpnHPOFVIRP9xE2YsktG6zf1oigAH4v1MOvDnOOeeca5AiDmIaIkvAU9Y5P81wzjnn3F5FO4s1QGliYEymwbq7ttZ9dkxCmw7wjR/DO09Bq6LuRuecc64givrbtzRxgmXN0roZKgHbE16vmAutSvev/MVLYeDosDjnnHNJdu/eTVVVFTt3NmpWnGahrKyM3r17U1qa4nsyD4o6iNkr+UxLx5777kxasxR6HV93DAzAsGvy0zbnnHPNUlVVFR07dqRv375Ezz1rUcyM9evXU1VVRb9+/QrSBg9iAPZ8Vvf9lthM1gvuTl2ndVnu2uOcc67Z27lzZ4sNYAAk0a1bN9atW1ewNhT1wN5DFI2JSTXmJRsfB+Occy6LlhrAJBR6/4o2iDGgdeKuo41/LWRTnHPOOdcIRRvEAPvunP7jQw2r1+VzTd4U55xzLhf69u3LkCFDqKioYOjQoQD86le/YtCgQZSUlLBo0aK9ZV955RWOO+44hg4dyooVYZrDTZs2MXLkSPbs2VOQ9mdStEFMxhNg7brtnzb47FjlFE/wdc455w5S8+bNY/HixXsDlsGDB/Pkk09y2mmn1Sl311138fzzz3PPPffw0EPhH/ypU6dy3XXXUVJy8IUMB1+LDgZn/ggue6VuWklpmIIAwi3YzjnnXANISrtMnz59b7np06dnLNsUjjnmGAYMGLBfemlpKTU1NdTU1FBaWsrKlSv58MMPGT58eJNst6kV9ejU9ulikQFnQPWyumlrlsLFz8KbM+EL38x105xzzrkmIYmRI0ciiQkTJjB+/Pi0ZSdPnsyFF15I27ZtmT17Ntdccw1Tp07NY2sbpmiDmLYfLKdTj8PTFyhJenDP2mXQritc9zGUtstt45xzzrU4ZlavcuPHj88YaDTUggULKC8vZ+3atVRWVjJw4MD9LiMlVFRU8PrrrwPw8ssv07NnT8yMc889l9LSUu666y569OjRZG07UMV7XcSyDFA6rH/q9DbtoYXfMuecc67lKC8vB6B79+6MGTOGhQsXZq1jZkydOpUpU6Zw0003cfvtt3PppZdy77335rq5DZLXIEZSV0lzJG2XtErSuCzl20h6V1JVmvwLJZmkSxrclrAC+Gx3uo3DMV9v6Gqdc865g8b27dvZunXr3tdz585l8ODBWev97Gc/Y/To0XTt2pWamhpKSkooKSmhpqYm101ukHxfTvoR8CnQA6gAnpO0xMyWpSn/fWAd0DE5Q9KhwHVAuropbd58OJ07r0usA955On3hYZPg3WfC694nNWQzzjnnXMFVV1czZswYAGpraxk3bhyjRo1izpw5XHnllaxbt46vfOUrVFRU8MILLwBQU1PDzJkzmTt3LgCTJk1i9OjRtGnThkcffbRg+5JK3oIYSe2Bs4HBZrYNWCDpGeAC4NoU5fsB5wOTgB+nWOWtwL3AOY1tk5mlPxMDdce+nDO7sZtxzjnnCuKoo45iyZIl+6WPGTNmb3CTrF27dsybN2/v+2HDhrF06dKUZQstn5eTjgZqzWx5LG0JMChN+fsIZ1p2JGdI+iIwFMj6lDpJ4yUtkhQ9zSd5PEuGgVbxIKZTz2ybcs4551we5TOI6QBsSUrbTOpLRWOAVmY2J0VeK+AB4AqzbKNzwcymm9lQMxsKsH59bwA+3ZZm2vDhk/e99ruQnHPOuYNWPsfEbAM6JaV1ArbGE6LLTrcDo9Os57vAW2b2emMa8VHVsVSv+RvablsKZtQ5M/OdV6H7sfvet27TmE0455xzLg/yGcQsB1pL6m9mK6K0L7D/wNz+QF9gfvRkwjZAZ0lrgJOBEcCXJSWCnK7A8ZIqzOyK7M0QtbWHpM7qkXRlq5UHMc4559zBKm9BjJltl/QkcHN0S3QFcCZwSlLRt4EjY+9PAe4HTiDcqXQxUBbLfxL4NTCjyRudCGI69mryVTvnnHPuwOT7FuvvAg8Da4H1wHfMbJmkYcBvzKyDmdUCaxIVJG0A9phZIm1TfIWSPgW2mNnmRrUo07AaCcbOgvITG7Vq55xzzuVOXh92Z2YbzOwsM2tvZp8zs0ej9Plm1iFNnZfMrHeGdQ43s580qj0A9lnmQoPOgi5HZi7jnHPOHaT69u3LkCFDqKioYOjQoQBs2LCByspK+vfvT2VlJRs3bgTgiSeeYNCgQQwbNoz169cDsHLlSs4999yCtT+T4p12ADhuxN9nn37AOeeca+bmzZvH4sWLWbQoPG1k2rRpjBgxghUrVjBixAimTZsGwH333ccbb7zBhAkT9j7Y7vrrrz9oJ4Es2gkgv/fLZ8OLN1dkLuicc84doIkTJ7J48eImXWdFRQX33HNPo+o+/fTTvPTSSwBcdNFFDB8+nNtuu42SkhJ27dpFTU0NpaWlzJ8/nyOOOIL+/dPMJ1hgRRvE7NW2S/g57vGCNsM555zLBUmMHDkSSUyYMIHx48dTXV1Nz57hIa5HHHEE1dXVAEyePJnTTz+dXr168cgjjzB27Fgee+yxQjY/o+INYhJTou+JxsR0+Vzh2uKcc65Fa+wZk6awYMECysvLWbt2LZWVlQwcOLBOviSiR5pQWVlJZWUlsG8SyOXLl3PnnXdy6KGH8sMf/pB27Q6eB8EW75iYGZVwU5d9Y2LUqqDNcc4553KhvLwcgO7duzNmzBgWLlxIjx49WL16NQCrV6+me/fudeokJoG8/PLLueGGG5g1axannnoqP//5z/Pe/kyKN4ipeiP8TJyJKfEgxjnnXMuyfft2tm7duvf13LlzGTx4MF//+teZNWsWALNmzeLMM8+sU++OO+7gqquuorS0lB07diCJkpISampq8r4PmRTv5aSEPbXhpwcxzjnnWpjq6uq9s1XX1tYybtw4Ro0axUknncQ555zDjBkz6NOnD48/vm9c6Mcff8zChQu54YYbALjyyis56aST6NKlC0899VQhdiMtD2ISz4nxy0nOOedamKOOOoolS5bsl96tWzdefPHFlHV69erFc889t/f92LFjGTt2bM7aeCCK93JSgl9Ocs4555olD2L8TIxzzjnXLHkQsye6O8nPxDjnnGtilnicRwtV6P3zIGbvmRjvCuecc02nrKyM9evXF/yLPlfMjPXr11NWVlawNvjAXh8T45xzLgd69+5NVVUV69atK3RTcqasrIzevdPO0ZxzHsT4mBjnnHM5UFpaSr9+/QrdjBYtr9dQJHWVNEfSdkmrJI3LUr6NpHclVcXSjpb0tKR1kjZIekHSgEY3ys/EOOecc81SvgeC/Aj4FOgBfAt4UNKgDOW/DySfh+sCPAMMiNazEHi60S168abw08/EOOecc81K3oIYSe2Bs4EpZrbNzBYQgpEL0pTvB5wP3BpPN7OFZjbDzDaY2W7gbmCApG4H1EA/E+Occ841K8rXqGlJxwOvmFm7WNo1wJfN7Gspyj8LzAA2Ao+YWcqRQ5LOAh40s55p8scD46O3g4G3D2Q/XKMdBnxS6EYUKe/7wvL+Lxzv+8IZYGYdc72RfA7s7QBsSUrbDOy3k5LGAK3MbI6k4elWKKk34RLVpHRlzGw6MD0qv8jMhja45e6Aed8Xjvd9YXn/F473feFIWpSP7eQziNkGdEpK6wRsjSdEl51uB0ZnWpmkw4G5wANm9osmbKdzzjnnmoF8BjHLgdaS+pvZiijtC8CypHL9gb7AfEkAbYDOktYAJ5vZXyUdSghgnjGzH+Sl9c4555w7qOQtiDGz7ZKeBG6WdAlQAZwJnJJU9G3gyNj7U4D7gROAdZI6AS8Qxtdc28BmTG9M212T8L4vHO/7wvL+Lxzv+8LJS9/nbWAvhOfEAA8DlcB64Foze1TSMOA3ZtYhRZ3hxAb2SroImAnUAPHGH2tmH+R0B5xzzjl30MhrEOOcc84511R81kPnnHPONUsexDjnnHOuWSqKIKahcza59CQdImlG1I9bJS2WdEYsf4Sk9yTVSJonqU9S3YclbZG0RtKkpHWnrevqktRf0k5Jj8TSxkWfy3ZJT0Vj0BJ5GX8HMtV1dUk6L5rTbbukldGYPj/2c0xSX0nPS9oY9eH9klpHeRWS3oz6701JFbF6knSbpPXRcpuiW1+z1S1Wkq6QtEjSLkkzk/Jycpxnq5uWmbX4BfgF8EvCA/dOJTxkb1Ch29UcF6A9cCPhNvgS4KuEZ/30JTwdczMwFigD7gBej9W9FZgPHAocA6wBRkV5Gev6st/nMDfqy0ei94Oiz+G06Dh/FHgsVj7t70C2ur7U6fdKYBVwcnT8l0eLH/u57/vnCTd1lAFHAEuBqwiP4VgF/DNwSJS2CmgT1ZsA/A/QO/qs3gEui/Iy1i3WBfgGcBbwIDAzlp6z4zxT3YxtLXRn5eHDaE+YdPLoWNpsYFqh29ZSFuAtwrxY44FXk/p+BzAwev8xMDKW/2+JL8tsdX2p09/nAY8TgslEEHML8GiszOej475jtt+BTHULva8H2wK8Cnw7Rbof+7nv+3eB0bH3dwD/AYwEPiK6USXK+yD25fkqMD6W9+3El2e2usW+AFOpG8Tk7DjPVDfTUgyXk44Gas1seSxtCeG/T3eAJPUg9PEyQp8uSeSZ2XZgJTAoekBhz3g+dT+HtHVz2f7mJnpO0s3sP9VGcv+tJApcyP47kKmui0hqBQwFDpf0vqSq6JJGW/zYz4d7gPMktZNUDpwB/JbQT29Z9M0XeYs0/cv+fZ+prqsrJ8d5PeqmVQxBTL3nbHINI6kU+Dkwy8zeI/T15qRiib7uEHufnEeWum6ffwNmmFlVUnq2vs/0O+B9Xz89gFLgH4BhhAd2Hg9cjx/7+fAy4UttC1AFLAKeInv/JedvBjpE42K87xsmV8d5trppFUMQU685m1zDSCohXJL4FLgiSs7U19ti75PzstV1hAGIwOnA3Smys/V9pr71vq+fHdHP+8xstZl9Avw7YZ43P/ZzKPp781vgScJliMMIYyduo+HHdydgW3T2xfu+YXJ1nGerm1YxBDF752yKpaWas8nVU/QfzAzCf6Znm9nuKGsZoW8T5doTxlcsM7ONwOp4PnU/h7R1c7QbzdFwwgDqDxTmErsGOFvSn9i//44iDFRcTvbfgUx1XSQ6hquo+6TwxGs/9nOrK/A54H4z22Vm64GfEgLIZcBx8TuOgONI07/s3/eZ6rq6cnKc16NueoUeOJSnwUmPEe7OaA/8HX530oH250PA60CHpPTDo749mzD6/Dbqjj6fBvyB8B/UwOigHVWfur4YQDvCXRmJ5U7g11HfJU6zD4uO80eoe3dS2t+BbHV9qfMZ3Ay8AXSPjuP5hEt8fuznvu//DFxLmPOvCzCHcCdd4g6jqwnB9xXUvTvpMsKg4HKgF+GLMfnupJR1i3WJ+riMcMfQ7Oh161we55nqZmxroTsrTx9IV8K10+2EkefjCt2m5roAfQj/fe4knAJMLN+K8k8H3iOcen8J6Burewhh7qwtQDUwKWndaev6kvKzuJHo7qTo/bjo+N4OPA10jeVl/B3IVNeXOv1UCjwAbCLcAnovUBbl+bGf276viPpmI/AJ4Q69HlHe8cCbUf/9CTg+Vk/A7cCGaLmduncjpa1brEv0t8WSlhujvJwc59nqplt87iTnnHPONUvFMCbGOeeccy2QBzHOOeeca5Y8iHHOOedcs+RBjHPOOeeaJQ9inHPOOdcseRDjnHPOuWbJgxjnioikmZKeLXQ74iSdKWmFpFpJM3O0jYNuv51zB86DGOfyJPoiNUlTktKHR+mHFaptBTYDeILwIMWrc7SNq4HzD2QFki6WtC17SedcvngQ41x+7QS+L+nwQjekKUUzmjemXhegG/CCmX1kZsmz3DYJM9tsZptysW7nXOF4EONcfs0D/gpMSVcg1ZkZSX2jtKFJZc6Q9KakHZLmS+ot6cuSlkjaJulZSd1SbON6SdVRmZ9KahvLk6R/kbQyWu9SSeenaMs3Jf1e0g5gQpp9OVTSLEkbo3X9l6RBiX0gPEIe4PfROoenWU8bSbdIWiVpl6Q/S7oqln+apD9K2hnt192S2sTy61xOkvSSpAeidX4iaa2kO6PZklN+JoQJB9tH7TRJN2bbxyi/s6TZ0TZ2Rm2fGMufIGl5lPeJpBcktY7l/6Okd6L85ZL+Od7ObPWda8k8iHEuv/YQJrG7TNLnm2B9NwETgb8lTJz2S+D/A+MJs14PIsyDEvdlwgyxIwiTsY0kTMaWMBX4NnA5cCxhErj/kPSVpPXcSphH6FjCvEypzIzadibwRaAG+G0UNL0atY+oHT2jtFRmARcCk4BjovZtApBUDvwG+G/CPDjfBr4ZtS+TbwG1wCmEif8mAuemKftqlF8TtbMnYQLObPsIoT+HAF8FBgD/BHwUtX0o8CPC5ziA8Jn8NrFRSZcCtxA+02OA7wH/Cny3PvWda/EKPdGUL74Uy0L4sns2ej2PaKZoQrBhwGGp3kdpfaO0oUll/j5W5ooo7YRY2o3A20lt2ERsBnLCWJFdhBms2xMmZxuW1PZ7gOeT2vK9LPvbPyp3WiytM2Em20ui94dFZYbXYz0pZ7QFfgCsAEpiaRdH+9Quue+j9y8BryWt53fATzK042JgWyP28Rng4TTr/EZUtmOa/A+AC5LSJgLv1Ke+L7609MVPOTpXGP8KvCbpjgNcz1ux19XRz6VJad2T65hZfIDqa0Ab4POEmWTLCGcS4rPDlhIug8UtytK2Ywhnnl5LJJjZZklLCWdv6uv4aD3zMmzndTPbE0tbQNinv6FuH8Ulp3/M/n2VTX328UHg15JOJARK/2lmf4jyfgesAv4i6QVgLvCkmW2Nxk0dSTgL9mBsm60JMzNnrN/A/XCuWfLLSc4VgJktJNyRc3uK7MSXsWJp6QbO7o6vNlp3clpDfs8TZb8GVMSWQYTLTnHbG7DeZJa9SJPItJ3dSe8b2lf12raZ/YZw59WdhDNPz0n6aZS3FTgBOIdw1mUy8J6kXrG2XEbdz2Iw0WW4LPWda/E8iHGucK4DhgGjktLXRT97xtIqmnC7QyS1j70/GfgUWAm8Q7gM08fM3k9aVjVwO+8S/sZ8KZEgqRNhfMg7DVjP4mg9/yfDdk5OGpR7Kvv2qal8CrRKse2s+2hmn5jZbDO7mDBm5yJJh0R5tWb2ezObDBxHuKT3VTOrJpwd+nyKz+L92LpT1m/C/XbuoOWXk5wrEDN7X9J09n82yvvAh8CNkq4ljEG5vgk33Rp4WNLNQC9gGvBjM9sOIOlO4E5JAl4GOhACnT1mNr2+GzGzFZKeJlwOGU8Yi/MDYAvwaAPWs1zS48BPJF0N/AnoDfQ1s9mEwcUTgQck/RA4Ktqn+82spr7bqYe/AmWSKgmDiGvqs49RP/8JWEbo+28AfzazXZK+SriM9zKwgRCodSQERwA3APdJ2gQ8TzgjdwJQbma31qO+cy2an4lxrrBuJtwhs1d0Oeg8wpfxEsKdJ9c14Tb/QPhCnQfMAX4P/EssfwphQPA1UbnfEe4e+ksjtvWPwELC4NaFQDvCAN0dDVzPhYSg4F7gPcJA3c4AZvYRcAZh7Mxi4GHgFzRtn2FmrwIPRetex74+y7aPuwiBzRLgFUKQ8bUobxNwFvBf0X5dQxgQPD/a5k8IdzNdENWfT7jz7C/1qe9cSyezfF2ads4555xrOn4mxjnnnHPNkgcxzjnnnGuWPIhxzjnnXLPkQYxzzjnnmiUPYpxzzjnXLHkQ45xzzrlmyYMY55xzzjVLHsQ455xzrln6X7mYwuJK2SQvAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 561,
       "height": 249
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Let's use the moons dataset",
   "metadata": {
    "cell_id": "30a961c801d9423cb8acd0e084518888",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "fdf428d5acf0463681e0d19a814438c1",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "24f49c44",
    "execution_start": 1658458298581,
    "execution_millis": 436,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 148
   },
   "source": "from sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_moons\n\nX, y = make_moons(n_samples=500, noise=0.30, random_state=42)\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "**Note:** to be future-proof, we set `solver=\"lbfgs\"`, `n_estimators=100`, and `gamma=\"scale\"` since these will be the default values in upcoming Scikit-Learn versions.",
   "metadata": {
    "cell_id": "9fbe6b5aff2d4077b01b1ca3f9f77c70",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 74.78125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "fd81385f247048a6a32c4d81cdcf4b21",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "2253734b",
    "execution_start": 1658458299022,
    "execution_millis": 528,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 292
   },
   "source": "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\n\nlog_clf = LogisticRegression(solver='lbfgs', random_state=42)\nrnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\nsvm_clf = SVC(gamma='scale', random_state=42)\n\nvoting_clf = VotingClassifier(\n    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n    voting='hard'\n)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "75f7ecc602494a8a867c5aecc253e080",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b3e67499",
    "execution_start": 1658458299557,
    "execution_millis": 223,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 92
   },
   "source": "voting_clf.fit(X_train, y_train)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 6,
     "data": {
      "text/plain": "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n                             ('rf', RandomForestClassifier(random_state=42)),\n                             ('svc', SVC(random_state=42))])",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n                             (&#x27;svc&#x27;, SVC(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n                             (&#x27;svc&#x27;, SVC(random_state=42))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "d71f1f96e275440e88be8dacda8c36e9",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e82a0152",
    "execution_start": 1658458299780,
    "execution_millis": 297,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 257.75
   },
   "source": "from sklearn.metrics import accuracy_score\n\nfor clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n    clf.fit(X_train, y_train)\n    y_pred = clf.predict(X_test)\n    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "LogisticRegression 0.864\nRandomForestClassifier 0.896\nSVC 0.896\nVotingClassifier 0.912\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Soft voting",
   "metadata": {
    "cell_id": "afe42fcbf8a1419696a9167d6e6b0a36",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "ad0f3aec7a95413ca9aa853fa00464ff",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b6aa5a3f",
    "execution_start": 1658458300079,
    "execution_millis": 208,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 236
   },
   "source": "log_clf = LogisticRegression(solver='lbfgs', random_state=42)\nrnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\nsvm_clf = SVC(gamma='scale', probability=True, random_state=42)\n\nvoting_clf = VotingClassifier(\n    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],\n    voting='soft'\n)\nvoting_clf.fit(X_train, y_train)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n                             ('rf', RandomForestClassifier(random_state=42)),\n                             ('svc', SVC(probability=True, random_state=42))],\n                 voting='soft')",
      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n                             (&#x27;svc&#x27;, SVC(probability=True, random_state=42))],\n                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;lr&#x27;, LogisticRegression(random_state=42)),\n                             (&#x27;rf&#x27;, RandomForestClassifier(random_state=42)),\n                             (&#x27;svc&#x27;, SVC(probability=True, random_state=42))],\n                 voting=&#x27;soft&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lr</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "2be38be9e94e4895a4622ab2f87391dc",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e82a0152",
    "execution_start": 1658458300286,
    "execution_millis": 330,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 257.75
   },
   "source": "from sklearn.metrics import accuracy_score\n\nfor clf in (log_clf, rnd_clf, svm_clf, voting_clf):\n    clf.fit(X_train, y_train)\n    y_pred = clf.predict(X_test)\n    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "LogisticRegression 0.864\nRandomForestClassifier 0.896\nSVC 0.896\nVotingClassifier 0.92\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Bagging and Pasting\n\n### Bagging and pasting in Scikit-Learn",
   "metadata": {
    "cell_id": "c0f328e43a0543cead32f0fb97e11eff",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 118
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "607756a6a0534840952d208e8430d0d1",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a3641d64",
    "execution_start": 1658458300668,
    "execution_millis": 823,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 220
   },
   "source": "from sklearn.ensemble import BaggingClassifier\nfrom sklearn.tree import DecisionTreeClassifier\n\nbag_clf = BaggingClassifier(\n    DecisionTreeClassifier(), n_estimators=500,\n    max_samples=100, bootstrap=True, random_state=42\n)\nbag_clf.fit(X_train, y_train)\ny_pred = bag_clf.predict(X_test)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "3a1910a71117409c9ef59e55202cbc07",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6570e191",
    "execution_start": 1658458301496,
    "execution_millis": 507,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 125.1875
   },
   "source": "from sklearn.metrics import accuracy_score\nprint(accuracy_score(y_test, y_pred))",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "0.904\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "cd9015380e55404984506bdc60dfc41c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3b8208f3",
    "execution_start": 1658458301506,
    "execution_millis": 500,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 161.1875
   },
   "source": "tree_clf = DecisionTreeClassifier(random_state=42)\ntree_clf.fit(X_train, y_train)\ny_pred_tree = tree_clf.predict(X_test)\nprint(accuracy_score(y_test, y_pred_tree))",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "0.856\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "A single Decision Tree (left) versus a bagging ensemble of 500 trees (right)",
   "metadata": {
    "cell_id": "4145bbf7bfde4d8cbac676ca770700ac",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a7dc9881ac5841c5b9df4ed9fe8a7ca8",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "996f7bb7",
    "execution_start": 1658458301518,
    "execution_millis": 3,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 387
   },
   "source": "from matplotlib.colors import ListedColormap\n\ndef plot_decision_boundary(clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.5, contour=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 contour:\n        custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n        plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", alpha=alpha)\n    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", alpha=alpha)\n    plt.axis(axes)\n    plt.xlabel(r\"$x_1$\", fontsize=18)\n    plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "18f79f4515134bbeb0d54b6632e9598f",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d46f392b",
    "execution_start": 1658458301525,
    "execution_millis": 1511,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 582.1875
   },
   "source": "fig, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\nplt.sca(axes[0])\nplot_decision_boundary(tree_clf, X, y)\nplt.title(\"Decision Tree\", fontsize=14)\nplt.sca(axes[1])\nplot_decision_boundary(bag_clf, X, y)\nplt.title(\"Decision Trees with Bagging\", fontsize=14)\nplt.ylabel(\"\")\nsave_fig(\"decision_tree_without_and_with_bagging_plot\")\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Saving figure decision_tree_without_and_with_bagging_plot\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 712,
       "height": 280
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Out-of-Bag evaluation",
   "metadata": {
    "cell_id": "d4ea54682a91425394769b3460675c9c",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 62
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "e27ff8e18b9d4bb4aef3ea7d8516b0f7",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "77384a30",
    "execution_start": 1658458303043,
    "execution_millis": 1775,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 182
   },
   "source": "bag_clf = BaggingClassifier(\n    DecisionTreeClassifier(), n_estimators=500,\n    bootstrap=True, oob_score=True, random_state=42\n)\nbag_clf.fit(X_train, y_train)\nbag_clf.oob_score_",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 15,
     "data": {
      "text/plain": "0.896"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "6ea073576cdd4b5b84c48efdda8a47ec",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5076203e",
    "execution_start": 1658458304053,
    "execution_millis": 774,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 92
   },
   "source": "bag_clf.oob_decision_function_",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "array([[0.32352941, 0.67647059],\n       [0.3375    , 0.6625    ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.06145251, 0.93854749],\n       [0.35465116, 0.64534884],\n       [0.01142857, 0.98857143],\n       [0.98930481, 0.01069519],\n       [0.97927461, 0.02072539],\n       [0.75586854, 0.24413146],\n       [0.0049505 , 0.9950495 ],\n       [0.75520833, 0.24479167],\n       [0.82122905, 0.17877095],\n       [0.98461538, 0.01538462],\n       [0.06315789, 0.93684211],\n       [0.00490196, 0.99509804],\n       [0.99004975, 0.00995025],\n       [0.92513369, 0.07486631],\n       [1.        , 0.        ],\n       [0.03409091, 0.96590909],\n       [0.35087719, 0.64912281],\n       [0.91111111, 0.08888889],\n       [1.        , 0.        ],\n       [0.96319018, 0.03680982],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.6635514 , 0.3364486 ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.0049505 , 0.9950495 ],\n       [0.        , 1.        ],\n       [0.19680851, 0.80319149],\n       [1.        , 0.        ],\n       [0.00591716, 0.99408284],\n       [0.38509317, 0.61490683],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.22346369, 0.77653631],\n       [0.32777778, 0.67222222],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.02906977, 0.97093023],\n       [1.        , 0.        ],\n       [0.01183432, 0.98816568],\n       [0.97916667, 0.02083333],\n       [0.88829787, 0.11170213],\n       [0.94594595, 0.05405405],\n       [0.96132597, 0.03867403],\n       [0.        , 1.        ],\n       [0.05612245, 0.94387755],\n       [0.98026316, 0.01973684],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.01005025, 0.98994975],\n       [0.98857143, 0.01142857],\n       [0.80769231, 0.19230769],\n       [0.44751381, 0.55248619],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.71523179, 0.28476821],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.84745763, 0.15254237],\n       [1.        , 0.        ],\n       [0.61578947, 0.38421053],\n       [0.12777778, 0.87222222],\n       [0.63387978, 0.36612022],\n       [0.9132948 , 0.0867052 ],\n       [0.        , 1.        ],\n       [0.16129032, 0.83870968],\n       [0.90607735, 0.09392265],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.00540541, 0.99459459],\n       [0.06358382, 0.93641618],\n       [0.02312139, 0.97687861],\n       [0.34939759, 0.65060241],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.84090909, 0.15909091],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.22340426, 0.77659574],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.96335079, 0.03664921],\n       [0.80628272, 0.19371728],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.25274725, 0.74725275],\n       [0.58235294, 0.41764706],\n       [0.        , 1.        ],\n       [0.03723404, 0.96276596],\n       [0.50543478, 0.49456522],\n       [1.        , 0.        ],\n       [0.01704545, 0.98295455],\n       [0.99456522, 0.00543478],\n       [0.2259887 , 0.7740113 ],\n       [0.45810056, 0.54189944],\n       [1.        , 0.        ],\n       [0.01570681, 0.98429319],\n       [0.98901099, 0.01098901],\n       [0.25698324, 0.74301676],\n       [0.88304094, 0.11695906],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.78285714, 0.21714286],\n       [1.        , 0.        ],\n       [0.00578035, 0.99421965],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.98895028, 0.01104972],\n       [0.99447514, 0.00552486],\n       [0.        , 1.        ],\n       [0.96534653, 0.03465347],\n       [0.99497487, 0.00502513],\n       [0.01092896, 0.98907104],\n       [0.1686747 , 0.8313253 ],\n       [0.95408163, 0.04591837],\n       [0.31515152, 0.68484848],\n       [0.98924731, 0.01075269],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.71929825, 0.28070175],\n       [0.36206897, 0.63793103],\n       [0.4       , 0.6       ],\n       [0.85326087, 0.14673913],\n       [0.95918367, 0.04081633],\n       [0.06349206, 0.93650794],\n       [0.81313131, 0.18686869],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.03508772, 0.96491228],\n       [0.98342541, 0.01657459],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.0052356 , 0.9947644 ],\n       [0.        , 1.        ],\n       [0.01932367, 0.98067633],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.94827586, 0.05172414],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.38674033, 0.61325967],\n       [0.27717391, 0.72282609],\n       [0.01117318, 0.98882682],\n       [0.        , 1.        ],\n       [0.30208333, 0.69791667],\n       [1.        , 0.        ],\n       [0.99378882, 0.00621118],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.98295455, 0.01704545],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.00502513, 0.99497487],\n       [0.61271676, 0.38728324],\n       [0.91747573, 0.08252427],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.99516908, 0.00483092],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.06930693, 0.93069307],\n       [1.        , 0.        ],\n       [0.05405405, 0.94594595],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.04294479, 0.95705521],\n       [1.        , 0.        ],\n       [0.93229167, 0.06770833],\n       [0.73295455, 0.26704545],\n       [0.62424242, 0.37575758],\n       [0.        , 1.        ],\n       [0.12755102, 0.87244898],\n       [1.        , 0.        ],\n       [0.96341463, 0.03658537],\n       [0.96226415, 0.03773585],\n       [1.        , 0.        ],\n       [0.02272727, 0.97727273],\n       [0.        , 1.        ],\n       [0.40223464, 0.59776536],\n       [0.85082873, 0.14917127],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.99408284, 0.00591716],\n       [0.00520833, 0.99479167],\n       [0.00540541, 0.99459459],\n       [0.96111111, 0.03888889],\n       [0.        , 1.        ],\n       [0.30808081, 0.69191919],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.00595238, 0.99404762],\n       [0.        , 1.        ],\n       [0.96236559, 0.03763441],\n       [0.80246914, 0.19753086],\n       [0.995     , 0.005     ],\n       [0.00543478, 0.99456522],\n       [0.04411765, 0.95588235],\n       [1.        , 0.        ],\n       [0.02808989, 0.97191011],\n       [0.        , 1.        ],\n       [0.04864865, 0.95135135],\n       [1.        , 0.        ],\n       [0.82777778, 0.17222222],\n       [0.        , 1.        ],\n       [0.8988764 , 0.1011236 ],\n       [0.99450549, 0.00549451],\n       [0.18857143, 0.81142857],\n       [0.20994475, 0.79005525],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.00549451, 0.99450549],\n       [0.        , 1.        ],\n       [0.25789474, 0.74210526],\n       [0.95212766, 0.04787234],\n       [0.00507614, 0.99492386],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.42857143, 0.57142857],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.04188482, 0.95811518],\n       [0.10404624, 0.89595376],\n       [0.98469388, 0.01530612],\n       [0.02185792, 0.97814208],\n       [1.        , 0.        ],\n       [0.34042553, 0.65957447],\n       [0.12315271, 0.87684729],\n       [0.52717391, 0.47282609],\n       [0.65151515, 0.34848485],\n       [0.00558659, 0.99441341],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.63679245, 0.36320755],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.22680412, 0.77319588],\n       [0.82089552, 0.17910448],\n       [0.03012048, 0.96987952],\n       [1.        , 0.        ],\n       [0.86910995, 0.13089005],\n       [0.        , 1.        ],\n       [0.00518135, 0.99481865],\n       [0.09137056, 0.90862944],\n       [0.00598802, 0.99401198],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.94318182, 0.05681818],\n       [0.1627907 , 0.8372093 ],\n       [0.95897436, 0.04102564],\n       [0.01630435, 0.98369565],\n       [0.59562842, 0.40437158],\n       [0.06989247, 0.93010753],\n       [1.        , 0.        ],\n       [0.78723404, 0.21276596],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.93684211, 0.06315789],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.23036649, 0.76963351],\n       [0.99418605, 0.00581395],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.85955056, 0.14044944],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.79255319, 0.20744681],\n       [0.91847826, 0.08152174],\n       [1.        , 0.        ],\n       [0.68306011, 0.31693989],\n       [0.50802139, 0.49197861],\n       [0.        , 1.        ],\n       [0.87790698, 0.12209302],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.9010989 , 0.0989011 ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.72139303, 0.27860697],\n       [0.09090909, 0.90909091],\n       [0.48170732, 0.51829268],\n       [0.25263158, 0.74736842],\n       [0.        , 1.        ],\n       [0.84782609, 0.15217391],\n       [0.81443299, 0.18556701],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.98930481, 0.01069519],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.03921569, 0.96078431],\n       [0.95959596, 0.04040404],\n       [0.95375723, 0.04624277],\n       [1.        , 0.        ],\n       [0.52542373, 0.47457627],\n       [1.        , 0.        ],\n       [0.0106383 , 0.9893617 ],\n       [0.99453552, 0.00546448],\n       [0.02659574, 0.97340426],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [0.98275862, 0.01724138],\n       [0.        , 1.        ],\n       [0.08421053, 0.91578947],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.00564972, 0.99435028],\n       [1.        , 0.        ],\n       [0.14124294, 0.85875706],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [0.37125749, 0.62874251],\n       [0.08695652, 0.91304348],\n       [0.23255814, 0.76744186],\n       [1.        , 0.        ],\n       [0.98181818, 0.01818182],\n       [0.19444444, 0.80555556],\n       [0.9902439 , 0.0097561 ],\n       [0.        , 1.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.95930233, 0.04069767],\n       [0.34285714, 0.65714286],\n       [0.98843931, 0.01156069],\n       [1.        , 0.        ],\n       [0.        , 1.        ],\n       [1.        , 0.        ],\n       [0.00561798, 0.99438202],\n       [0.02139037, 0.97860963],\n       [1.        , 0.        ],\n       [1.        , 0.        ],\n       [0.02564103, 0.97435897],\n       [0.6344086 , 0.3655914 ]])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "133ea8bb24fa4d1cbd7de4d099396eb7",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5fd98c1f",
    "execution_start": 1658458304064,
    "execution_millis": 765,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 128
   },
   "source": "from sklearn.metrics import accuracy_score\ny_pred = bag_clf.predict(X_test)\naccuracy_score(y_test, y_pred)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 17,
     "data": {
      "text/plain": "0.92"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Random Forests",
   "metadata": {
    "cell_id": "55b8c71fae8d471cb3d7b323169ee6f7",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 70
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "60e79c5678a2452f83ae9e9f4dea7779",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7a1a39db",
    "execution_start": 1658458304124,
    "execution_millis": 774,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 153
   },
   "source": "from sklearn.ensemble import RandomForestClassifier\n\nrnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, random_state=42)\nrnd_clf.fit(X_train, y_train)\ny_pred_rf = rnd_clf.predict(X_test)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "A Random Forest is equivalent to a bag of decision trees",
   "metadata": {
    "cell_id": "6b8a8b43887d433fa2fb875ac1e1093b",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "afafe449e05441d1a694bb7a650019b6",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8144b506",
    "execution_start": 1658458304903,
    "execution_millis": 4,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 130
   },
   "source": "bag_clf = BaggingClassifier(\n    DecisionTreeClassifier(max_features='sqrt', max_leaf_nodes=16),\n    n_estimators=16, random_state=42\n)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "3a5fadc4118146acbd5b240de66a1ed6",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "dd31eb4d",
    "execution_start": 1658458304912,
    "execution_millis": 32,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 94
   },
   "source": "bag_clf.fit(X_train, y_train)\ny_pred = bag_clf.predict(X_test)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "99911b7ee12f485abc0da3885fb3a9f3",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fdb05902",
    "execution_start": 1658458304951,
    "execution_millis": 4,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 92
   },
   "source": "np.sum(y_pred == y_pred_rf) / len(y_pred)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 21,
     "data": {
      "text/plain": "1.0"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Feature Importance",
   "metadata": {
    "cell_id": "f5715b793165407b93f7f77cc5840c8a",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 62
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "fe1cbf9ff521405ea7716ba71a611bd8",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "879a5ed5",
    "execution_start": 1658458304998,
    "execution_millis": 648,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 275.75
   },
   "source": "from sklearn.datasets import load_iris\n\niris = load_iris()\nrnd_clf = RandomForestClassifier(n_estimators=500, random_state=42)\nrnd_clf.fit(iris['data'], iris['target'])\nfor name, score in zip(iris['feature_names'], rnd_clf.feature_importances_):\n    print(name, score)",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "sepal length (cm) 0.11249225099876375\nsepal width (cm) 0.02311928828251033\npetal length (cm) 0.4410304643639577\npetal width (cm) 0.4233579963547682\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "8bf7ec3a15a74f809df771d0464428f0",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "843cec9a",
    "execution_start": 1658458305645,
    "execution_millis": 57,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 92
   },
   "source": "rnd_clf.feature_importances_",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "array([0.11249225, 0.02311929, 0.44103046, 0.423358  ])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "The following figure overlays the decision boundaries of 15 decision trees. As you can see, even though each decision tree is imperfect, the ensemble defines a pretty good decision boundary",
   "metadata": {
    "cell_id": "8efaf47371eb4f2abd88632ca4140099",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 74.78125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "27b32892280644719331a1f8decb62e9",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e44f48a4",
    "execution_start": 1658458305705,
    "execution_millis": 456,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 521
   },
   "source": "plt.figure(figsize=(6, 4))\n\nfor i in range(15):\n    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n    tree_clf.fit(X_train[indices_with_replacement], y_train[indices_with_replacement])\n    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.45, -1, 1.5], alpha=0.02, contour=False)\n\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 399,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "**MNIST pixel importance (according to a Random Forest classifier)**\n\n**Warning:** since Scikit-Learn 0.24, `fetch_openml()` returns a Pandas `DataFrame` by default. To avoid this and keep the same code as in the book, we use `as_frame=False`.",
   "metadata": {
    "cell_id": "c05f8db91b764478918053c1f9dd4f6f",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 111.171875
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "ff3b091f6aeb4196b4cd9f59e17d4ce6",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e0e6612d",
    "execution_start": 1658458306170,
    "execution_millis": 30600,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 130
   },
   "source": "from sklearn.datasets import fetch_openml\n\nmnist = fetch_openml('mnist_784', version=1, as_frame=False)\nmnist.target = mnist.target.astype(np.uint8)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "651cc323b7e54a07ba0ad003f38fe0e2",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "26cb5dca",
    "execution_start": 1658458336777,
    "execution_millis": 50577,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 110
   },
   "source": "rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)\nrnd_clf.fit(mnist['data'], mnist['target'])",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 26,
     "data": {
      "text/plain": "RandomForestClassifier(random_state=42)",
      "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "92bd0c7bff634b33b332b7b2771e497a",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "294225b8",
    "execution_start": 1658458387257,
    "execution_millis": 0,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 130
   },
   "source": "def plot_digit(data):\n    image = data.reshape(28, 28)\n    plt.imshow(image, cmap = mpl.cm.hot, interpolation=\"nearest\")\n    plt.axis(\"off\")",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "5bba7fa5ed044566a276bc8c4c2ba30c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "db2ba74b",
    "execution_start": 1658458387258,
    "execution_millis": 444,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 233.1875
   },
   "source": "plot_digit(rnd_clf.feature_importances_)\ncbar = plt.colorbar(ticks=[rnd_clf.feature_importances_.min(), rnd_clf.feature_importances_.max()])\ncbar.ax.set_yticklabels(['Not important', 'Very important'])\nsave_fig('mnist_feature_importance_plot')\nplt.show()\n",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Saving figure mnist_feature_importance_plot\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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OQuUzacoCQC4chcqFsgBQHTOLfCgLAE0ws8iFsgBQHTOLfCgLANVRFvlQFgCa4DBULpQFgOqYWeRDWQBogrLIZaTK4rcCmcgiqA2BzLMDGUlaeHIgtG8gE9nxkw/rjTx4yv/2Zq4LbOr7gczPApnIw4pcvW5hYKXc5sCqvFWBba0MZKTYfkeuuBhZazlqf3h509l8RqosAIyOUSs4bB9lAaA6Zhb5UBYAmmBmkQtlAaA6Xg2VD2UBoAkOQ+VCWQCojplFPpQFgOooi3woCwBNcBgql5Eqi8gvX2SB0+GBTPjqZfvVGWzTov7MQ4v6F9xtDOxOZDFd5Oc4J5CZFshEFu7dFFhwd0NgnLWBTGR/JGl1MLc7YmaRz0iVBYDRwcwiF8oCQHXMLPKhLAA0QVnkQlkAqI63+8iHsgBQXZH00K7eCVRFWQBogplFLpQFgOo4wZ0PZQGgCWYWuUyasog8C4kslJsSyFwdyLwrkJEk3dsfefDc/kzkynSR/Z4VyKwIZCI/68jCvamBzIZA5nuBTGQxXeRKeZGr20nSfYHMLwOZjM/AmVnkM2nKAkAulEUulAWA6njpbD6UBYAmmFnkQlkAqI5zFvlQFgCa4DBULpQFgOqYWeRDWQCojhPc+VAWAJpgZpELZQGgOg5D5TNSZRH55VsTyESmx58JZCRpy7n9meMD40QuUfq0QGaPQGZuIBO5ZOisQGZ5IBO51Gngqqqhce4PZKKX1K21yj3rH1UOQ+UyUmUBYDQws8iHsgDQBGWRC2UBoDpeDZUPZQGgCWYWuVAWAKpjZpEPZQGgCWYWuVAWAKrj1VD5UBYAmuAwVC4jVRaRX77IIqhNgcxXAhkptuBuaSAzP5A5KZCZdnIgdFUgE9ihbwXGuS6wqR8HMhsCmciCu1qX75V27wV3fZhZ5DNSZQFgNFAW+VAWAJrgMFQulAWA6phZ5ENZAGiCmUUulAWA6phZ5ENZAGiCssiFsgBQHW/3kQ9lAaAJZha5pCuLyC9oZPFW5EpxknRxIDM1kDkykIns06EX9Gd+Ehhn6sr+zKrAOJEr3EUWt0UWykUWW9ZcSMcfw23jnEU+6coCwOTAYahcKAsA1TGzyIeyANAEM4tcKAsA1TGzyIeyANAEZZELZQGgOtZZ5ENZAGiCmUUulAWA6jhnkc9IlUWtX77IOJEFXlJssdi+gcwPA5k9ApkbAplaV5SLjBPZ50gmevW6PpHHFVm4h+3jMFQ+I1UWAEYHM4tcKAsA1TGzyIeyANAEM4tcKAsA1XGCOx/KAkATHIbKhbIAUB0zi3woCwBNUBa5UBYAquPVUPmkK4vIs5lIZn1we5EFZRuCY/WZFshEHlvkyn1PCmRqLe6L/AwfCmRYTDe5MLPIJV1ZANj1mFnkQ1kAaIKZRS6UBYDqeDVUPpQFgCY4DJULZQGgOmYW+VAWAKqjLPKhLAA0wWGoXCgLANUxs8iHsthBtRYBRmyoNA6wMzCzyIWyAFAdM4t8KAsATVAWuVAWAKrj7T7yoSwANMHMIpcpu3oHAOQzds5iIh+7mu0DbT9gO/JGyLsdygJAE49O8KOP7RW277E9bei2k2xfG9kf29faPmlb3y+l3FFKmV5K2eXdZXue7WK7ytEf20fbvnNHxqAsAFTXcGaxh6T3VN3ZSaZWQdRGWQBoovbMovMpSafanrW1b9p+ge2bbG/s/vuC7vYzJb1I0qe7Q02f3sp9H/NsvpuJfML2Dd19rrA92/Zltu/rxp83dP9i+xTby22vs/0p21O6702xfbrtld3saLHtfcZt962275C0VNJ3umE3dNt+vu1n2l5q+95u/MuGfw7dzOtU2z/qHv8S23t1M7GrJM3txnrA9tz4j3xguw22qRRPdEAAeFS6epP01AnebS/by4a+vrCUcuG4zDJJ10o6VdLpw9+w/RRJV0o6RdLlkv5C0pW2Dyml/K3tP5R0aSnlogns019KOkbSOkk3dh/vlPRmSZdI+oikRUP5V0k6UtJ0SddI+pmkiyS9pft4iaR7JC2W9GlJJwzd98WSfleD7pwj6eeSZpVSHu4e3yGS/l6DIpkp6cuSzpD03qExXidpoaQtkq6X9JZSygW2j+0e+/4TeOyPMSmnOwBGWyllYcPh/07S9bbPG3f7n0m6tZTy+e7ry22fIunlkv71CW7rs6WU2yXJ9lWS5pdSrum+/qKkj4/Ln11KWS9pve1zJR2vQVm8UdI5pZTl3X0/JOkW28NFc0YpZVP3/cftSCnlNkm3dV+utX2OBmU17PxSyupujCskLXgiD3prOAwFYKSUUm6R9A1JHxz3rbmSVo67baWk/XZgc2uGPn9wK19PH5dfNW7bY4d7xu/bSg2erM/Zxn0fx/Yc21+wfZft+yRdqsfP3u4e+nzzVvbvCaMsAIyij0h6mx5bBKslHTQud6Cku7rPy07YrwPGbXt19/n4fTtQ0sN6bPmUbXw+5qzu9sNLKTMlvUlS9FTBDj92ygLAyOkOySzR4PzEmG9KOsz2G2zvafv1kuZrMAuRBn+YD268a6fZfrLtAzR41daS7vbLJb3P9jNsT9fgD/+SsfMRW7FWg3MXw/s7Q9IDkjba3k/SaRPYrzWSZo+dVH8iKAsAo+pjkn695qKUcq+k4yS9X9K9kj4g6bhSyroucp6k19r+he3zG+3T1yTdLOkHGpxsv7i7/RJJn9fg5PTPNTgB/e5tDVJK2SzpTA3OzWyw/TxJH5V0hKSN3dhfie5UKeWnGhTW8m68Cb8ayqXsjJkZAORmu0g6tJv1pMPMAgDQi7IAAPTiMBQAoBczCwBAL8oCANCLsgAA9KIsAAC9KAsAQK//B/92u0EIOST2AAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 395,
       "height": 280
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Boosting\n\n### AdaBoost",
   "metadata": {
    "cell_id": "f098e97655764cb0962546e96640fc56",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 118
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a2b088e545dd4a19a57287fef8b455fc",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f0355d48",
    "execution_start": 1658458387703,
    "execution_millis": 372,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 200
   },
   "source": "from sklearn.ensemble import AdaBoostClassifier\n\nada_clf = AdaBoostClassifier(\n    DecisionTreeClassifier(max_depth=1), n_estimators=200,\n    algorithm='SAMME.R', learning_rate=0.5, random_state=42\n)\nada_clf.fit(X_train, y_train)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 29,
     "data": {
      "text/plain": "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n                   learning_rate=0.5, n_estimators=200, random_state=42)",
      "text/html": "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n                   learning_rate=0.5, n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1),\n                   learning_rate=0.5, n_estimators=200, random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=1)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "8f69b3df5eb44a4889e0c3daa2be2ed8",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d23ae3ba",
    "execution_start": 1658458388071,
    "execution_millis": 431,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 92
   },
   "source": "plot_decision_boundary(ada_clf, X, y)",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 399,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Decision boundaries of consecutive predictors",
   "metadata": {
    "cell_id": "743d30030fd44d0e90dcde3fa0c54d63",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "f2499008e64644bb8857c5d53514deca",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a16d7eb7",
    "execution_start": 1658458388513,
    "execution_millis": 3158,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 570
   },
   "source": "m = len(X_train)\n\nfix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\nfor subplot, learning_rate in ((0, 1), (1, 0.5)):\n    sample_weights = np.ones(m) / m\n    plt.sca(axes[subplot])\n    for i in range(5):\n        svm_clf = SVC(kernel=\"rbf\", C=0.2, gamma=0.6, random_state=42)\n        svm_clf.fit(X_train, y_train, sample_weight=sample_weights * m)\n        y_pred = svm_clf.predict(X_train)\n\n        r = sample_weights[y_pred != y_train].sum() / sample_weights.sum() # equation 7-1\n        alpha = learning_rate * np.log((1 - r) / r) # equation 7-2\n        sample_weights[y_pred != y_train] *= np.exp(alpha) # equation 7-3\n        sample_weights /= sample_weights.sum() # normalization step\n\n        plot_decision_boundary(svm_clf, X, y, alpha=0.2)\n        plt.title(\"learning_rate = {}\".format(learning_rate), fontsize=16)\n    if subplot == 0:\n        plt.text(-0.75, -0.95, \"1\", fontsize=14)\n        plt.text(-1.05, -0.95, \"2\", fontsize=14)\n        plt.text(1.0, -0.95, \"3\", fontsize=14)\n        plt.text(-1.45, -0.5, \"4\", fontsize=14)\n        plt.text(1.36,  -0.95, \"5\", fontsize=14)\n    else:\n        plt.ylabel(\"\")\n\nsave_fig(\"boosting_plot\")\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Saving figure boosting_plot\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 712,
       "height": 280
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Gradient Boosting\n\nLet's create a smple quadratic dataset",
   "metadata": {
    "cell_id": "584e201808ca4b9f9251316e5b8e5f06",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 100.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a9d2dc60600d4e86ae7ea421f6ac2d14",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1dd669",
    "execution_start": 1658458391681,
    "execution_millis": 0,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 97
   },
   "source": "np.random.seed(42)\nX = np.random.rand(100, 1) - 0.5\ny = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Now let's train a decision tree regressor on this dataset",
   "metadata": {
    "cell_id": "57f0aa883b824ef9bdb22e452f71be4c",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "beb8e0dfb01a40ae9e996065600ecf11",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "45623854",
    "execution_start": 1658458391681,
    "execution_millis": 8,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 115
   },
   "source": "from sklearn.tree import DecisionTreeRegressor\n\ntree_reg1 = DecisionTreeRegressor(max_depth=2, random_state=42)\ntree_reg1.fit(X, y)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 33,
     "data": {
      "text/plain": "DecisionTreeRegressor(max_depth=2, random_state=42)",
      "text/html": "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
     },
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    }
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  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "7ae6b2b878494d32916ce98819663e1c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c7b659b8",
    "execution_start": 1658458391690,
    "execution_millis": 9,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 97
   },
   "source": "y2 = y - tree_reg1.predict(X)\ntree_reg2 = DecisionTreeRegressor(max_depth=2, random_state=42)\ntree_reg2.fit(X, y2)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 34,
     "data": {
      "text/plain": "DecisionTreeRegressor(max_depth=2, random_state=42)",
      "text/html": "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
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  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "88a84542a5da41dc918f9a092e7d3364",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "7c745d0d",
    "execution_start": 1658458391701,
    "execution_millis": 8,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 97
   },
   "source": "y3 = y2 - tree_reg2.predict(X)\ntree_reg3 = DecisionTreeRegressor(max_depth=2, random_state=42)\ntree_reg3.fit(X, y3)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 35,
     "data": {
      "text/plain": "DecisionTreeRegressor(max_depth=2, random_state=42)",
      "text/html": "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "414b015ca64246af8ba3484c441d89ea",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c287fe",
    "execution_start": 1658458391712,
    "execution_millis": 0,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "X_new = np.array([[0.8]])",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "73811b68f57041919393824cb5dbabd3",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5c1b90a",
    "execution_start": 1658458391745,
    "execution_millis": 0,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "y_pred = sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "61c6887f63c040f28e9c9ac49485c0f1",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ba549d12",
    "execution_start": 1658458391746,
    "execution_millis": 59430238,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "y_pred",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 38,
     "data": {
      "text/plain": "array([0.75026781])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "In this depiction of Gradient Boosting, the first predictor (top left) is trained normally, then each consecutive predictor (middle left and lower left) is trained on the previous predictor’s residuals; the right column shows the resulting ensemble’s predictions",
   "metadata": {
    "cell_id": "fbe99a5209484c4a881d0da04af1fb04",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 97.171875
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "69662ea175a34ea78e0844058b527c94",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1bccb16b",
    "execution_start": 1658458391746,
    "execution_millis": 3,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 192
   },
   "source": "def plot_predictions(regressors, X, y, axes, label=None, style=\"r-\", data_style=\"b.\", data_label=None):\n    x1 = np.linspace(axes[0], axes[1], 500)\n    y_pred = sum(regressor.predict(x1.reshape(-1, 1)) for regressor in regressors)\n    plt.plot(X[:, 0], y, data_style, label=data_label)\n    plt.plot(x1, y_pred, style, linewidth=2, label=label)\n    if label or data_label:\n        plt.legend(loc=\"upper center\", fontsize=16)\n    plt.axis(axes)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a9b2bb94f0b5409d8dfe42e04885ab60",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c00847e0",
    "execution_start": 1658458391748,
    "execution_millis": 2305,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 624
   },
   "source": "plt.figure(figsize=(11,11))\n\nplt.subplot(321)\nplot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h_1(x_1)$\", style=\"g-\", data_label=\"Training set\")\nplt.ylabel(\"$y$\", fontsize=16, rotation=0)\nplt.title(\"Residuals and tree predictions\", fontsize=16)\n\nplt.subplot(322)\nplot_predictions([tree_reg1], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1)$\", data_label=\"Training set\")\nplt.ylabel(\"$y$\", fontsize=16, rotation=0)\nplt.title(\"Ensemble predictions\", fontsize=16)\n\nplt.subplot(323)\nplot_predictions([tree_reg2], X, y2, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_2(x_1)$\", style=\"g-\", data_style=\"k+\", data_label=\"Residuals\")\nplt.ylabel(\"$y - h_1(x_1)$\", fontsize=16)\n\nplt.subplot(324)\nplot_predictions([tree_reg1, tree_reg2], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1)$\")\nplt.ylabel(\"$y$\", fontsize=16, rotation=0)\n\nplt.subplot(325)\nplot_predictions([tree_reg3], X, y3, axes=[-0.5, 0.5, -0.5, 0.5], label=\"$h_3(x_1)$\", style=\"g-\", data_style=\"k+\")\nplt.ylabel(\"$y - h_1(x_1) - h_2(x_1)$\", fontsize=16)\nplt.xlabel(\"$x_1$\", fontsize=16)\n\nplt.subplot(326)\nplot_predictions([tree_reg1, tree_reg2, tree_reg3], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"$h(x_1) = h_1(x_1) + h_2(x_1) + h_3(x_1)$\")\nplt.xlabel(\"$x_1$\", fontsize=16)\nplt.ylabel(\"$y$\", fontsize=16, rotation=0)\n\nsave_fig(\"gradient_boosting_plot\")\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Saving figure gradient_boosting_plot\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 792x792 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 775,
       "height": 784
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Now let's try a gradient boosting regressor",
   "metadata": {
    "cell_id": "6724fee4dd114af6916b2e1fc8f496c6",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "edaf977b70c24bc8a5c2dbe4bbf872b8",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "122daaea",
    "execution_start": 1658458394058,
    "execution_millis": 9,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 120
   },
   "source": "from sklearn.ensemble import GradientBoostingRegressor\n\ngbrt = GradientBoostingRegressor(max_depth=2, n_estimators=3, learning_rate=1.0, random_state=42)\ngbrt.fit(X, y)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 41,
     "data": {
      "text/plain": "GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n                          random_state=42)",
      "text/html": "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n                          random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=1.0, max_depth=2, n_estimators=3,\n                          random_state=42)</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "GBRT ensembles with not enough predictors (left) and too many (right)",
   "metadata": {
    "cell_id": "8866874787294066aea3ba6c0395a04d",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "ae09a5d23c79494cb10e6e0acbc122d7",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1b0d80ea",
    "execution_start": 1658458394070,
    "execution_millis": 55,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 84
   },
   "source": "gbrt_slow = GradientBoostingRegressor(max_depth=2, n_estimators=200, learning_rate=0.1, random_state=42)\ngbrt_slow.fit(X, y)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 42,
     "data": {
      "text/plain": "GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)",
      "text/html": "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(max_depth=2, n_estimators=200, random_state=42)</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a7772f17bc934767baef7d0fdd8e70ba",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "45bdf111",
    "execution_start": 1658458394131,
    "execution_millis": 830,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 318
   },
   "source": "fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n\nplt.sca(axes[0])\nplot_predictions([gbrt], X, y, axes=[-0.5, 0.5, -0.1, 0.8], label=\"Ensemble predictions\")\nplt.title(\"learning_rate={}, n_estimators={}\".format(gbrt.learning_rate, gbrt.n_estimators), fontsize=14)\nplt.xlabel(\"$x_1$\", fontsize=16)\nplt.ylabel(\"$y$\", fontsize=16, rotation=0)\n\nplt.sca(axes[1])\nplot_predictions([gbrt_slow], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\nplt.title(\"learning_rate={}, n_estimators={}\".format(gbrt_slow.learning_rate, gbrt_slow.n_estimators), fontsize=14)\nplt.xlabel(\"$x_1$\", fontsize=16)\n\nsave_fig(\"gbrt_learning_rate_plot\")\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Saving figure gbrt_learning_rate_plot\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x288 with 2 Axes>",
      "image/png": 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JPgVcYK2dZYw51BizNWC9K3CnspuHmyD8GNw5n0VEREREkiKhZ27z5i88PsTy93GD3/zb63AzOYiIiIiIpESiK74iIiIiIhkpoRXfVCgrg2nTYPRoKC1Nd2tis2nTJtauXUtFRUW6myKS04qKiujYsSNt2rRJd1NSZts2mDIlu2KmiEiyZFXiu20bjBkDFRVQVATvvJP5gby8vJxVq1bRs2dPmjdvjjEm3U0SyUnWWnbs2MGyZcsoLi6mWbNm6W5SSsyZA9ddlz0xU0QkmbKqq8OWLS7prapy19OmpbtF0a1Zs4ZOnTpRUlKipFckjYwxlJSU0LFjR9asWZPu5tRnLVRVUfZBFX+8xV1T5V0aoaddwh1VF3Nb+cXYyZNh3boENVhEJDOUlbkjW2Vl0dfNqopvq1awfn1txXf06HS3KLry8nK6du2a7maIiKdVq1asy7Tkr6rKlWI/+YRSIJFF2c6s4WKmggXeAx5sA1dfncBnEBFJn7Ky+HoDZFXi26KF26Fs6uNbWVlJQUFWvcwiTVpBQQGVlZXpbkZda9fCJ58AUBVwIM4YyDNAdXWDH3pnl168OeIq9qv4H53eeNz1fRARyXbz5sE//kHeh7u4pRyqLeSVQ95VwIjwm2VdRlZaWj/hzfQBb+riIJI5MvLzuH07AOVd+9B+06L6lYtGtLm4Z2fGvXwxvD0I3ngcvv8+42OmiEhUN94ITzzBgcCB/jILfOBdwsi6xDdYvCVuEZGM4yW+zdqV8M7zSUpK+/cHYOd3CxQzRST7+V3WJk5kUbNBfP+9C3N9+3r3X3FFyM2yPvGdNq3+gDcFcRHJKjt2uOuSkpBHtRKiVy8oKKB4zXJm04ddFDJ5561Mm3aiYqaIZB+vYMCECfQdNYq+wfeHSXyzalaHUEaPdlWL/PzsGfCWzR555BGMMSEvbdu2TXfzGszfr/nz50dcb9GiRRhjeOSRR1LTsCTz93vRokU1y/r27cvZZ58d9+M89NBDMT2+hOAH8JKS5D1HQQEcfjgAfVjCAL7nLP6pmCki2WnbNncdZ9zM+opvaWn2DXhrCp599ll69uxZZ5kG8TUNL7zwAq1bt45rm0ceeYTKykp+8Ytf1Fk+fvx4ysrK6NatWyKb2PSkIvEFeO01WLaMOY9/zB7Xnsxhg9fTWjFTRLKRHzdbtIhrsyaRqSTt0KCENXToUAYMGJDuZuS0nTt3UlxcnPDH3W+//RL2WJ06daJTp04Je7wmK1WJb34+9OnDHsdtgWuh9a71yX0+EZFkaWDczPquDpKZ/EPcH330EaeffjqtW7eme/fuXHLJJZSXl9esV1lZyXXXXUf//v1p1qwZHTt25JBDDuGDD+oOyXzggQcYMmRIzTrnnHMO69fX/dI2xnDttddy++2306dPH0pKShg/fjyrV69m9erVnHzyybRp04ZevXpx6623hmz3Dz/8wPHHH0/Lli3p0KEDF110ETv8/pcRvPfee4wZM4ZWrVrRokULjjrqKL755puo25199tn07NmTGTNmMHz4cJo1a0bfvn25++67Q76e06dP56c//Slt27blwAMPrHkNp0yZwqBBgyguLqZ79+785je/qfM6AyxYsIDx48dTUlJCp06duPTSS9m5c2e9NoXq6rBw4ULOOOMMunbtSnFxMf369ePSSy8FYPTo0bz33nt8+OGHNd1eRnvHz0N1ddi1axfXXnstffv2paioiL59+3Lttdeya9eumnX8LiX3338/119/Pd26daNt27b8+Mc/ZtmyZXXa9uSTT7LffvvRsmVLWrduzeDBg7n//vujvvYZxQ/gzZun5vnat3fXmTafsYhIrHK1q0MoGT9VT6ZMp2RtgzetqqqqNxdqXl4eeXl1f0udccYZnHrqqTz//POUlZVxww030K5dO2688UYAbr31Vv76179yyy23MHToUDZv3synn35aJ6m9+uqruf3227nkkku47bbbWL58Oddeey3ffPMNM2bMID8/v2bdxx57jH322Yd77rmHVatWcdlll3HmmWeyZcsWfvSjH3Huuefy7LPPcvXVVzN48GCOOeaYOu2dMGECJ598MhdeeCEff/wxN910E9u2bYvYp/e///0vxx13HOPHj+fxxx+v2a9DDz2Ur776il69ekV8LTdv3swpp5zCpEmTGDBgAE8//TSXXHIJrVq1qpeAnn766Zx66qn8+9//rnn9J0yYwH/+8x8mTZrEQQcdxOzZs7nuuutYtGgRzz33HAAVFRWMHTuWHTt2MHXqVDp37sz999/P888/H7Ft4JLeESNGUFJSwk033cTAgQNZsmQJb775JgD33HMPEyZMoKqqqibhjNRV4qyzzuKZZ57hmmuu4ZBDDmHGjBnccsstLFiwgCeffLLOulOmTOGggw7ioYceYvXq1fzmN79hwoQJTPNO2/jBBx8wYcKEmvdGdXU13333HRs3boy6XxklVRVfn5/4rl/v4oAxmR83RUQCNbCrA9barLkccMABNpoZM6xt3tza/Hx3PWNG1E2S6ttvv62/0H3VpP/SAA8//LDFzZRX7zJ+/Ph6611//fV1th8/frwdOHBgndsnnHBC2OdbuHChzcvLszfeeGOd5R988IEF7AsvvBDwsmIHDhxod+3aVbPs17/+tQXszTffXLNs165dtlOnTvbss8+u197zzjuvzvP8/ve/t3l5eXbOnDk17QHsww8/XLNO//797RFHHFFnu02bNtkOHTrYSy+9NOy+WWvtWWedZQH71FNP1Vl+5JFH2t69e9vq6uo67bvsssvqrDd9+nQL2EcffbTO8scff9wC9osvvrDWWvvAAw9YwJaVldWsU1VVZffaay8L2IULF9Ys79Onjz3rrLNqbp9xxhm2RYsWdvny5WH3Y9SoUfbggw+ut9xvt//4X3/9tQXs7373uzrr3XzzzRawM2fOtNbWvs6jRo2qs95tt91mgZq23HbbbbZdu3Zh2xVOyM9lOt1xh/tMXnJJyLuBT22i42ZJiXvOLVsyLm6KiERUXV2by1RVhVwlXNxscl0dQk1vlnHSn/I2qtoLbgDUJ598Uudyxx131Ftv/PjxdW4PHjyYJUuW1NwePnw4r776Kr/97W/54IMPqKioqLP+W2+9RXV1NaeffjqVlZU1lwMPPJBWrVoxffr0OuuPHTu2ziC7QYMGAXDUUUfVLCsoKGDAgAEsXbq0XntPPvnkOrd/9rOfUV1dzccffxzydZg3bx7ff/99vfaVlJRQWlpar32h5Ofnc+KJJ9Z73iVLlrB8+fI6y0844YQ6t19//XWKioo46aST6jz/uHHjAGqev6ysjF69ejFy5MiabfPy8urtbyhvvvkmxx57LN27d4+6bjR+eyZMmFBnuX/7vffeq7M8uCI/ePBggJr30PDhw9mwYQMTJkzglVdeyb5Kry/VFV+o092h7PVNNN+5kZKqzZkbN0VEfH4XxGbNIC++VLbJJb6a3iw19tlnH4YNG1bnEmqwW3v/y9VTXFxcp1/pNddcw4033sjLL7/MoYceSocOHfj5z3/O2rVrAVi9ejUAAwYMoLCwsM5ly5YtrAvqo9iuXbs6t4uKisIuD+4DC9ClS5eQt4MTUJ/fvnPOOade+1555ZV67QulXbt2FBYWxvS8wbMjrF69moqKClq0aFHnuTt37gxQ8/wrVqyot2+h9jeUdevW1ZvBo6H8LizB+9G1a9c69/tCvX+Amv/dqFGjePbZZ1m6dCknnHACnTp14sgjj+Srr75KSHtTJp2J77HHcvlNbVlX3Y7NtOF28xvFTRHJbI2ImU2mj29g/zRNb5Y9CgsLmTRpEpMmTWLlypW88sorXH755Wzfvp1//etfdOjQAXBVx+DkFai5P1FWrVrF3nvvXec2QI8ePUKu7z//lClTOPLII+vd7yfekWzYsIFdu3bVSX7DPW/w6XY7dOhAs2bNeP/990M+tl+l7datG7Nmzap3v/88kXTs2DFs4h8vP5FduXIl/b0zifm3A++Px0knncRJJ53E1q1bmTZtGpMmTeLoo49m2bJl9fqcZ6x0Jr7ffIPNy6O6uDn5O7ZxzoDptFTcFJFM5g9si7d/L00k8Q112uLJk9PdKolX165dmThxIq+++mrNjAhjx44lLy+PJUuWMHbs2KS34ZlnnuGII46ouf3000+Tl5dXM4NCsD322IO+ffsya9Ysrr766gY9Z1VVFc899xw/+9nP6jxv7969wybcvqOPPppbb72VTZs2MWbMmLDrlZaW8vDDD/PRRx/VdHeorq7mmWeeidq+cePG8fzzz7NixYqw8/EWFxezZcuWqI912GGHAW7/fvvb39Ysf+KJJwBqZoNoiJYtW3LssceyYMECLr30UtatW5c9U6mlelYHYMnAI+jt9WmYnPcnTr3vSIacNZSWRRWRNxQRSbdcr/jqtMWp9+WXX9Z0Rwg0bNiwuE5kcdxxxzFkyBD2339/2rVrxxdffMHrr7/OeeedB0D//v2ZNGkSF198MXPmzGHUqFE0a9aMpUuX8tZbbzFx4kQO985GlQivvvoqV155JePGjePjjz/mxhtv5Mwzz2TgwIEh1zfGMHXqVI477jgqKio4+eST6dixI6tWrWLGjBn07t2byy+/POJztmrViquuuoq1a9cycOBAnnrqKd5+++2aqcAiGT16NKeeeionnXQSl19+OSNGjCAvL49Fixbx6quvcuutt7L77rtz1lln8cc//pGf/OQn/OEPf6Bz587cd999bN68OeprcuONN/Lqq69y0EEHcc011zBgwACWL1/O66+/XjOLxV577cU999zDv/71L/r370+rVq3YY4896j3WPvvsw6mnnsoNN9xAZWUlBx10EGVlZdx8882ceuqpNX14Y3X99dezatUqDj/8cLp3786yZcu46667GDp0aPYkvZCWiu8Tu13Hn/N+RWW1YZttQ78vZjMEIMQUdyIiGSXXE1+/X69f8VX/tOT76U9/GnL5mjVr6NixY8yPc9hhh/Hss88ydepUtm/fTu/evbnqqqvqVAP/8Ic/sOeeezJ16lSmTp2KMYZevXoxZsyYsAlpQz3++OPcfvvt3HvvvRQVFfHLX/6SP//5zxG3OeaYY5g+fTq33HILEydOZMeOHXTt2pWRI0dyyimnRH3O1q1b8/TTT3PppZfy9ddf06VLF+68807OOuusmNt8991389BDD3HLLbdQXFxM3759Oeqoo2r68BYVFfHWW29x8cUXc+GFF9KiRQtOO+00xo8fz/nnnx/x8fv27ctHH33Etddey+TJk9m6dSs9evTguOOOq1ln0qRJzJkzh4kTJ7J161ZGjRpVM+VYsEceeYR+/frx0EMP8fvf/57u3bszadIkfve738W0v4EOPPBA7rrrLn7961+zfv16OnfuzLhx47j55pvjfqyUs5Zv73qbBe8vY/S3s2kJKU18R4+Gm4vb1sTNYQcXwx24QCoikoH8bq0/brONfaBBXR2MbeTo/lQaNmyY/fTTT0Pel6lzUM6ePZs999wz3c2QDHX22Wfz9ttv1zspgyRXJnwuZz7yBUN+vn/dha+/DgEzkPiMMZ9Za4c15Hlijpu9lkGvXtC9OySoT7eISKKUlcGDox7nZ7seo6NZx/72Mxg3Dt54I+T64eJmk6j4gk5bLCLZ5bv3VjEEWE533jLj2P2wbhyUwG47sagTN9d4p79WxVdEMtC0aXDdruvYjUXu7AEA/frF/TjZlfjOmgUBI+4T5v/9P5gyJfGPKyISxr57uzPvzWQoFzZ7mHemANEnAUkefwYS9fEVkQw0ejS0wg2inlD4Lybf3pG9f3lQ3I+TXYlveTl8+23iH3f+fCW+khaRToUsTdueA1ziO2DPAt75RwYcsSoOX/HN1K5kIpI7Skuhqng77IRfvT6evY+Iv38vZFviu9deEMP0S5F8+SWccw7s2gXFhdV8snNfd0NEJJUqXeK7+54FkAnJZGDF11rwZhQJNV2kkl8RSbnqavJ3ujO2HTi64VM/Zlfi27x5o7s6vPYyzKyEqmrIrwRrDMZaqK6O+7R3sbLWRp2WSkRSI2MG9HqJL3FM/5dUeXmuLZWV7uKdUEXTRYpIRvBPU9y8eaPytSw5rVHiBJ/S2OZ7Xzr+l1CCFRYWssP/Z4lI2u3YsaPeKaLTItMSXwjZz1engReRjNCIs7UFyqCImxqlpXVPaZw3pgAqd7kvoRhOLxuvzp07s3z5cnr06EHz5s1V+RVJE2stO3bsYPny5TXzG6dVVZW7zqTEt7jYTQwf0M+3tO1sPv/dXL6eZeh7+iEML43/tNIiIo2WoBP9ZFDETZ7ggRl1pvApSG7Ft3Xr1gD88MMP7FJfYpG0KiwspEuXLjWfy7TK4Irvpx/u5K1v4Mghaxh+4v4MKi9nEMCqcXBU6DkzRUSSShXf2EQdmJHkxBdc8psRX7Qikjn8mJOfn952BPJmdjj9pxV8Xwmf55fxbEU5tG0LGzfCvHlpbZ6I5LAEVXybfB/fUAMz6khB4isiUk8GV3xb7NrI2KrXOH6XN4vOySe76/Xr09QwEcl5Car4NvnEN+rADCW+IpIOmZj4ehXfv3IZr3EMp9sn3PJx49z0Zps2KVaKSHqoj29sggez1ZuGx//SUf9bEUmlTEx8vYrvQa2/gY2wZe+RtDp0KBx7LLRr5yq+GzdCx47pbKWI5CL18Y1dncFswVTxFZF0yMTE16v4Fm5cC0Crf9wBBx7o7mvf3iW+69cr8RWR1PMrvurq0EhKfEUkHbyYM+PjAsrK0twWX/CUju3a1f9b/XxFJB28iu/n35U0KmYq8fUnso+Q+JaVwZQpZM6Xk4hkvaULXcx5d3oBY8ZkSHzxKr412rev/3eMia/ipogk0qLZruI77ZMWjYqZCU18jTHtjTEvGGO2GWMWG2NOi7Du/saY6caYrcaYVcaYSxPZlliUlcGq9ZErvv50aNddR+Z8OYlI1luywMWcClsQesaZdAiu+LZtW/u3l/i+/sCSqHFQcVNEEm3pbFfx3WpLGhUzE13xnQpUAF2A04F7jTF7B69kjOkIvA7cD3QABgBvJrgtEfmBedlKl/h+9XnoxDfqdGgiIhGEq3z27eliTrUpyJxTAQdWfFu1qtP/eGWFS3yPfukCxh+xI2Iyq7gpIg0VLmb277gJgK2mdaNiZsISX2NMC+BE4Dpr7VZr7QfAy8AZIVa/HHjDWvuEtXantXaLtXZ2otoSCz8wV3rj+774JHTiq/PUi0hD+T+wr70WRo2CBx6ova9HV3fK4iOOKqh/Yp10Caz4tq97auI3O51e83ePioURk1nFTRFpiEgxs3vzDQCMOqF9o2JmIiu+uwOV1tq5ActmAvUqvsBIYL0xZoYxZrUx5j/GmN6hHtQYc64x5lNjzKdr1qxJWGP9wFzlJb4HDAmd+PrTod18c4izvomIRDBtGuzcCdXVbsbEiy4KqGJ43auOGFuQ8LjS4LgZWPENHNgGDDyzlPfyRgPQu+CHiMms4qaINETEmLnBJb7jJ7RrVExJ5Dw6LYHNQcs2Aa1CrNsT2B8YC3wN/Al4Cjg4eEVr7QPAAwDDhg2ziWqsH5h7nFYAi2CfQeEHt0WcDk1EJIzRo13Vs7ra3a6udoG9tJSkTmfW4LgZWPENSnxLS2HNkd3hTbhj0g8MjBITFTdFJF5HjNjKWeZ58tkJQF4VLLu3C4z8cU3iGxyb4pXIiLsVaB20rDWwJcS6O4AXrLWfABhjbgTWGmPaWGs3JbBNEZWWAv1c4qvpzEQkUcrKak+a87e/uapFdbUrqNZUSjNxHt9hw+DBB93fBx1U7+5O+7rEd2DJD5EfZ8ECWLXK/d2jB/QOeUBPRHLd4sXw1Vd89x18/TWMWfwQD1W9WHu/BR4DfvF/tTPKZFDiOxcoMMYMtNbO85YNAWaFWPcr3O74ElbJjUdZGXRZWkA/UOIrIgnh91GrqHAF1HfegenTQ5w9MhMT33PPheOPd23r3r3e3YsqutMXWPHZD3QL9xiffw4HHFB7Oy8P5s+H3XZLfHtFJHtVVbkf22vXMggY5C2uLipmzVETWLHCsPu6MkoWzoJvvklYxTdhfXyttduA54GbjDEtjDEHA8fhcvVgDwMnGGOGGmMKgeuAD5JV7Q01QtD/cvpunvvSmf21El8RabxQMxqUlsLkyUGH/v3ENz8/Da2MoHNnyhZ3Dxkzr7vXJcPd/n03nz/7fejt53l1j/btoXVrV+r+Psy6IpK7Nm2CtWupyi/kFY7lPxzLf/gx/znhYbq8/HeGfvIgJed58yMsWJCRXR0ALgQeAlYD64ALrLWzjDGHAq9Za1sCWGvfNcZcA/wXKAE+AMLO+dsYoaovpaW1X067vJdg1sxK9kxGA0Qkp/gDZ/2YE3YQWCZWfIkcM2dUHViz3tp/vgo//VX9B9jp+uZxzDHu0OSrr8KOHalpvIhkj02u1lnZsRsnb/5PbcwJPKtDv37u+rvv3CmL8/OhZctGPW1C5/G11q631h5vrW1hre1trX3SW/6+n/QGrHuvtbaHtbadtfbH1tqliWyLL9x8ksGzOgzeUxVfEWm8mGc0yNDEN1LMXFHcl9+b6wDYp9Xi0A9QUeGui4uheXP3d3l5MpssItnIS3yLO7cJHzP9xNc//NSuHRjTqKfNrIibBOGqL/6XU5vzCuBr2KO/El8RSYyYZjTI0MQ3WsxcfccgeAa67wqT+PoV36IiaNbM/a3EV0SCeYkvbdqEj5kDBkBhIWzc6G736tXop82siJsEfrCuN7DEu4/BLvF9941d3PoQnHiiG98hIpJUGZr4Ro2Z1X3gGVj96WLOOCpEzPQT38CKr7o6iEiwgMQ3rDZt4I034IsvXKX36KMb/bSZFXGTJGL1xfvS+efDlbwJvOmdOFnJr4gkS1kZdJxdyUDIuMQXosTMPn0AaL5oNscvuoCqN+GjN/ox8t9XuC+mwK4OfnKviq+IBIsl8QU4/HA4/HA3TeSLMHpj4+YIz7yIm2rel04BtV0dnntOia+IJIc/eOzfO6oYCMyeV5BdA2u7dWNLQVtaVW7kAu5zy54HvhoHQ4bU7ergn7lDFV8RCRZr4kv4QbcNkdDBbVmpsBCom/ieeGK6GiMiTZ0/eCzfizkzZ2VZ/SE/nzcnvcsF3MMF3MNcV7eu7YMX2NVBfXxFJJw4Et9wg24bIssibhJ4Fd8zflbJwvXq4ysi4QWeka2h1QZ/8FjhDpf4Dt4/+8Lwib/fj3W99+O556D5shfh23m1Vd3Arg55Xm1FFV+RnBU2bvqJb9u2UR8j5mkiY5B9ETfRvMT34AMreeOy9DZFRDJXqENtEH8i7A8e63FaJSyCvffNzjB87rlekeD45vAtbo5NqNvVwe+/rIqvSO75+mu+f3wGT/7Vdfd/sgA6/9rdNXcOHLz4f7SGmCq+kQbdxis7I24i+YFZpywWkQiCD7X985/w6KMN63NWWgp0d4lvJg5ui0vwzA2BXR2srXufiOSOcePov3Ild/u3dwF/cn/2D1yvU6eYHi6maSJjkOURNwGU+IpIDIIPtUHoUxPHLEOnM4tbSYm7DtXVwZ9oXhVfkdxSXQ0rVwLwj/xzqaqG/DzYfXeY/Z37TWwM7D2qEwcfc0xKm5blETcBlPiKSAyCD7VB3Ypv3H3OmkriG67iW1TkTi8aeJ+I5Ab/x26zZuz17v114uZlgV3G/gA0T23TsjziJoASXxGJUfChtkb1OWvqiW9xcc2sOar4iuQYPx40b57YuJkAWR5xE0CJr4g0UKP6nPkxx6+KZis/8fUHtwV2dfD7hKjiK5JbAhLfYInqq9tQSnyV+IpIOuRCxVfz+IrkpgiJb7rpBBZKfEUkHaqq3HW2J77Bg9sC+/gq8RXJTUp8M1gDEt+yMpgyxV2LiDRIU634BnZ18O7bvm6HYqZILsngxDfLI24CxJn4JvJ80SKSAA89BC+/nLCHW78e1q6Fjh2hffuEPWx9q1a566aW+Ibo6tBs4becd017/lpwFUy/WjFTpImpd3Y2Jb4ZzP/S+egjuPHGqKtXvg+TyqHaQl45VF4HHBpm5U6dYOLE2gEeIpJ4V1wBGzYk7OHae5eUKCmJ6XSdGS3SdGa9erGh3W6027CQ9mzgpMqneHWaEl+RpiRkQVCJbwbzv3Q++8xdojiUgDzXAu94l3B69IDjjmtMC0UkEj/Reuqp2j6lUXz3HXwzC/bZGwYNql3+7+fgySfd3Ot5eXDaaXDSiUlos2+vvaBlyyQ+QQpEmtWhuJjvXprLVWO/4P2dIygxO+Kf71hE0q5eRTdA8Fktp02D0j28xDfGmJxKSnyPPx7uvBPWrYt5k6XLYPEi6NMXevUMs9ILL8DXX8OmTQlopIiEVV3tro87LqbqQlkZjDktdHelHl3g9edq77vyQkDVycjCDW4rLgag9NAC7niqC/wEenfawUC9niJZJVoXz+CzWo4eDSxQxTdzNWsGl1wS1ya9vEtES5a4xFezRYgkl5/45sU2VjdkdcIL4sFnZ9Mh+RhE6urgOeBQlxwXV2k+X5FsEylmQpi4+a03k4sS36YnbPnfn5Ten7JIRJLD/4zFmPiGrE4ESPfk6lnH/2KbNQtGjXKjA6Gm4ltnHS85jnTYVEQyS8iYuW4dHHUU/PAD4A6MlTZvDiMeAMZocFtTFbH8r/mBRVIjzoqvqroJ1rOnS3K3bYPp092yTp3q9l0O6AdcNsMy5kijmXFEskTImPlKWehxUU8+6RIjJb5NU8Tyvyq+IslnrbtAzIkvqKqbUB06wNy5sHBh7bI996w7TVteXk3J6P23d1JR0SzsYVMRyTz1YqY/mPXYY+H++92P3lNPhQUL3HIlvk1TxEOmSnxFks9Peo1xF0mP3r3dJZKSEqioYPSBOygqaha2q4mIZAE/8W3fHrp3h2HD3G0lvk1bxEOm6uogknxxdnOQNGreHDZuZMQ+23nnnXbqaiKSyX74AZYvr7use3c3RSvUJrb+rC69e7s4vHQprF4NGze65Up8s0esgy/CHjJVxVck+ZT4ZpSIcdP/Apwzh9L3/07pyJFQelSKWygiUS1eDP37189f8vNh/nzo27d+Rdc7YQ2LF0OXLrXbKPHNDgk5LbEqviLJp8Q3Y0SNm/4X4FVX1Q6K2bUr+0/ZLNLULFjgkt6WLWvP8DN3LmzeDHPmuMTX7+oQmNhOnAh/+UttF7QOHeDww1Pa9Fjo2yKEUIPW4qaKr0jyxTmVmSRP1LjpHxL97rvaZWvXpqh1IhKzXbvcdWkpfPKJu4wZ45Zt3equQ/XhvfZaN53hhg3uMn8+7LFH6todI31bhOAPWsvPb8TgC1V8RZIvyRXfsjKYMsVdS2RR46b/BbltW+2yVatS1DoRiZmf+BYW1i5r1cpdb9nirv2Kr/+D1pMNMVPHmEJIyDyfqviKJF8SE9+EdHnKIVHjZqi+fkp8RTJPRYW7Djj7Yr3EN0TFN1tiphLfMBo9z6df8VXiK5I8fuLr/9BMoGin6ZT6IsbNUInvypVJbY+INEAsFd8QiW+2xEx1dUiAkKV9/4tYXR1EkieJFd+EdHmSWkGHRAHefWpVRh8SFclJfsU3MPH1z8QYoatDtsRMVXwbKWxpXxVfkeRLYuKrUxsnWIiKb/PXX+CDtzfQ+cr+9P/DOWlolIjU41d84+zqkC0xU4lvI4Ut7aviK5J8SR7cplMbJ1Dr1vUWlVJGaWUZTAFOHQGDB6e+XSJSV6SuDpFmdSA7YqYS30YKe9piDW4TST7N45s9zjsP1qyBnTuZdej53Pub+bStXMupPMnedpab+kiJr0j6herqEOOsDtlAiW8jhS3tazozkeTTPL7ZY4894LHHANgbOH3YEUybBh0+WQYvzIJly9LaPBHxNLCrQ7ZI6LeFMaa9MeYFY8w2Y8xiY8xpUdYvMsbMNsZkZcTzB7UBTJ4cVN5vRMU3G+bBE8kIqvhmneC42XVYT3dj6dKEPK7ipkgjxTOPbxYmvomu+E4FKoAuwFDgv8aYmdbaWWHWvxJYA7RKcDuSLup8dV7F96svqthWFnufl2yZB08kIyRxOjNJvJDxrVcvd+fSpZSVNWxgjOKmSAJFmtVh6VK4//7asy5mYVeHmMokxpg3jTEfhVg+2BizyxhzujGmBXAicJ21dqu19gPgZeCMMI+5GzABN6wh60Q7Pee8he6L+KsvKhkzJvYqREJOlyySK1TxzSoh45uX+Fa9+DJtD96LOb/9Z1wxM+zjikjDhOrq0KGDu16+HM4/352S2JiQg1YzXawV3w+Ba4wxxdbanQDGGAPcA8yw1j5hjNkPqLTWzg3YbiYwKsxj3g1cA+xoWNPTK+ygNs+3cwoYCOTZqrgmco72uCISQIlvVgkZ33YfDK1akb9lC3sym3N4kMcrzoxr8nvFTZEEClXx7dEDpk6FmTNrl40YAW3bprRpiRBP4lsE7Af4ld8zgZHeMoCWwOag7TYRohuDMeYEIN9a+4IxZnSkJzbGnAucC9C7d+8Ym5t80ear23OffHgGCk1lXIE4W+bBE8kISnxDyq642QGWLmXOPe+wxzUnUsL2uJNXxU2RBArVxxfgwgtT35YkiDXx/QiowiW6Hxlj2gJ/Av5mrf3GW2crEFzzbg1sCVzgdYn4E3BMLE9srX0AeABg2LBhNsb2pkSk+ep238u9tIP3quKdB+MLxNkwD55IRlDiG1LWxc02bdjjuEFwDfTptJ13Xoo/BipuiiRIqK4OTUhMia+1dqsxZiYu8QW4BagGfhew2lygwBgz0Fo7z1s2BAge2DYQ6Au873pLUAS0McasBEZaaxc1YD8yjzfYZlD/SlAwFkkOTWfWdHiDZDqW7KCjYqZI+oTq6tCExPNt8SEw0hizP3A+cKW1tqZrg7V2G/A8cJMxpoUx5mDgOOCxoMf5BuiFm/VhKDARWOX93bj5bDKJTlksknyNqPhq+qsM40+L5E+TJCLpEaarQ1OJmfFMZ/YB8Cvgn8CH1trHQ6xzIfAQsBpYB1xgrZ1ljDkUeM1a29JaWwms9DcwxqwHqq21K0M8XvYKM49vQ6frEZEQGjidmaa/ykD+tEg76o53jilmTp8OEye6pLlDB3jxRdhttyQ2VqQJC9HVoSnFzHgS3w+960HA/qFWsNauB44Psfx93OC3UNtMA3rG0Y7s4H8RB5y5rSm9cUQyQgMrvqGmv9JnMc0CK77WgjGxx8xnn4V5Xg+75cvh7bfhl79MWdNFmpQQXR2aUsyM59tiK+7kFH+z1n6VpPY0HSG6OmiuSZEEC0h84zkM509/lZ+v6a8yRkGB+6Ktrq6pOMUcM9evd9dt2rjrHVk5S6ZISoWNmSG6OjSlmBlPxfd6YD11B7RJOCEqvpprUiTBvMR36/a8uI6maPqrDNW8ufvS3b4diopij5l+4tuzJ2zapMRXJIqIR1P8im9AV4emFDMjJr7GmBLczAyHApcCP7XWbkpFw7JeiIpvU3rjiGQEL/HdvDUv7sNwmv4qA5WUwObNLvFt2zb2mOknvj16wKxZSnxFoojYdSHM4LamEjOjVXyPBF4ClgOXWmtfSH6TmogQFV+I7Y2jAXAiMfJ+WLZqk0fROh1NyXp+P9+AxDWWmLnjh/U0B1YX9aQzaGYIkSgiHk0JdwKLJiJi4mutfRkwKWpL09LA6cw0AE4kDl7Ft1WbPB1NaQr8mR22b3dfvs89Bxs3wqhRsOeeITcpK4M9lq2jOfCP13swGVTxFYki4tGUEF0dmpJ4+vhKPBo4nVlTGjkpknQB05k1lcNwOS2w4vvUU3DWWQBsL+nA6nOuoe++reGMM6C4uGaT996t4kA2ArC0qnvt9iIS2rRpcO65lG7fTmn79nDKi0C/2vtzueIrjeBXfOOczkwD4ETioFMWNy2BFd+vaicPKtm+jr53/8bdWLMGJk+uuW/MsE3kYdlIG8oLWsIulPiKRBI8/d9LL8Gvf117vxJfaZAQFd9w1dzgKrAO2YrESIlv0xI4l+/33wNwqbmLrvYHeprlnGEfg9//nh33PsyO7dC8BIbnuy9p2649k64sgWtQH1+RSDZscNdDh8KXX8LcuXXvV1cHaZAYpzMLVwVWwisSAyW+TUvg2du8xPezolI+qhxGcWE1P+nxJS2+/5rm2+fRHNz5QT3txo2g3dD6g+NEJIif+B58sEt858ype78qvtIgMU5nNmWK+vSKNJgS36bFr/jefHPNl/FfXurPO5/D6NF5tNj/E+6/ZjF33AFV1ZCfB5ddBuedb6B/f3fqYlDiKxKJP/3fyJEwdSp8+ilMmFB7/7Jl7lqJr8QlxunM1KdXpBH8H5ZKfJuGft4Am5kza26POKodI47yVyhm35N2Z/G9tTFz35OAgd7dgRVjEQnNr/jutx+0b+8S4SeeqLtOYSF06JD6tqWAEt9kiXE6M/XpFWkEVXybluuuc4GwvNzd3n//eqtEjJmBfYRFJDQ/8e3UCT780FV8g+25p0uKmyAlvskSZjqzUNSnVyQ+/oDQEwqqGQRKfJuKoiI36CGKsDEzxAkwRMQpK4Np/2e5esMGd4KGdu2gc2cYNCjdTUspJb7J4lV8t2+uZGZZ7ImtztomElnggNAv8qt5Bmp/aErWa0gM9LcZt3dzDgAlviJB/LhZvHMLk6urqGregvwm2oc3GiW+SfLJ5/kMB8q3VzFmTGxnYNNZ20SiC5wWsMrW7eqgH47ZrSExMHCbuwtL+AGU+IoE+eL5hVxQ/iJtrRvYtrWoPW3IzZipxDdJps8oYDhQQGXMszXEOs+vSC6rMyA0vxoqgLw8/XBsAhpy5srAbTZZr6vDxo3cc+VC9vvJbnoPiACnvX8Bbe0bNbfzenTL2ZipxDdJDhnlDr3mUxXzbA3xzPMrkqsCBzf9pLoargXy8nS67yagIbPcBG5jC4uprsgnr7qKC//cjxF3z+TO/9tX7wPJeW2XfQPA5yPOp+uAlnS/7GSmvZ2bMVOJb5IceLB7aYvzK2NOVjXPr0hsagY3/au2q4OmBsx+DZnlpu42hvevv4FRb18HQJ9d85g2TYmv5LgdO9ypiQsK2P/Du2vGII2uzM2YqcQ3WbzBNgVVFZSe2jfmzUq9C/e727/eCadVgwVMNXS5q/a+pDriCHjooRQ8kUgjBMzjq6kBm4aGzHITuE3ZTdfy5P/N4bSqx2mdvz1nvsxFwlq40F336VM71Sq5O52qEt9kyc+HwYPh669h8eIGP0wzoI9/wwIrE9C2WDz8MPztb7UTwoukkd/PvUMHWLcuIEgHzeOrqQGltBT6HVsCL8FvL9tGP70fJJfMmgWXXAJbt7JlK2zZDM0rN9MO2NihP22DVs/FmKnEN1mMgc8+c4cXss3ee7sJ4GOYg1gk2fx+7jt3ujw3Lw+Ki73+7n7iq+nMJECX3dwP9n5ddSILyTFPPw3vvgtAK+/im/p5KUfEMb1qU6XEN5kKC6Fv36Q8dFJneojxrHMiqeAPWvNz3OrqgP7u3XTmNgnBP1IVcAY3zY4jOWHrVgA+Kb2ES/93GlVeiNxJMbOq9yVvmt7/SnyzUNJnevCrZ36mIZJG/qC1wIpvzUCM2Up8JYQWLdy1l/hqdhzJGd57vsMhe/LllwfWO1KmPu9KfLNS0qdtiuN0yyLJFjgAo14f31lKfCWEoIqvprqTnOG95/vtUxI+buY4Jb5ZKOnTNvlJhBJfyRBhB2BUK/GVEIISX011Jzlj2zZ3XVKSkwPXYqHENwslfQoSVXwlWwRMZyZSw098vSQgV6dtkhzk92v3u/tIPUp8s0TwwIyk/pJT4ivZQhVfCcVLfOd8uZ31ZSmImSKZIqDiK6Ep8c0CKR+YocFtki00nZmE8O3iFuwFbPh2JX8Y/SaPjryX9kVbI2/Upg389a/Qq1dK2iiSFKr4RqXENwukfGCGKr6SLVTxlRA+m13CXsBIPuI/FUfB9Bg3HDECrroqmU0TSS4/8VXFNywlvlkg5QMzNLhNsoUSXwlhSGkJPFh7e81RE+h02enhjww88wz8/e+wZk1qGiiSLH5XB1V8w1LimwVSPjBDFV/JFkp8JYR9R9ZWu+bc8m/2uObEyBssXuwS33XrktwykSRTxTcqJb5ZIqUDM5T4ShQZcxYsJb4SSkC1a48T9oq+focO7lqJryRJymKmBrdFpcRX6lPiKxFk1FmwNJ2ZhNK8ee3fAwZEX1+JryRRymJmdTWUl7u/Az8DUocSX6lPszpIBMkebBlXZUQVXwmlY0c47zzo0QMKC6Ovr8RXkihVMfOIA3dwILikVzExLCW+Up8Gt0kE4QZbJuJQXtyVEU1nJqEYA/fdF/v6SnwliZIaM2dYjhmzk4oKeKhwPfNAA9uiUOKbg6J+2NTVQSIINdgyUYfy4q6MqOIridC+vbtevx6++84lzgG++AI+/tjNdrbfcb11GFniksyY2fXMsWwof8fd2OktVP/eiJT45piYPmxKfCWK4MGWiTqUF/fUfUp8JRGKiqB1a9i8Gfbcs97d+3kXgPLuu9FsyTwdZZC4JCVmbtjAbt+7pLecYgCKigx5J52UkDY3VQlNfI0x7YF/AOOAtcBka+2TIda7EjgL6OOtd4+19rZEtqWpSdSI0Jg+bEp8JU6Jmms67qn7lPhKGHHHzMsvhyfrfV2xbj2sXev+HsB8mv2w0CXI7dolsrmSYxISM7//HoBt/Qdz1zlfpX+WnSyR6IrvVKAC6AIMBf5rjJlprZ0VtJ4BzgS+AvoDbxpjllprn05we5qERI4IjenDpsFtEqfGzjUdnKTEvL0SXwmhQTHzd79zlyBzAx5raVV3urHCzZWqxFcaIRExc9Vd33M80GJwfyZPTnwbm6qEJb7GmBbAicA+1tqtwAfGmJeBM4CrA9e11v4p4OYcY8xLwMGAEt8QEjkiNKYPmyq+0gANnWu6UT/sNJ2ZhJCsmNn2nhawjNq5UkViUV0Nb79de2bAPn3gkEMaFTPPO3wuL+10qdUPzfvTPYHNbeoSWfHdHai01s4NWDYTGBVpI2OMAQ4F7g9z/7nAuQC9e/dOTEuzTKJPWRz1w6ZZHSSJgqu7jUpSVPENKdfjZtJi5tMlLvH1z44lEos334Qf/ajuss8+g/33j2nzUDHzuZ3j2Y1FAMzcsbsS3zgkMvFtCWwOWrYJaBVluxuAPODhUHdaax8AHgAYNmyYbVwTs5NOWSxNRXB19447YMmS2rdc3EmKpjMLKdfjZtJipj9NlCq+Eo+lS911//7QqhV8+SVceSUccoi7feGFYWdiCHVErGvJZgYyH4C/FlzBQRedmqIdaRoSmfhuBVoHLWsNbAm3gTHmYlxf30OttTvDrSc6ZbE0DYHV3Z074aKLwFooKIBf/hLOPDPO97kqvhJGUmKmn5yo4ivx2LHDXf/oRzBhAowcCe++6y4AM2bAqNAHx7e+BeeVQ7WFvHJYegU0+98nAHzNYFpMvY0Dj0zFTjQdiUx85wIFxpiB1tp53rIhQPDANgCMMb/A9f09zFq7LIHtkMbS4DZJksBD0Hl5LgH232a9ezcgUVHiK6mkiq80hP9DqaQEDjwQnngC5s1zv/5vvRVeeMFdQhjrXQCwwIza+5bSS+dcaYCEJb7W2m3GmOeBm4wxE3GzOhwHHBS8rjHmdOAPwOHW2gWJaoMkSAwV30RNryZNRyzvicBD0B06wK9+Bbt2ubdcg/phKvGVVGpExVcxM4f5FV//xCennQa498SKxQdQums63bqF33zFCli2DHr2dLe7PXsXAPPy9mh0//VclOjpzC4EHgJWA+uAC6y1s4wxhwKvWWtbeuv9HugAfGJqz5DzuLX2/AS3RxoiyuC2UH2OQEE9l8UzM4N/CLqsrPYEWUEnyoqdEl9JJb/iG2fiq5iZ44ITXwLfEydSVHRixJjZzbvUbPfSJVxQcSd/KbhKU2E1QEITX2vteuD4EMvfxw1+82/vlsjnlQSLUvENHoX/z3/Co48mZp5hyTCbN7u+Z4sWRVxtaDksL/du7IDi0UCUs2YOLYcVfs/+ndDsCKBZnO3zExAlvpIKfsU3zq4Oipk5LrCrgyfwPVFe7t4TsbwHpk2DuVX9uYS7yK9q3FR9uUqnLJb6oiS+wVMFQeLmzJQM8+WX7hJFc+9So8K7xLNNuXeJV7NmMU8LJNIoDaz4KmbmuBAV39Gj3aDeqio3wPehh2Ib3OtvV13trtXVIX5KfKW+KIPbgqcKgrrVC30Qm5AKL3s97DB48cWIq/7mN/Dww278RX4e/Pa38OtfR374Rx5xs/pUV0NxsXuKESPibGOzZnW+UESSpoEVX8XMHBci8S0thZ//HO6/3yW+VXFUb62tey3xUeIrdZSVQcvZ+QyGiIPbgqcKSuk8w5I6u3a565KSqKdoPemXcO/TtV/mI38ERDmr64py2ABUWcjfBe98DiOOSkjLRRIvRMU31kFripk5LERXB3AV3nh/AE2bVlsljidZllpKfKWG39n+3vI8BgPz51QxIMZtUzrPsKSOn/gWFkZdtSEnDUj0GbZEkiqo4tuY020rZuaQEBVfUMxMFyW+UsPvbF9pXVeHubNjT3ylifK7OvgdE6OI98s85WclFGkMv+L74YcwaRJ8BDd6JxfYXN6WGW9cTGlptJOVSs7xE98QZ2dTzEw9Jb5Sc6iuQweX31SX54OFPQbqzG05L6jim4y5SFX5kqzRpYu7njULZs2iFKh561r4fkd74Lz0tE0yUlkZ9F2w3U1HlqCxCIqZjaPEN8cFH6q74w444B/58DH076szt+U8v+JbWNiow7oiTcKYMcz/7cM8cusqqqrcOOAf/xi6zHyDvgv+j/6t14bf9vXX4fLL3dm6Ap13Hlx1VXLbLWnhx8xPduygG/DlnOYMHZ7uVokS3xwXPL/kunWw/3CX+EYa3BZMZyVqovyKb1FRvfeKBlVIzsnP59kWZ/NH6w3ItNBiOEw+oBKu/T/YujX8to8/DrNn11tcfse9NFPi2yT5MbM5rqtD2cwShk5Ib5sENOt7jvM7yufnB3SU904GsHB+FVOmuKQ2Ev9X7XXXueto60vDlZUR0/8kYQIqviHfKyI5JuTnoKV3fqZt28J/Rjdtctf338/nz8zn8KIPAdiwYqdiZhKlPGYG8N8rfuI7/DBNu5gJVPHNcSE7yj/jBrfdN7WK2230w9qqBKZGWroaBFR8NahCJEzMnOUGva1euC38Z9RPfHffnTfK+jO7sjUAhVQoZiZJurtn+e+Vdodvh50w7FAlvplAia/U7ygfcOa2KlubzELopEfTq6RGWn5gBA1u06AKkRCfA6/iu3bx1nqfUXDXl6zYRAuANm0YPRruKCqGcihmp2JmkqStKHPnnXDffWCtG/y4c7NbHmJWB0k9Jb5Sn5f4FuVXke9VfDt0CP/LWZXA1EjLD4yArg4iEoY3zVm3VtvqfEYD4+ap1ZtrEt/S/eCl14rgcGhRWKGYmSRpK8rccQcsWlR32aBBiqMZQomv1Oclvr88p5qSPi5YRPvlrEpg8qXlB0ZAVwcRCcNLfNsVbq3zGQ2Mm63xujq0aQPAyMPcZypvV4U7DZcxqW93E5e2oszq1e76s89qq7x9++p/nCGU+Ep9XuLbu0cVkyfXLlZ3hvRL+Q+MoIqvZu8QCSFgcFvpiCpKB6wHoGAD3FsIm20zWld7h7tbu7695OVBQQFUVrofmPpxmRQpj5nbtrlTFDdrBvvtB8a4uPmC4mamUOIr9XmzOgROZ6buDDkqoI9vugeKiGQs/4xuW7fCsGHw5ZcADAeWBK7XvHndw93FxS7x3blTiW9T4Vd7O3euSXoVNzOLEl+pL2BwWyB1Z8hBmsdXJDq/4jt/vktkjXEdfAHKy2vn9/W6OdQoLnYVwp07oZVOddwk+Imvd5Y/xc3Mo3l8pb4wia/khjrzXmoeX5Ho/IpvZaW7HjYM1qxxl1dfrV0vOPH1q7z+50wSq7ISrrgCTjkF7rknaU9TJ2YGVnwJM++zpJUqvlKfn/hW65TFuSb4sNzCsbvoAprHVyQSv+Lr69ev9u+hQ2v/9hNkX3Gxuw4+jbEkRlkZ3H67+/vZZ+HssxM3pdisWfDGGyxaBC/e6+pEL+bDwNGf0BFqEl/FzcyjxFfqU8U3ZwUfllu1rMIlvprHVyS84mI3NsIvFgQmvq1a1U7xcNBBdbdTxTe5Ak8hbS2sX5+4xPfUU+Hrr+kL3OovqwTe9v7u2bNmVcXNzKLEV+oLMbgNNKI/FwTPe9mtY90TWIhICMa42Ro2bnS3AxLfsjJ4b8xbjP3Vcg44oXfd7VTxTa7gHxQbNtRJSBtl2TIAVv7kQp55qZjqavfVedJJ0H1gC7j44sQ8jyScEl+pL0TFN5EjU5VAZ67gw3Kd/qp5fEVictNN7nB6x45w/PFAYNws4KaiPvXjZowVX8XMBgr+QbFhQ2Ie19qaU1B3ffoOhn9aWPP/6a7/T8ZT4iv1hUh8EzUyNZemdsnWL6s6h+Vu1ZnbRGLyq1+5S4CocTOGim8uxUxIcNxMVuK7davr1lJSAoWF6sqQZZT4Sn0hBrcl6tSPuTK1Syq/rJKaYO9SVweRhooaN/3EN0LFN1diJiQhbkZIfBsVNzfVPQufZBclvlJfiIpvokampu3c6SmWqi+rpCfY/heyujqIxC1q3PQ/VxEqvrkSMyEJcTNUH18SEDf9xLdt20Y0TtJF8/hKfd7gtlUrqmrnJsQFBv8UxoHL4+F/Edx8c9M+ZJequRtDfVEklCq+InGpM6crUeJmDBXfXImZkIS4Gabi2+i46Q9iVMU3K6niK/V5Fd83X63iulfr/iJORIUxF/pDpWruxqRXg1TxFYlZuPgYNm7GUPGF3IiZkIS46b+uxcXu75UrYcMGxuwPdxdChYXqwmaMHt08vsdVxTerKfGV+mr6+FZRZeseckrWIfxsHQgWSbK+rIJfq6Qm2Kr4isQsXHwMGzdjqPiG0xRjJiQ4bvqva9eusHgxPPggPPggI4AfvFWqKwvJK38DODz2x1Uf36ymxFfq8xLfgrxq8qlbSUxGhTHXRi03RrjXKmmvlxJfkZiFi49h42YD5/FVzIyR97quGjKOTUveo5N1pxNu1QoK8oGKCvK2b4fHHoPDlfjmCiW+Up+X+I49vIqbj6hbUUhkhdGvWCxZkjujlhsr5SO81dVBJGbh4mPYuNmAM7eVlcENN7icrrpaMTMiL/GdVTGQcXkPUFXlvt5unuz1u/7yS9hvPzf/8oIFsT/u0qXuWl0dspISX6nPG9zWcdGnTF5xCTyFu3hKvUvw8nisWAFfvAAtq2GfPLjTQrWBfOCEz4FLGrMDmWXFCli+HHr0gG7dGvdYZ6+ANkBVql6rH7wDgqr4isQk3BGYkMtjrfhecAE88wy7KmH3zfBk4H1VUPDsYLjirSbzOU1YNw7vB8VuexRR9F6IivuQIdC/P3z/Pbz3XvyPP2hQIxon6aLEV+rr2NFdz58Pd9+dlKfoBlzo3wg8M3IV8O+kPGXadPMuiXqsOq9bKl6rvDxo1y4FTySSY2IZ3LZiBdx3HwCFQIdQ63zxHnz3HQweHNfTZ2I/4YR24/Be190GFYeuuBsD//sffP111IeaNcsViIcOhb33xvWX2H//BjZM0kmJr9Q3ejT8619uBGySLFwIf5sKVZWQXwAXXwS77ZY5j5cob70F//0vVFvIMzB+PIwdm942xf1a7b03dOqUsvaJ5Ay/4nvrrfD3v4deZ9s2d/2jH/HJJY9xwgm1SeELL8DwW09yGd2yZXElvhnVT/j55+Gyy6CigsHb4M/lJ3KRndr4bhwBszqEHQvRoUPUwSplZTDmyqDX6oAGtknSTomv1JefDyef3KBNY60g7AacdHLturs1MuA+PQXurIYqC/nV0LEzjB4eW1uSWfVoORweeKc2YJ5yDV4/kfQJ9VpNbkJdS0Syxr77uuv1690lkl/8guFHd+DZd2vj1fBS4Ok+7v7ly+N66nBz2aYlZj79dE2/2ZbA+dzLjXk3sqWoY+MGUCdojEIunT0vFyjxlYSJt4KQyNkIgkdNd+gQW1uSXfVI1Xy+8cilM0GJZLKyXiczodkhFFVspbAQHn3UjbWqp2VL6N4dCBE3e/Rw13EmvhkVM/1TCT/9NNxzD3nTp/POvpfRdr9+9Fw2GPhpwx43cB7fRlDMbFqU+ErCpPNXcXCCGWtbUtHmhib4jamqRNo2E5NxkVw0bRos3tWdqmrIr4TXF8B+p8T5IA1MfDMqZvrV7v79XX+w6dPZ58sn4EvgYVw3Dn8/o6gT++JIfBUzc4cSX0mYdP8qDk4wY2lLutscTmOqKrFsmytnghLJZAmJPz17uuvHHnMDCuJQCpQ2bw6HPQqjD05fzPQrvu3awUUXuQfevNn1e1661M15GUPiGxz7fthnJ20halcHxczcosRXEiaTfhXH2pZManOgxlRV1B9NJDskJP4MG+a6QmzdWjv9YLzOO4/SE05g7inuBGd9+kDPWX1g5Dlu5oNEtzmYX/Ft1w5atHAD3QA++8wlvitWxPQwwbFv85oKl/hGqfgqZuaWhCa+xpj2wD+AccBaYLK19skQ6xngj8BEb9HfgauttTaR7ZHUy6RfxbG2JZPa7GtMVSVTq9giUl+j40/37m4Gno0b49+2vNwlzrNmwaxZ9AR6+vc9gpvndvjwepslNGZWVbkzoRlT/0xo/sTnMSa+wbGvXYvYujooZuaWRFd8pwIVQBdgKPBfY8xMa+2soPXOBY4HhgAWeAtYCNyX4PZIhom132omzi8ZTjLa2piqSqZWsUUkfjHFlxYtoEWLhsWi115z8y4GevZZN7ftwoUhE9/GqtPO3Te6hW3a1Jw1tEaciW9w7Gt1YWyJr2JmbklY4muMaQGcCOxjrd0KfGCMeRk4A7g6aPWzgNuttcu8bW8HfokS3yYt1n6rje3fmsrglcxZIRpTVcnEKraIxCee+NLQWFRmRzKtYGTdmLlmjUt8G9p1ItLzBbXzw0c3sB+EPklO167uOo455evEvjimM1PMzB2JrPjuDlRaa+cGLJsJjAqx7t7efYHr7R3qQY0x5+IqxPTu3TsxLZW0SPao4XRMyJ6qvmHZVAGX9FPcbBriiS8NiUVhY6Y3dVoyEt9p06B452bOr/47bcs3w21eNbd9+/or+xXfxx6Dl16K/UlOP52yU+5g2NwFFEKjpzOTpiWRiW9LYHPQsk1AqzDrbgpar6UxxgT387XWPgA8ADBs2DD1Ac5isfajamh/q3QMUPDbunOnO7Nvh5DnE22cjDrDkmQFxc2mIZ5Y2JC4GTZmJjHxHT0aNuT/gz9V/8Z1dPzEu8N/zkDDhrkuEJs2wdq1MT9H1f0P8sg9QyitLAfgs2+KOWBgo5suTUQiE9+tQOugZa2BLTGs2xrYqsFtTVuyZ1pIxwCF0lK44w43A09VlRuMPHhwYhNTjTgWyU3xxMKGxM2wMdNPQufMgY8+iv5A+fluIFyMXQpaHroY3oXX+BGfF4zgjJ/n0/uqU+uv7A/c2xIqjQhjt93I37aNsbwKQAWFvDm7FwecEPtDSNOWyMR3LlBgjBlorZ3nLRsCBA9sw1s2BPg4ynrSxCRzpoV0DVBYtw6sherq5CSmGnEskrviiYXxxs2wMdNPfD/9NPYHPOMM+Oc/Y1o1b+1qAB7ndP5lTydvN5g8IMzKzZq5S6y6dYP58znQ/A8snFL0IlcdbqJvJzkjYYmvtXabMeZ54CZjzETcrA7HAQeFWP2fwOXGmFdxBzt+A9ydqLZI7krHAIV4E9N4++tqxLGIJEvImDloEEyc6Aa4RVNRAV98AR98EPNz9ixcBcC6vM4x/5iPOW527w7z59PLLgXghn/0YohipgRI9HRmFwIPAauBdcAF1tpZxphDgdestS299e4H+gH+p+rv3jKRrBNPYtrQ/roacSwiKZOXBw8+GNu6FRVQUgKLFsGOHdC8edRN2ux0Fd+fXtSZ353aiEF4ofgD4jxDju0Vw05ILklo4mutXY+bnzd4+fu4AW3+bQtc5V1Esl6sian664pIk1JUBAMGuP7Ar77q/u7XD1qFGtfuWeUqvudc0wW6Rn+KuOJm4CC5Vq3qnxRDcp5OWSwSQbjDaw2dXkz9dUWkydlzT5f4nnQSABva92PZn55i8NC6J6T46iv47FPL2WvXYoyBjh1jevi44mb//rV/7757vVMuiyjxFQkj3OG1xkwvpv66ItLkXHQRLFvGto0VbJ//A53WL6DdxAPrrbavdwHY1bYjhQWxpSBxxc2zz3ZdNTZvhmOPjW8/JCco8RUJI9zhtcZ2V4i3v65OXiEiGe3II+HII7lrCjx57bdMrT6fFmyje3fo5nVlWLGy7rTAGw6awJFxPEXMcbNFC8qGXuBi5mZQyJRgSnxFwgh3eC2V3RV08goRyRajR8PNxXtxRMV0F6/+Dd28eLUoOJZdm5w2KGZKNEp8RcLwT07x3HNw4om1wTOV3RU0GE5EsoUfG0NN55uquKmYKdEo8RUJo6zMnYmtogLef7/uGdlSNb2YBsOJSLZ59FEXsx59tG7FNRVxUzFToslLdwNEUqmsDKZMcdfRhKocpJpfJbn5Zh2yE5HUiydmQvrjpmKmRKOKr+SMePt+xVI50MAzEWmqGtJfNhPipv+YftKt2CyBlPhKzoi371e0PmmpGEShgRoiki4N6S+ruCmZTomv5IyG9P2K1CctFYMoNFBDRNKlof1lFTclkynxlZyR6FHFqRhEoYEaIpIuyZiJQXFT0s1Ya9PdhpgNGzbMfvrpp+luhkiNVPTxVT9iMcZ8Zq0d1pBtFTcl0yhuSiqEi5tKfEVEMpwSXxGR+ISLm5rOTERERERyghJfEREREckJSnxFREREJCdkVR9fY8waYHEam9ARWJvG50837b/2P1f3P9373sda26khGypuplUu7zto/7X/GRg3syrxTTdjzKcNHWDSFGj/tf+5uv+5vO+NlcuvXS7vO2j/tf+Zuf/q6iAiIiIiOUGJr4iIiIjkBCW+8Xkg3Q1IM+1/bsvl/c/lfW+sXH7tcnnfQfuv/c9A6uMrIiIiIjlBFV8RERERyQlKfEVEREQkJyjxFREREZGcoMQ3AmNMe2PMC8aYbcaYxcaY02LYpsgYM9sYsywVbUymePbfGHOlMeYbY8wWY8xCY8yVqWxrIsS6v8a51RizzrvcaowxqW5vosWx/1n/vw4l3s97U/qsJ4piZm7FTMjtuKmYmZ0xsyCdT54FpgIVQBdgKPBfY8xMa+2sCNtcCawBWiW/eUkXz/4b4EzgK6A/8KYxZqm19ulUNTYBYt3fc4HjgSGABd4CFgL3paylyRHr/jeF/3Uo8X7em9JnPVEUM3MrZkJux03FzGyMmdZaXUJcgBa4f+juAcseA/4YYZvdgNnAj4Bl6d6HVO9/0PZ3AXenez+Ssb/ADODcgNvnAB+lex/S9f/Otv91Iva/KX3W0/UaNrXXMddiZrz73NTipmJm9sZMdXUIb3eg0lo7N2DZTGDvCNvcDVwD7Ehmw1KkIfsPuENawKFApCpPpolnf/f27ou2XjZp0P87S//XocS7/03ps54oipm5FTMht+OmYmaWxkwlvuG1BDYHLdtEmBK9MeYEIN9a+0KyG5Yice1/kBtw762HE9ymZIpnf1t69wWu1zLL+6s19P99A9n3vw4l5v1vgp/1RFHMzK2YCbkdNxUzszRm5mzia4yZZoyxYS4fAFuB1kGbtQa2hHisFsCfgEuS3/LESOT+Bz3uxbi+TOOttTuT0/qkiGd/g9dtDWy13vGcLBX3/zuL/9ehxLT/2fhZTxTFTMXMEHI5bipmZmnMzNnBbdba0ZHu9/5ZBcaYgdbaed7iIYQ+PDEQ6Au87/14LQLaGGNWAiOttYsS1OyESfD++9v8ArgaOMxam20jtOcS+/7O8u77OMp62SSe/c/2/3Uose5/1n3WE0UxUzEzhFyOm4qZ2Roz091BOpMvwNPAU7hO3Afjyvh7h1ivAOgacPkJ8IP3d3669yPZ+++tezqwEtgz3e1Owf/7fFwn/R5Ad9wH/fx0tz+F+5/1/+uG7n9T/ayn4T3UJF/HXIuZcf7Pm1zcVMzMzpiZ9hcuky9Ae+BFYBuwBDgt4L5DcYdpQm03miwfoRzv/uOmpdmFO/zhX+5L9z4kYn9D7KvBHbpZ713+BJh0tz+F+5/1/+vG7H/QNk3is57s1zBXXsdci5mR9jkX4qZiZnbGTOM1RERERESkScvZwW0iIiIikluU+IqIiIhITlDiKyIiIiI5QYmviIiIiOQEJb4iIiIikhOU+IqIiIhITlDiKyIiIiI5QYmviIiIiOQEJb4iAYwxA4wxu4wxNwUtv9cYs8UYMyxdbRMRyTSKmZJtlPiKBLDWzgf+DlxmjOkAYIy5HvgFcIK19tN0tk9EJJMoZkq20SmLRYIYY7oB84F7gDnA/cCp1tpn0towEZEMpJgp2UQVX5Eg1toVwB3Ar4D7gEsCA7gx5jpjzFxjTLUx5vj0tFJEJDMoZko2UeIrEto8oBgos9ZODbrvLeBoYHrKWyUikpkUMyUrKPEVCWKMGYM7VFcGHGyM2TfwfmvtR9baBWlpnIhIhlHMlGyixFckgDFmf+AF3GCN0cASYEo62yQikqkUMyXbKPEV8RhjBgCvAW8Cv7LWVgA3AscYYw5La+NERDKMYqZkIyW+IoAxpisueM8GTrfWVnt3/RP4DvhjutomIpJpFDMlWxWkuwEimcBauxLoF2J5FbBn6lskIpK5FDMlW2keX5E4GWNuACYCnYAtQDkw0lq7LJ3tEhHJRIqZkkmU+IqIiIhITlAfXxERERHJCUp8RURERCQnKPEVERERkZygxFdEREREcoISXxERERHJCUp8RURERCQnKPEVERERkZygxFdEREREcsL/B4t45pEwFXVGAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 702,
       "height": 280
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "#### Gradient Boosting with Early stopping",
   "metadata": {
    "cell_id": "e81fdf79cfa54534ae9e4a5baebc5737",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 54
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "08a56a7100e84a15a1c4f202b1c395ae",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f4728930",
    "execution_start": 1658458394965,
    "execution_millis": 84,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 282
   },
   "source": "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\nX_train, X_val, y_train, y_val = train_test_split(X, y, random_state=42)\ngbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120, random_state=42)\ngbrt.fit(X_train, y_train)\n\nerrors = [mean_squared_error(y_val, y_pred) for y_pred in gbrt.staged_predict(X_val)]\nbst_n_estimators = np.argmin(errors) + 1\n\ngbrt_best = GradientBoostingRegressor(max_depth=2, n_estimators=bst_n_estimators)\ngbrt_best.fit(X_train, y_train)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 44,
     "data": {
      "text/plain": "GradientBoostingRegressor(max_depth=2, n_estimators=118)",
      "text/html": "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(max_depth=2, n_estimators=118)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(max_depth=2, n_estimators=118)</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Tuning the number of trees using early stopping",
   "metadata": {
    "cell_id": "d0cb2aaa90704188bb1e077509e4c257",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "5f25447084c54b52be728a1474ff915c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "863e2133",
    "execution_start": 1658458395049,
    "execution_millis": 4,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "min_error = np.min(errors)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "f0b3768bc3594348aa6c21b9d64e96cc",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "174782df",
    "execution_start": 1658458395056,
    "execution_millis": 1091,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 421
   },
   "source": "plt.figure(figsize=(10, 4))\n\nplt.subplot(121)\nplt.plot(np.arange(1, len(errors) + 1), errors, \"b.-\")\nplt.plot([bst_n_estimators, bst_n_estimators], [0, min_error], \"k--\")\nplt.plot([0, 120], [min_error, min_error], \"k--\")\nplt.plot(bst_n_estimators, min_error, \"ko\")\nplt.text(bst_n_estimators, min_error*1.2, \"Minimum\", ha=\"center\", fontsize=14)\nplt.axis([0, 120, 0, 0.01])\nplt.xlabel(\"Number of trees\")\nplt.ylabel(\"Error\", fontsize=16)\nplt.title(\"Validation error\", fontsize=14)\n\nplt.subplot(122)\nplot_predictions([gbrt_best], X, y, axes=[-0.5, 0.5, -0.1, 0.8])\nplt.title(\"Best model (%d trees)\" % bst_n_estimators, fontsize=14)\nplt.ylabel(\"$y$\", fontsize=16, rotation=0)\nplt.xlabel(\"$x_1$\", fontsize=16)\n\nsave_fig(\"early_stopping_gbrt_plot\")\nplt.show()",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Saving figure early_stopping_gbrt_plot\n",
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 720x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 712,
       "height": 280
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Early stopping with some patience (interrupts training only after there's no improvement for 5 epochs)",
   "metadata": {
    "cell_id": "02b947e80a864db8add461415eb1acff",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 74.78125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "1c8dbc64e94142ecb2d3e3013feb6ce4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "77263b69",
    "execution_start": 1658458396038,
    "execution_millis": 104,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 331
   },
   "source": "gbrt = GradientBoostingRegressor(max_depth=2, warm_start=True, random_state=42)\n\nmin_val_error = float(\"inf\")\nerror_going_up = 0\nfor n_estimators in range(1, 120):\n    gbrt.n_estimators = n_estimators\n    gbrt.fit(X_train, y_train)\n    y_pred = gbrt.predict(X_val)\n    val_error = mean_squared_error(y_val, y_pred)\n    if val_error < min_val_error:\n        min_val_error = val_error\n        error_going_up = 0\n    else:\n        error_going_up += 1\n        if error_going_up == 5:\n            break  # early stopping",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "097c9da544474626819d3d9c9caf59dd",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b7922aaf",
    "execution_start": 1658458396144,
    "execution_millis": 7,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "print(gbrt.n_estimators)",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "105\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "122c7c4332034a0b95c8d0a194e66632",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fc5c15b2",
    "execution_start": 1658458396155,
    "execution_millis": 41,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "print(\"Minimum validation MSE:\", min_val_error)",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Minimum validation MSE: 0.003023701609089896\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "#### Using XGBoost",
   "metadata": {
    "cell_id": "15f7c3f0856947ee80e305aab54f36b0",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 54
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "3ca1bda8c3d24e11abbe8788427b81c6",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e2130961",
    "execution_start": 1658458396195,
    "execution_millis": 652,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 133
   },
   "source": "try:\n    import xgboost\nexcept ImportError as ex:\n    print(\"Error: the xgboost library is not installed.\")\n    xgboost = None",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "c02f2d7c1f034e76a38c9134051dc310",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5a6a1f26",
    "execution_start": 1658458396852,
    "execution_millis": 332,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 151
   },
   "source": "if xgboost is not None:  # not shown in the book\n    xgb_reg = xgboost.XGBRegressor(random_state=42)\n    xgb_reg.fit(X_train, y_train)\n    y_pred = xgb_reg.predict(X_val)\n    val_error = mean_squared_error(y_val, y_pred) # Not shown\n    print(\"Validation MSE:\", val_error)   ",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Validation MSE: 0.0030701301701716146\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "9ec314ec357c43f3909ad1761de11ef9",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "942201fa",
    "execution_start": 1658458397185,
    "execution_millis": 188,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 151
   },
   "source": "if xgboost is not None:  # not shown in the book\n    xgb_reg.fit(X_train, y_train,\n                eval_set=[(X_val, y_val)], early_stopping_rounds=2)\n    y_pred = xgb_reg.predict(X_val)\n    val_error = mean_squared_error(y_val, y_pred)  # Not shown\n    print(\"Validation MSE:\", val_error)   ",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "[0]\tvalidation_0-rmse:0.19678\n[1]\tvalidation_0-rmse:0.14325\n[2]\tvalidation_0-rmse:0.10835\n[3]\tvalidation_0-rmse:0.08482\n[4]\tvalidation_0-rmse:0.07044\n[5]\tvalidation_0-rmse:0.06255\n[6]\tvalidation_0-rmse:0.05927\n[7]\tvalidation_0-rmse:0.05698\n[8]\tvalidation_0-rmse:0.05519\n[9]\tvalidation_0-rmse:0.05513\n[10]\tvalidation_0-rmse:0.05474\n[11]\tvalidation_0-rmse:0.05463\n[12]\tvalidation_0-rmse:0.05427\n[13]\tvalidation_0-rmse:0.05376\n[14]\tvalidation_0-rmse:0.05377\n[15]\tvalidation_0-rmse:0.05363\n[16]\tvalidation_0-rmse:0.05358\n[17]\tvalidation_0-rmse:0.05387\nValidation MSE: 0.0028706534131390338\n/root/venv/lib/python3.9/site-packages/xgboost/sklearn.py:793: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n  warnings.warn(\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "6648513bc18d4dfdb43b359453b2b60f",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "816c9955",
    "execution_start": 1658458397372,
    "execution_millis": 2184,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 84
   },
   "source": "%timeit xgboost.XGBRegressor().fit(X_train, y_train) if xgboost is not None else None\n",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "273 ms ± 6.92 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "ceb8f048216445ffb4b9cf6a228eeca9",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e50208dc",
    "execution_start": 1658458399554,
    "execution_millis": 2448,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "%timeit GradientBoostingRegressor().fit(X_train, y_train)",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "22.4 ms ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Voting Classifier\n\nLoad the MNIST data and split it into a training set, a validation set, and a test set (e.g., use 50,000 instances for training, 10,000 for validation, and 10,000 for testing).",
   "metadata": {
    "cell_id": "06177652113045db9b2a423e9ad195bc",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 130.78125
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Load the MNIST dataset",
   "metadata": {
    "cell_id": "38e0f795153540e387dafc50ae9eb393",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 62
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "e7756dc7b5c64cec8786e7fd97123faa",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e0e6612d",
    "execution_start": 1658458401371,
    "execution_millis": 20128,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 115
   },
   "source": "from sklearn.datasets import fetch_openml\n\nmnist = fetch_openml('mnist_784', version=1, as_frame=False)\nmnist.target = mnist.target.astype(np.uint8)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### Train, test split",
   "metadata": {
    "cell_id": "1d2f8abc25d542a0a6b21f5f41ecd11b",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 62
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "a4c46b79162a43b2bff9ceb0389288a3",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "87c46b95",
    "execution_start": 1658458421502,
    "execution_millis": 774,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 151
   },
   "source": "from sklearn.model_selection import train_test_split\n\nX_train_val, X_test, y_train_val, y_test = train_test_split(\n    mnist.data, mnist.target, test_size=10000, random_state=42)\nX_train, X_val, y_train, y_val = train_test_split(\n    X_train_val, y_train_val, test_size=10000, random_state=42)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### Training\n\nThen train various classifiers, such as a Random Forest classifier, an Extra-Trees classifier, and an SVM",
   "metadata": {
    "cell_id": "f788e28de926489591881a416df3cec8",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 122.78125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "ec42139fb6d64e7886fdfcbb49b092e4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fec2a5ca",
    "execution_start": 1658458422280,
    "execution_millis": 22,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 97
   },
   "source": "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.neural_network import MLPClassifier",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "ff13f9974e704bf7bc6c03a09852e57c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "591bee98",
    "execution_start": 1658458422311,
    "execution_millis": 3,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 115
   },
   "source": "random_forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\nextra_trees_clf = ExtraTreesClassifier(n_estimators=100, random_state=42)\nsvm_clf = LinearSVC(max_iter=100, tol=20, random_state=42)\nmlp_clf = MLPClassifier(random_state=42)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "5903272f86ef4ed78646d2d0da79b604",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4e867b86",
    "execution_start": 1658458422322,
    "execution_millis": 200892,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 115
   },
   "source": "estimators = [random_forest_clf, extra_trees_clf, svm_clf, mlp_clf]\nfor estimator in estimators:\n    print(\"Training the\", estimator)\n    estimator.fit(X_train, y_train)",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "Training the RandomForestClassifier(random_state=42)\nTraining the ExtraTreesClassifier(random_state=42)\nTraining the LinearSVC(max_iter=100, random_state=42, tol=20)\nTraining the MLPClassifier(random_state=42)\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "e5c2fd9326d24de891bda9fbe183fb8c",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f9133767",
    "execution_start": 1658458623215,
    "execution_millis": 1269,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "[estimator.score(X_val, y_val) for estimator in estimators]",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 60,
     "data": {
      "text/plain": "[0.9692, 0.9715, 0.859, 0.9604]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "The linear SVM is far outperformed by the other classifiers. However, let's keep it for now since it may improve the voting classifier's performance.\n\nNext, try to combine them into an ensemble that outperforms them all on the validation set, using a soft or hard voting classifier.",
   "metadata": {
    "cell_id": "82df6cc50bed425f83a2abafb20e2f90",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 133.5625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "237bf2e652c44ad2abd38b376c02c6bd",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cfa058ca",
    "execution_start": 1658458624057,
    "execution_millis": 4,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "from sklearn.ensemble import VotingClassifier",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "87e7d8c587af4f4d960a42d3e1df347a",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f3547f7c",
    "execution_start": 1658458624073,
    "execution_millis": 59245393,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 151
   },
   "source": "named_estimators = [\n    (\"random_forest_clf\", random_forest_clf),\n    (\"extra_trees_clf\", extra_trees_clf),\n    (\"svm_clf\", svm_clf),\n    (\"mlp_clf\", mlp_clf),\n]",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "2344bc4310384598a128ec90785aad46",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "20f40cca",
    "execution_start": 1658458624079,
    "execution_millis": 59245346,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf = VotingClassifier(named_estimators)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "c9ddd146993f48459b140041ccd52561",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b3e67499",
    "execution_start": 1658458624085,
    "execution_millis": 201607,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.fit(X_train, y_train)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 64,
     "data": {
      "text/plain": "VotingClassifier(estimators=[('random_forest_clf',\n                              RandomForestClassifier(random_state=42)),\n                             ('extra_trees_clf',\n                              ExtraTreesClassifier(random_state=42)),\n                             ('svm_clf',\n                              LinearSVC(max_iter=100, random_state=42, tol=20)),\n                             ('mlp_clf', MLPClassifier(random_state=42))])",
      "text/html": "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n                              RandomForestClassifier(random_state=42)),\n                             (&#x27;extra_trees_clf&#x27;,\n                              ExtraTreesClassifier(random_state=42)),\n                             (&#x27;svm_clf&#x27;,\n                              LinearSVC(max_iter=100, random_state=42, tol=20)),\n                             (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n                              RandomForestClassifier(random_state=42)),\n                             (&#x27;extra_trees_clf&#x27;,\n                              ExtraTreesClassifier(random_state=42)),\n                             (&#x27;svm_clf&#x27;,\n                              LinearSVC(max_iter=100, random_state=42, tol=20)),\n                             (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>random_forest_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>extra_trees_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svm_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(max_iter=100, random_state=42, tol=20)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlp_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "53cfeaa7e613421c8ac9b7f2986725a7",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3922694d",
    "execution_start": 1658458825691,
    "execution_millis": 1931,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.score(X_val, y_val)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 65,
     "data": {
      "text/plain": "0.9698"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "24a178d367ba4020af1fcc67a1e47f44",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e6741c7d",
    "execution_start": 1658458826966,
    "execution_millis": 1309,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "[estimator.score(X_val, y_val) for estimator in voting_clf.estimators_]",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 66,
     "data": {
      "text/plain": "[0.9692, 0.9715, 0.859, 0.9604]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Let's remove the SVM to see if performance improves. It is possible to remove an estimator by setting it to `None` using `set_params()` like this",
   "metadata": {
    "cell_id": "73deda1731a24e4f9cf7aa57ecf41960",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 74.78125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "2fb6408180a647cf8aa59b1e2d449d23",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "71f1671a",
    "execution_start": 1658458828280,
    "execution_millis": 28,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.set_params(svm_clf=None)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 67,
     "data": {
      "text/plain": "VotingClassifier(estimators=[('random_forest_clf',\n                              RandomForestClassifier(random_state=42)),\n                             ('extra_trees_clf',\n                              ExtraTreesClassifier(random_state=42)),\n                             ('svm_clf', None),\n                             ('mlp_clf', MLPClassifier(random_state=42))])",
      "text/html": "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n                              RandomForestClassifier(random_state=42)),\n                             (&#x27;extra_trees_clf&#x27;,\n                              ExtraTreesClassifier(random_state=42)),\n                             (&#x27;svm_clf&#x27;, None),\n                             (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;random_forest_clf&#x27;,\n                              RandomForestClassifier(random_state=42)),\n                             (&#x27;extra_trees_clf&#x27;,\n                              ExtraTreesClassifier(random_state=42)),\n                             (&#x27;svm_clf&#x27;, None),\n                             (&#x27;mlp_clf&#x27;, MLPClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>random_forest_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>extra_trees_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>svm_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">None</label><div class=\"sk-toggleable__content\"><pre>None</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>mlp_clf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "This updated the list of estimators",
   "metadata": {
    "cell_id": "3581e34dae354840a97f88162850833b",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "da9a463b3c24495d8be73c823dcb8bc5",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "724db99f",
    "execution_start": 1658458828311,
    "execution_millis": 14,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.estimators",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 68,
     "data": {
      "text/plain": "[('random_forest_clf', RandomForestClassifier(random_state=42)),\n ('extra_trees_clf', ExtraTreesClassifier(random_state=42)),\n ('svm_clf', None),\n ('mlp_clf', MLPClassifier(random_state=42))]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "However, it did not update the list of trained estimators",
   "metadata": {
    "cell_id": "070dadb3074a4b8497b90bc5a921ae4b",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "66627e118c654ca9b7d17b5f0fcfb455",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fa387a08",
    "execution_start": 1658458828330,
    "execution_millis": 12,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.estimators_",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 69,
     "data": {
      "text/plain": "[RandomForestClassifier(random_state=42),\n ExtraTreesClassifier(random_state=42),\n LinearSVC(max_iter=100, random_state=42, tol=20),\n MLPClassifier(random_state=42)]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "So we can either fit the `VotingClassifier` again, or just remove the SVM from the list of trained estimators:",
   "metadata": {
    "cell_id": "2fb18f9280e744c7bfad80d45d79cfb0",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 74.78125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "13948e819ce34004a267585fe1b33060",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "4327761a",
    "execution_start": 1658458828351,
    "execution_millis": 5,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "del voting_clf.estimators_[2]",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Now let's evaluate the VotingClassifier again",
   "metadata": {
    "cell_id": "4d0c01b0cc30476ba14ec7de20fdf406",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "0ce3a5ca0ae34c118c6f53a08beb3d08",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3922694d",
    "execution_start": 1658458828369,
    "execution_millis": 2086,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.score(X_val, y_val)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 71,
     "data": {
      "text/plain": "0.973"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "A bit better! The SVM was hurting performance. Now let's try using a soft voting classifier. We do not actually need to retrain the classifier, we can just set `voting` to `\"soft\"`",
   "metadata": {
    "cell_id": "2ee1024f8bec47ab91907e2fafd21fc6",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 74.78125
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "b6c80c29b46f4746ba3459a5eed2c42f",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f90e591",
    "execution_start": 1658458829757,
    "execution_millis": 8,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.voting = \"soft\"",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "86d52481c43d44fc972862bc13f777ff",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3922694d",
    "execution_start": 1658458829773,
    "execution_millis": 1186,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "voting_clf.score(X_val, y_val)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 73,
     "data": {
      "text/plain": "0.9676"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "Nope, hard voting wins in this case.\n\n> Once you have found one, try it on the test set. How much better does it perform compared to the individual classifiers?",
   "metadata": {
    "cell_id": "c6fde61a31ac4a488e5e93e9915aaa2d",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 125.171875
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "cc7cd917d0074846aabedc904c8a0a45",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "10182b76",
    "execution_start": 1658458830965,
    "execution_millis": 1423,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 79
   },
   "source": "voting_clf.voting = \"hard\"\nvoting_clf.score(X_test, y_test)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 74,
     "data": {
      "text/plain": "0.971"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "c67cbf6ef040416e99f3a32c3cd86d19",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5e75384e",
    "execution_start": 1658458832391,
    "execution_millis": 1115,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "[estimator.score(X_test, y_test) for estimator in voting_clf.estimators_]",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 75,
     "data": {
      "text/plain": "[0.9645, 0.9691, 0.9639]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "The voting classifier only very slightly reduced the error rate of the best model in this case.",
   "metadata": {
    "cell_id": "f13f0f95e9a74d80b11ae84724422367",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 52.390625
   }
  },
  {
   "cell_type": "markdown",
   "source": "## Stacking Ensemble\n\nRun the individual classifiers from the previous exercise to make predictions on the validation set, and create a new training set with the resulting predictions: each training instance is a vector containing the set of predictions from all your classifiers for an image, and the target is the image's class. Train a classifier on this new training set.",
   "metadata": {
    "cell_id": "0617ec27f6f347c69adacebc61fa47e8",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 175.5625
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "c1e8fb60312647c189f700a4ce611839",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ba2f66e",
    "execution_start": 1658458833481,
    "execution_millis": 944,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 115
   },
   "source": "X_val_predictions = np.empty((len(X_val), len(estimators)), dtype=np.float32)\n\nfor index, estimator in enumerate(estimators):\n    X_val_predictions[:, index] = estimator.predict(X_val)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "ddcd6e56501f4c48aab0154ff6d0e3b4",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e1911593",
    "execution_start": 1658458834434,
    "execution_millis": 18,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "X_val_predictions",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 77,
     "data": {
      "text/plain": "array([[5., 5., 5., 5.],\n       [8., 8., 8., 8.],\n       [2., 2., 3., 2.],\n       ...,\n       [7., 7., 7., 7.],\n       [6., 6., 6., 6.],\n       [7., 7., 7., 7.]], dtype=float32)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "66046d90d92b4f2a84cf358b14257aaf",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9d5d963a",
    "execution_start": 1658458834459,
    "execution_millis": 1693,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 79
   },
   "source": "rnd_forest_blender = RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)\nrnd_forest_blender.fit(X_val_predictions, y_val)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 78,
     "data": {
      "text/plain": "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)",
      "text/html": "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" checked><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200, oob_score=True, random_state=42)</pre></div></div></div></div></div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "6b6fb22acb3f4761a990a9addace4c19",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "2281daee",
    "execution_start": 1658458835602,
    "execution_millis": 550,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "rnd_forest_blender.oob_score_",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 79,
     "data": {
      "text/plain": "0.9685"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "You could fine-tune this blender or try other types of blenders (e.g., an `MLPClassifier`), then select the best one using cross-validation, as always.\n\n> Congratulations, you have just trained a blender, and together with the classifiers they form a stacking ensemble! Now let's evaluate the ensemble on the test set. For each image in the test set, make predictions with all your classifiers, then feed the predictions to the blender to get the ensemble's predictions. How does it compare to the voting classifier you trained earlier?",
   "metadata": {
    "cell_id": "b31b232308d04534aceecb9615e17574",
    "tags": [],
    "deepnote_cell_type": "markdown",
    "deepnote_cell_height": 192.34375
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "dd8effde499f4ad7ad181c82fb029b2f",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5b968bf7",
    "execution_start": 1658458835614,
    "execution_millis": 828,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 115
   },
   "source": "X_test_predictions = np.empty((len(X_test), len(estimators)), dtype=np.float32)\n\nfor index, estimator in enumerate(estimators):\n    X_test_predictions[:, index] = estimator.predict(X_test)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "47d5651964544288bb9bb1df62472eac",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "45d34399",
    "execution_start": 1658458836451,
    "execution_millis": 305,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "y_pred = rnd_forest_blender.predict(X_test_predictions)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "1093397b0f5a4c1ba2184621611ed24a",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "dcc0610b",
    "execution_start": 1658458836760,
    "execution_millis": 4,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "from sklearn.metrics import accuracy_score",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "6f75d1c76fed488eb6461e3b4fdaa5b3",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "92661170",
    "execution_start": 1658458836769,
    "execution_millis": 5,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "accuracy_score(y_test, y_pred)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 83,
     "data": {
      "text/plain": "0.9672"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "This stacking ensemble does not perform as well as the voting classifier we trained earlier, it's not quite as good as the best individual classifier.",
   "metadata": {
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   }
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  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "cf7bc3ba7a7546088e1ffd6834ba50c5",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ba62bfdc",
    "execution_start": 1658458918515,
    "execution_millis": 2,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "AdaBoostClassifier",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 84,
     "data": {
      "text/plain": "sklearn.ensemble._weight_boosting.AdaBoostClassifier"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "62ba92c3f2e84f608d8c9ac34d6e93a1",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d94bada",
    "execution_start": 1658458939257,
    "execution_millis": 20,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "DecisionTreeClassifier",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 85,
     "data": {
      "text/plain": "sklearn.tree._classes.DecisionTreeClassifier"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "cell_id": "6da92a69e18c4a1b9ad508dc4777f457",
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "28ab515",
    "execution_start": 1658458950234,
    "execution_millis": 17,
    "deepnote_cell_type": "code",
    "deepnote_cell_height": 61
   },
   "source": "IMAGES_PATH",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 86,
     "data": {
      "text/plain": "'./images/ensembles'"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Excercies\n\n1. If you have trained five different models on the exact same training data, and they all achieve 95% precision, is there any chance that you can combine these models to get better results? If so, how? If not, why?\n\n> If you have trained five different models and they all achieve 95% precision, you can try combining them into a voting ensemble, which will often give you even better results. It works better if the models are very different (e.g., an SVM classifier, a Decision Tree classifier, a Logistic Regression classifier, and so on). It is even better if they are trained on different training instances (that’s the whole point of bagging and pasting ensembles), but if not this will still be effective as long as the models are very different.\n\n\n2. What is the difference between hard and soft voting classifiers?\n\n> A hard voting classifier just counts the votes of each classifier in the ensemble and picks the class that gets the most votes. A soft voting classifier computes the average estimated class probability for each class and picks the class with the highest probability. This gives high-confidence votes more weight and often performs better, but it works only if every classifier is able to  estimate class probabilities (e.g., for the SVM classifiers in Scikit-Learn you must set `probability=True`).\n\n3. Is it possible to speed up training of a bagging ensemble by distributing it across multiple servers? What about pasting ensembles, boosting ensembles, Random Forests, or stacking ensembles?\n\n> It is quite possible to speed up training of a bagging ensemble by distributing it across multiple servers, since each predictor in the ensemble is independent of the others. The same goes for pasting ensembles and Random Forests, for the same reason. However, each predictor in a boosting ensemble is built based on the previous predictor, so training is necessarily sequential, and you will not gain anything by distributing training across multiple servers. Regarding stacking ensembles, all the predictors in a given layer are independent of each other, so they can be trained in parallel on multiple servers. However, the predictors in one layer can only be trained after the  predictors in the previous layer have all been trained\n\n4. What is the benefit of out-of-bag evaluation?\n\n> With out-of-bag evaluation, each predictor in a bagging ensemble is evaluated using instances that it was not  trained on (they were held out). This makes it possible to have a fairly unbiased evaluation of the ensemble without the need for an additional validation set. Thus, you have more instances available for training, and your ensemble can perform slightly better.\n\n5. What makes Extra-Trees more random than regular Random Forests? How can this extra randomness help? Are Extra-Trees slower or faster than regular Random Forests?\n\n> When you are growing a tree in a Random Forest, only a random subset of the features is considered for splitting at each node. This is true as well for Extra-Trees, but they go one step further: rather than searching for the best possible thresholds, like regular Decision Trees do, they use random thresholds for each feature. This extra randomness acts like a form of regularization: if a Random Forest overfits the training data, Extra-Trees might perform better. Moreover, since Extra-Trees don’t search  for the best possible thresholds, they are much faster to train than Random Forests. However, they are neither faster nor slower than Random Forests when making predictions.\n\n6. If your AdaBoost ensemble underfits the training data, which hyperparameters should you tweak and how?\n\n> If your AdaBoost ensemble underfits the training data, you can try increasing the number of estimators or reducing the regularization hyperparameters of the base estimator. You may also try slightly increasing the learning rate.\n\n7. If your Gradient Boosting ensemble overfits the training set, should you increase or decrease the learning rate?\n\n> If your Gradient Boosting ensemble overfits the training set, you should try decreasing the learning rate. You could also use early stopping to find the right number of predictors (you probably have too many)\n\n8. Load the MNIST data (introduced in Chapter 3), and split it into a training set, a validation set, and a test set (e.g., use 50,000 instances for training, 10,000 for validation, and 10,000 for testing). Then train various classifiers, such as a Random Forest classifier, an Extra-Trees classifier, and an SVM classifier. Next, try to combine them into an ensemble that outperforms each individual classifier on the validation set, using soft or hard voting. Once you have found one, try it on the test set. How much better does it perform compared to the individual classifiers?\n\n9. Run the individual classifiers from the previous exercise to make predictions on the validation set, and create a new training set with the resulting predictions: each training instance is a vector containing the set of predictions from all your classifiers for an image, and the target is the image’s class. Train a classifier on this new training set. Congratulations, you have just trained a blender, and together with the classifiers it forms a stacking ensemble! Now evaluate the ensemble on the test set. For each image in the test set, make predictions with all your classifiers, then feed the predictions to the blender to get the ensemble’s predictions. How does it compare to the voting classifier you trained earlier?\n",
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