05_Оценка и улучшение качества модели.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import mglearn\n",
    "from IPython.display import display\n",
    "plt.rc('font', family='Verdana')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оценка и улучшение качества модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Правильность на тестовом наборе: 0.88\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# создаем синтетический набор данных\n",
    "X, y = make_blobs(random_state=0)\n",
    "# разобъем данные на обучающий и тестовый наборы\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "# создаем экземпляр модели и подгоняем его на обучающем наборе\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "# оцениваем качество модели на тестовом наборе\n",
    "print(\"Правильность на тестовом наборе: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Перекрестная проверка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
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drz7fjBzrd766fE0u9Tjf2Xwvmjlz5kNbtmzZo6r1DQwMbBgZGZm0QYmIIwAknQbMj4iz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\nLCNu0szMzMzMzDLy/wElvP0hToMs9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b68f595ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Перекрестная проверка в scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки: [ 0.96078431  0.92156863  0.95833333]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "scores = cross_val_score(logreg, iris.data, iris.target)\n",
    "print(\"Значения правильности перекрестной проверки: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки: [ 1.          0.96666667  0.93333333  0.9         1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=5)\n",
    "print(\"Значения правильности перекрестной проверки: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя правильность перекрестной проверки: 0.96\n"
     ]
    }
   ],
   "source": [
    "print(\"Средняя правильность перекрестной проверки: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Преимущества перекрестной проверки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Стратифицированная k-блочная перекрестная проверка и другие стратегии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Метки ирисов:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(\"Метки ирисов:\\n{}\".format(iris.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rwJTBtrevvH3AcN55512AO++888XzlchjQeG8885Xljertf5GwrcB/k/SbyV9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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b68dc08d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_stratified_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More control over cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки:\n",
      "[ 1.          0.93333333  0.43333333  0.96666667  0.43333333]\n"
     ]
    }
   ],
   "source": [
    "print(\"Значения правильности перекрестной проверки:\\n{}\".format(\n",
    "      cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки:\n",
      "[ 1.          0.93333333  0.43333333  0.96666667  0.43333333]\n"
     ]
    }
   ],
   "source": [
    "kkfold = KFold(n_splits=3)\n",
    "print(\"Значения правильности перекрестной проверки:\\n{}\".format(\n",
    "    cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки:\n",
      "[ 0.9   0.96  0.96]\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3, shuffle=True, random_state=0)\n",
    "print(\"Значения правильности перекрестной проверки:\\n{}\".format(\n",
    "    cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Перекрестная проверка с исключением по одному"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество итераций cv:  150\n",
      "Средняя правильность: 0.95\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
    "print(\"Количество итераций cv: \", len(scores))\n",
    "print(\"Средняя правильность: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Перекрестная проверка со случайными перестановками при разбиении"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I16tv3771rZn/ueeeu65Lly5fnm515513rrjvvvt6DBgwYPj999/f\n484771wOcPbZZ6+96667evbv33/4G2+8sVWSsXTp0o4jR44cusceewx79tlnd7zyyisrhwwZUjt5\n8uTlJ5xwwsA99thj2Pjx4/dcs2ZNYbN5fzpu3LiNo0aN2qusrGzY+eefv7uZMWHChDWdO3duGDhw\n4PBBgwYNf+WVV7r06NFjy9FHH71+wIABI04++eT+hx122BcXXHDBqoMOOmjwHnvsMezEE08cWF1d\nXfCjH/3o04KCAvr37z/8u9/9bkrnUyrR0I2kBeGpWY1/FwPlZhb3HLhwNOQQYP/wZw9gFMH1IZeY\n2bfCcucD/czsyibXnIwBJpvZOEkTgU1mNjmJ+d0FvGRm9yQoazNmzIjEF1k6ykvCzNS8bGM7ox5/\nsuXHjx/fYjtz8G4qedHW1rYzyv0zXvlU12dU4m9N+VhtTbbfRiH+ZMvHW6dnnHFG5Ha8Uy0/ceLE\nuO2MevytKR+rrbm2zYWW+26+mDNnzrKRI0euzXYc+W7OnDk9Ro4cOSDWZy2OnEj6H0lzgT0kvRe+\n5gLLgacTzdTM6s1supldZWb7A58BsR76Ukx4J7C2MLN64EVgTDLlo/7Fl47ykF8jRC63Ra2/+YhJ\nekUt/ra0N4o73ukuD/k1QuScaz/xTuuaBhwDPB7+PAb4FjDYzC6IV6mk8ZIuDEdZkNSP4PStpWGR\nkvD9UuB7wAtxqlsP7B6Wb35EYydJh4W/FxGcHjY7XmzJitoXn59K4fJZ1Pqb76inV9Tiz3R7o7bj\nneqOelTi8cTEudwSLznpFD7T5HKCWwc3vrpJ2j1BvR8BhwOLJc0HHgLONbPGW6UNk/Q2MA940sxe\nj1PXo8B4SeVA8wviBVwpaRmwAFgG3JcgtoSi9sXXli/KKMQTtR0Dt32JWn9LpXxrRDF+T0zSJ2o7\n3m3ZUY9CPJ6YuBQ0NDQ05O1pbVEQLv+Glj6P95yT+4DDgGcJkpKmK9KAr7Y0oZktBY6NU/dbjdec\nNJuuS/hzNsEthBsvih/Rwnw+BRLfbL4VovbF5zsGLp9Frb+lWj5ZUY3fE7H0iNqOtz+Q0eWpeWvW\nrBnas2fPDQUFBbl1QdF2oKGhQWvWrOlGMEARU8IL4tMtfKjiJbGSk/ZSWlq6qqampne25p9uJSUl\nldXV1X2av+/t3H7lS1u9nbnVToCSkpLV1dXVW7UpX9oJUFRUtKq+vj5n2lpYWFhZV1e3Td/NtXZC\n7LbmaN+NuT3KF2+99VavwsLCKcBwknven0uvBmBefX392aNHj14dq0C7JyfOOeecc845F4tnjM45\n55xzzrlI8OTEOeecc845FwnxnnNyrqSe7RmMc84555xzLn/FGzkZCvxb0kuSzpeUUxeEOeecc845\n56Il4QXxkvYFvk3wgMNPgEeAx8zsk8yH55xzzjnnnMsXrbpbl6RRwGXASWYW7xkpzjnnnHPOOdcq\nSSUYkoYQjJ6cCHQEJmUyqEzLtfuWx3mGQmVNTU2vbMSUCXn2rIiYbc21Zwt06NDh0/r6+u7N38+1\ndZrvfTfX+i20/PwP3+5uv2K1NV/a6VyUtDhyImkkQTLybYIHpjwKPGJm89svvMyQZLn0fBdJmJli\nvG8zZsyIxJOU01E+XjtzaX1C/LZOnDgxck9STrV8VVVVUut0e+if8bS270Yt/taUj9XWxn7bkqj2\nz3jlJ06cmPL2KErrK1H5fN/uNn6Pbi/rK5nyLa1T56Ii3gXxU4E64EQzG25mE1uTmEg6RtJbkhZI\nWizpriSm2RT+3EfSfeHv4yR9I4lpL5U0L9n48kUUN4zpLp9vorBjlq7yyYhaf0ulfGtEMf5M/j9G\nuX8mU761ora+fHubWJSWv69flw/iJSc/BD4wswVN35R0UniRfIsk9QJuBo42s6HA14B3kg3KzN40\ns9PDP8cBcZMTSfsDpyRbv/uPqG0YfUOaWJR2zDJdPmr9LdXyyYpq/JlKxKLW3zwxcbFEZfn7+nX5\nIl5yMhFYFuP9cuDaBPXuBtQCqwHMrMrMboMvR0LmSpouaamkqZK2uvZF0hhJMyX1BS4EJkhaJOno\n5jOS1IMgETo3QUyumahtGH1Dmpyo7Jh5YpJ8+WREOf5MJGJR62+emLiWRGH5+/p1+SRecjLAzF5t\n/qaZvQ30S1Dvu8B6YJ6k2ySdIqlTszKnAnsCOxLcpngbZrYS+D1wo5kNMbPnmn4uScC9wATCRMgl\nJ2obRt+QJi8KO2btseMXpf7m5ZMvn4yo9bdUyrdGlNeXb2/bLmrry9ev297FS06K43wW9yo4M9sC\nHAz8jCBJuQB4R1JpWORDM1sTXk33LLB38iFv5afAa2Y2M1FBSedImi1pdorzyhlR2zD6hjS9orgj\nl8oR6aj0Ny+f3vJR62+plk9W1Ja/b2/TK2rry9evywXxkpNXJf2g+ZuSzgMWxCi/FTOrN7PpZnaV\nme0PfAYMi1G0GKhONuBmyoDTJS0C/gEMkvRKC/H80czGmNmYFOeVE6K2YfQNaXpFdUculVNlotDf\n2qM/RykeT0ySL5+MqC3/VMq7lkVxffn3qcsF8Z5z8lPgGUknA2+E7+0L7AocGa9SSeMJEpE/mlmt\npH4Ep28tJbg4viQsVwp8D/hJnOrWAwPD8lvdu9DMftxkngOAZ8zswHix5bOobRh9Q5peUd6R83P4\nWxaVeDwxSW/5qC3/VMu72KK6vvz71OWCFkdOzOxTM/sGcBOwEfgc+F9gpJl9lKDej4DDgcWS5gMP\nAeea2Ybw82GS3gbmAU+a2etx6noUGC+pHNjmgniXnKhtGP0IXnpFbcfME5PkRSGe9mhvlPqbJybJ\nl3fbivL68sTE5YKET4g3s+nA9NZUamZLgWPjFHnLzL4VY7ou4c/ZBLcQbrwofkQS81wGDG9NnPki\nahtGP4KXXlHbMfPEJL2iFn+q7Y1Kf/ObOaS3fL6J2vL3A30uF7X4hPiMzVAaB1wSKzlpL6Wlpatq\namp6Z2v+6VZSUlJZXV3dp/n73s7tV0ttLSoqWlVfX58zbS0sLFxdV1e3TXtybZ3me98tKiqqrK+v\n75WtmDKhsLCwsq6ubpt12rFjx3W1tbU7ZyOmTMj3vpuj7VxdXV2dU21yuaXdkxPnnHPOOeeciyXe\n3bqcc84555xzrt14cuKcc84555yLBE9O2omkc7IdQ3vwduaefGmrtzP35EtbvZ25J5/a6lxznpy0\nn3zZ0Hg7c0++tNXbmXvypa3eztyTT211biuenDjnnHPOOeciwZMT55xzzjnnXCR4ctJ+/pjtANqJ\ntzP35EtbvZ25J1/a6u3MPfnUVue24s85cc4555xzzkWCj5w455xzzjnnIsGTE+ecc84551wkeHLS\nDiTtLem9bMeRSZJKJL0oqVzSEklXZjumTJH0gKSl4esxSZ2zHVMmSbpU0rxsx5FJkmZKWiZpUfj6\nRbZjygRJnSTdLul9SSsk7ZTtmNJN0sgm63FR2NaZ2Y4rEySdIWleuM19VFKXbMeUKZJ+JmmBpIWS\nLsl2POnWfD9BUndJz4fr9nlJO2czPufakycnGSbpJuAF8mNZ32BmA4GvAt+VNCrbAWXIPcCeZjYI\n2AyclN1wMkfS/sAp2Y6jnZxoZkPC12+yHUyG3AasAwYBuwOfZTec9DOzOU3W4xDgRuCdbMeVbpJ6\nA78C9jOzPYHVwI+zG1VmSDoIOA74GrA3cIKkr2c3qvRpYT/ht8AT4bp9ApiYhdCcy4p82GHOKjO7\nGBid7TgyzcxqzOyFxt+B94He2Y0qM8zsRTOzcMSkJ7Aw2zFlgqQewM3AudmOxbWdpD7A14GJ1kS2\n48okSYXAz4DJ2Y4lA4qBzkDjaMkqoDZ74WTUPsCLZrbZzKqBu4DjsxxT2rSwn3AI8GD4+4PA0e0a\nlHNZ5MmJS7vwiN7XgTeyHUumSDqLYGdgDvBmlsNJO0kC7gUmEByRzXUGPCppsaSbJXXIdkAZMJyg\nnS+F7Zya66ckAqcBL5vZymwHkm5mtgL4HbBQ0hRgLHBHdqPKmAXAEZI6h9um7kCun+bU3cw2AIQ/\nc729zn3JkxOXVpJKgEeAn5tZzp0y0sjM7gJ2AnoBZ2Q5nEz4KfCamc3MdiDt5CgzG0Bw2kg/4P9l\nN5yM6AUsAY4AhgKVBKcF5aQwwZwA3JDtWDJBUjeCU532A6YDewD/ldWgMsTMngOeB94C/g0cTnBw\nKJdtafZ3cVaicC4LPDlxaSOpI/Ao8DczuyfL4WScmdUDLwJjsh1LBpQBp0taBPwDGCTplSzHlDHh\nqYiYWRXwV2BgdiPKiPXAF+GpMVuAJ4G9shxTJn0PeNvMyrMdSIYcBiw0s4Vm9ghwCfA/WY4pY8zs\nN+F1RGOACuDdbMeUYRsab3AQJqKfZjke59qNJycuLSR1Ap4GXjGz67IdT6ZI2knSYeHvRQTnPc/O\nblTpZ2Y/NrPB4QXFhwBLzezAbMeVCeGd5saFvxcB/w28ltWgMuOfwEGSBoR/H0WOnnopqQC4EsjZ\nbRHwAXBgk7s4jQEWZTGejArXKZIOB/YFnspuRBn3EvDd8PfvERwkci4veHKSYZKuJthpHyhptqSD\nsx1ThuwDjAN+0OQWnrm4YyDgSknLCM6DXgbcl82AXJsJuDpcp3OBcuAvWY0oA8xsI/BD4ClJCwhO\n8/ptdqPKmG8D75tZzt4C28zeBn4PvC5pIcGper/OblQZ9aKk9wnuSHZsOPqXE1rYT7iU4K6XSwj6\n84Rsxuhce1KO36zFOeecc845t53wkRPnnHPOOedcJHhy4pxzzjnnnIsET06cc84555xzkeDJiXPO\nOeeccy4SPDlxzjnnnHPORYInJ845l2WSGiQtlFQu6U1J301imlGSjm6H2O6TtE+CMmdK2jXTsTjn\nnMt9hdkOwDnnHFVmtheApD2BeySVmNm9caYZRfDgvecyGZiZnZ5EsTOBecDHmYzFOedc7vORE+ec\nixAzW0LwROirACSNkLQ0fLDpPEmHhUWvA74fvv8jSUeEIy8LJb0taVSs+iXVSLpX0nxJ/5LUN3x/\nePj3YkmPSOoavv+MpHHh79WSpkhaED4sro+k/yZ4COujYSw9JP02jGWZpBszusCcc87lFE9OnHMu\nYsxsOVAgaWfgA2CkmQ0BzgauCYtdAUw1syFm9ifgDWCvcATmRuDyFqovAH5nZsOAJ8N6AO4DJpjZ\nYOA9wuSomQ7AFDMbCrwM/MDMngDeBE4MY+wLHAJ8BdiD4MnXzjnnXFL8tC7nnIsmC38KuCYcvegM\nFLdQviNwo6SvA12AD1soV2tmc8Lf/wpMkdQN6G5mr4TvPwA81sK0r4e/zyc4tay5j4DewG0ECcwz\nLcThnHPObcNHTpxzLmIk7QbUm9mnwC0ECcnBwP4EyUos0whGWfYFvhOnXFPFQHWKYW6JNQ8zWweM\nIBhNOR54McX6nXPO5SFPTpxzLkIkDQSmAr8O3+oO/N3MPgdGNym6Htg9nEZhuefMrLpZuRizUMfw\n9x8AL5jZBuBTSQeE758CzGxF2E1jGQzsbmb3ARcAg1pRj3POuTznyYlzzmVfp/Bi8nKCxOQ2M5sa\nfnYDcJOkhUDTWwy/CPSQVEFwLcpE4GlJ84ED48yrGHhJ0lKgB8HIDMAZwGRJi4C9gatbEf8dwP9K\nWgL0BP4UtmUWQYLinHPOJUVmlriUc865nCBpk5l1yXYczjnnXCw+cuKcc84555yLBB85cc4555xz\nzkWCj5w455xzzjnnIsGTE+ecc84551wkeHLinHPOOeeciwRPTpxzzjnnnHOR4MmJc84555xzLhL+\nPwZ85vtEyKB0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b692fe0978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_shuffle_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки:\n",
      "[ 0.90666667  0.85333333  0.96        0.96        0.97333333  0.92\n",
      "  0.97333333  0.93333333  0.97333333  0.90666667]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "shuffle_split = ShuffleSplit(test_size=.5, train_size=.5, n_splits=10)\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)\n",
    "print(\"Значения правильности перекрестной проверки:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Перекрестная проверка с использованием групп"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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96iE3tixAfvxl3vMf/nrn2lPCCQmxA9knAb8M/p0EfB4YYmbXJVuopPGSrg+2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b692f6cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_group_kfold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки:\n",
      "[ 0.75        0.8         0.66666667]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "# создаем синтетический набор данных\n",
    "X, y = make_blobs(n_samples=12, random_state=0)\n",
    "# предположим, что первые три примера относятся к одной и той же группе,\n",
    "# затем следующие четыре и так далее.\n",
    "groups = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "scores = cross_val_score(logreg, X, y, groups, cv=GroupKFold(n_splits=3))\n",
    "print(\"Значения правильности перекрестной проверки:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Решетчатый поиск"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Простой решетчатый поиск"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающего набора: 112 размер тестового набора: 38\n",
      "Наилучшее значение правильности: 0.97\n",
      "Наилучшие значения параметров: {'C': 100, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "# реализация наивного решетчатого поиска\n",
    "from sklearn.svm import SVC\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)\n",
    "print(\"Размер обучающего набора: {} размер тестового набора: {}\".format(\n",
    "      X_train.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # для каждой комбинации параметров обучаем SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # оцениваем качество SVC на тестовом наборе\n",
    "        score = svm.score(X_test, y_test)\n",
    "        # если получаем наилучшее значение правильности, сохраняем значение и параметры\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "print(\"Наилучшее значение правильности: {:.2f}\".format(best_score))\n",
    "print(\"Наилучшие значения параметров: {}\".format(best_parameters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Опасность переобучения параметров и проверочный набор данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
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mA2+WpEKANZKFFe/TpG4jzXgCODciDi8rsw14lUVvpCDdZNSntcPU/Hd1YFoX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\nPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b693348198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_threefold_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающего набора: 84 размер проверочного набора: 28 размер тестового набора: 38\n",
      "\n",
      "Лучшее значение правильности на проверочном наборе: 0.96\n",
      "Наилучшие значения параметров:  {'C': 10, 'gamma': 0.001}\n",
      "Правильность на тестовом наборе с наилучшими параметрами: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# разбиваем данные на обучающий+проверочный набор и тестовый набор\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)\n",
    "# разбиваем обучающий+проверочный набор на обучающий и проверочный наборы\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_trainval, y_trainval, random_state=1)\n",
    "print(\"Размер обучающего набора: {} размер проверочного набора: {} размер тестового набора:\"\n",
    "      \" {}\\n\".format(X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # для каждой комбинации параметров обучаем SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # оцениваем качество SVC на тестовом наборе\n",
    "        score = svm.score(X_valid, y_valid)\n",
    "        # если получаем наилучшее значение правильности, сохраняем значение и параметры\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "# заново строим модель на наборе, полученном в результате объединения обучающих \n",
    "# и проверочных данных, оцениваем качество модели на тестовом наборе\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)\n",
    "test_score = svm.score(X_test, y_test)\n",
    "print(\"Лучшее значение правильности на проверочном наборе: {:.2f}\".format(best_score))\n",
    "print(\"Наилучшие значения параметров: \", best_parameters)\n",
    "print(\"Правильность на тестовом наборе с наилучшими параметрами: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Решетчатный поиск с перекрестной проверкой"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # для каждой комбинации параметров,\n",
    "        # обучаем SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        # выполняем перекрестную проверку\n",
    "        scores = cross_val_score(svm, X_trainval, y_trainval, cv=5)\n",
    "        # вычисляем среднюю правильность перекрестной проверки \n",
    "        score = np.mean(scores)\n",
    "        # если получаем лучшее значение правильности, сохраняем значение и параметры\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "# заново строим модель на наборе, полученном в результате \n",
    "# объединения обучающих и проверочных данных\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OvRAZGdnVy8urpF27dkWtW7curi6vl19+OWPy5MmBr7/+ertevXpVlCcqKirz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\nBJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6933aee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_cross_val_selection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hide_input": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yzp4966TT6SJv3bo1SUQuaB0XE0sLxD5L66WUCvT09Dw1fPhw50mTJqFGjRpa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\nKeL/AVZAYcOaKJ2VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b69188eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_grid_search_overview()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сетка параметров:\n",
      "{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "\n",
    "print(\"Сетка параметров:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,\n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Правильность на тестовом наборе: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Правильность на тестовом наборе: {:.2f}\".format(grid_search.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Наилучшие значения параметров: {'C': 100, 'gamma': 0.01}\n",
      "Наилучшее значение кросс-валидац. правильности: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Наилучшие значения параметров: {}\".format(grid_search.best_params_))\n",
    "print(\"Наилучшее значение кросс-валидац. правильности: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Наилучшая модель:\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "print(\"Наилучшая модель:\\n{}\".format(grid_search.best_estimator_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Анализ результатов перекрестной проверки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001603</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.001}</td>\n",
       "      <td>22</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.011371</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.01}</td>\n",
       "      <td>22</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000637</td>\n",
       "      <td>0.011371</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001200</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.1}</td>\n",
       "      <td>22</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.011371</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1</td>\n",
       "      <td>{'C': 0.001, 'gamma': 1}</td>\n",
       "      <td>22</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.011371</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001401</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "      <td>{'C': 0.001, 'gamma': 10}</td>\n",
       "      <td>22</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.011371</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  mean_score_time  mean_test_score  mean_train_score param_C  \\\n",
       "0       0.001603         0.000800         0.366071          0.366079   0.001   \n",
       "1       0.000800         0.001003         0.366071          0.366079   0.001   \n",
       "2       0.001200         0.000199         0.366071          0.366079   0.001   \n",
       "3       0.001000         0.000601         0.366071          0.366079   0.001   \n",
       "4       0.001401         0.000801         0.366071          0.366079   0.001   \n",
       "\n",
       "  param_gamma                        params  rank_test_score  \\\n",
       "0       0.001  {'C': 0.001, 'gamma': 0.001}               22   \n",
       "1        0.01   {'C': 0.001, 'gamma': 0.01}               22   \n",
       "2         0.1    {'C': 0.001, 'gamma': 0.1}               22   \n",
       "3           1      {'C': 0.001, 'gamma': 1}               22   \n",
       "4          10     {'C': 0.001, 'gamma': 10}               22   \n",
       "\n",
       "   split0_test_score  split0_train_score       ...         split2_test_score  \\\n",
       "0              0.375            0.363636       ...                  0.363636   \n",
       "1              0.375            0.363636       ...                  0.363636   \n",
       "2              0.375            0.363636       ...                  0.363636   \n",
       "3              0.375            0.363636       ...                  0.363636   \n",
       "4              0.375            0.363636       ...                  0.363636   \n",
       "\n",
       "   split2_train_score  split3_test_score  split3_train_score  \\\n",
       "0            0.366667           0.363636            0.366667   \n",
       "1            0.366667           0.363636            0.366667   \n",
       "2            0.366667           0.363636            0.366667   \n",
       "3            0.366667           0.363636            0.366667   \n",
       "4            0.366667           0.363636            0.366667   \n",
       "\n",
       "   split4_test_score  split4_train_score  std_fit_time  std_score_time  \\\n",
       "0           0.380952            0.362637      0.000804        0.000400   \n",
       "1           0.380952            0.362637      0.000400        0.000637   \n",
       "2           0.380952            0.362637      0.000398        0.000398   \n",
       "3           0.380952            0.362637      0.000632        0.000491   \n",
       "4           0.380952            0.362637      0.000801        0.000400   \n",
       "\n",
       "   std_test_score  std_train_score  \n",
       "0        0.011371         0.002852  \n",
       "1        0.011371         0.002852  \n",
       "2        0.011371         0.002852  \n",
       "3        0.011371         0.002852  \n",
       "4        0.011371         0.002852  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# преобразуем в DataFrame\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "# показываем первые 5 строк\n",
    "display(results.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1b692f5a390>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kk5bqIarSoXXTKDMZ1XbCK+MtbNtSRKHLg9aa3Bw3uTkVg744cz0MvO40C+bk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b692fd4da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.array(results.mean_test_score).reshape(6, 6)\n",
    "\n",
    "# строим теплокарту средних значений правильности перекрестной проверки\n",
    "mglearn.tools.heatmap(scores, xlabel='gamma', xticklabels=param_grid['gamma'],\n",
    "                      ylabel='C', yticklabels=param_grid['C'], cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1b6931cb9e8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S6i1gq1KqCNgJVJxS4UI6wUIIIYQQ4oJdrMcma62jgegKn80D5pV5/yZwXsP/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\nCYAlOxYS2MKfqHPRLLx3CVarNmrToW87nl41h5wsMwkxibw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XcQPwvJSy1N5fCDHrxuf0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gAAAJ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\nP+kuOSIzgjbBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b692e065f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ffig, axes = plt.subplots(1, 3, figsize=(13, 5))\n",
    "\n",
    "param_grid_linear = {'C': np.linspace(1, 2, 6),\n",
    "                     'gamma': np.linspace(1, 2, 6)}\n",
    "\n",
    "param_grid_one_log = {'C': np.linspace(1, 2, 6),\n",
    "                      'gamma': np.logspace(-3, 2, 6)}\n",
    "\n",
    "param_grid_range = {'C': np.logspace(-3, 2, 6),\n",
    "                    'gamma': np.logspace(-7, -2, 6)}\n",
    "\n",
    "for param_grid, ax in zip([param_grid_linear, param_grid_one_log,\n",
    "                           param_grid_range], axes):\n",
    "    grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    scores = grid_search.cv_results_['mean_test_score'].reshape(6, 6)\n",
    "   \n",
    "    # строим теплокарту средних значений правильности перекрестной проверки\n",
    "    scores_image = mglearn.tools.heatmap(\n",
    "        scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
    "        yticklabels=param_grid['C'], cmap=\"viridis\", ax=ax)\n",
    "\n",
    "plt.colorbar(scores_image, ax=axes.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of grids:\n",
      "[{'kernel': ['rbf'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}, {'kernel': ['linear'], 'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n"
     ]
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "               'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
    "              {'kernel': ['linear'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n",
    "print(\"List of grids:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Наилучшие значения параметров: {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
      "Наилучшее значение кросс-валидац. правильности: 0.97\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5, return_train_score=True)\n",
    "grid_search.fit(X_train, y_train)\n",
    "print(\"Наилучшие значения параметров: {}\".format(grid_search.best_params_))\n",
    "print(\"Наилучшее значение кросс-валидац. правильности: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.00110087</td>\n",
       "      <td>0.00120034</td>\n",
       "      <td>0.00190072</td>\n",
       "      <td>0.000996685</td>\n",
       "      <td>0.00140114</td>\n",
       "      <td>0.00170083</td>\n",
       "      <td>0.00100102</td>\n",
       "      <td>0.00100117</td>\n",
       "      <td>0.00100064</td>\n",
       "      <td>0.00100031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000504541</td>\n",
       "      <td>0.000500345</td>\n",
       "      <td>0.00120111</td>\n",
       "      <td>0.00140104</td>\n",
       "      <td>0.000600863</td>\n",
       "      <td>0.000500631</td>\n",
       "      <td>0.000400448</td>\n",
       "      <td>0.000296259</td>\n",
       "      <td>0.000400496</td>\n",
       "      <td>0.000500345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.000400257</td>\n",
       "      <td>0.000499868</td>\n",
       "      <td>0.000701141</td>\n",
       "      <td>0.000400019</td>\n",
       "      <td>0.000601006</td>\n",
       "      <td>0.00060091</td>\n",
       "      <td>0.000299549</td>\n",
       "      <td>0.000500154</td>\n",
       "      <td>0.000300074</td>\n",
       "      <td>0.000400305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000200224</td>\n",
       "      <td>0.000400448</td>\n",
       "      <td>0.000300217</td>\n",
       "      <td>0.000200176</td>\n",
       "      <td>0.000299883</td>\n",
       "      <td>0.000200176</td>\n",
       "      <td>0.000300264</td>\n",
       "      <td>0.000300169</td>\n",
       "      <td>0.000200176</td>\n",
       "      <td>0.000200272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.955357</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>0.919643</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.366071</td>\n",
       "      <td>0.848214</td>\n",
       "      <td>0.946429</td>\n",
       "      <td>0.973214</td>\n",
       "      <td>0.964286</td>\n",
       "      <td>0.964286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988788</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.366079</td>\n",
       "      <td>0.855069</td>\n",
       "      <td>0.966538</td>\n",
       "      <td>0.984368</td>\n",
       "      <td>0.988813</td>\n",
       "      <td>0.993258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_C</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_gamma</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_kernel</th>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>...</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 10, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 100, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.01, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.01, 'gamma': 0.01, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.01, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.01, 'gamma': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>...</td>\n",
       "      <td>{'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 100, 'gamma': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 100, 'gamma': 10, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 100, 'gamma': 100, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 0.01, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 100, 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>1</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988636</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.886364</td>\n",
       "      <td>0.965909</td>\n",
       "      <td>0.988636</td>\n",
       "      <td>0.988636</td>\n",
       "      <td>0.988636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.913043</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>...</td>\n",
       "      <td>0.977528</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.88764</td>\n",
       "      <td>0.977528</td>\n",
       "      <td>0.977528</td>\n",
       "      <td>0.988764</td>\n",
       "      <td>0.988764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.988889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.954545</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.988889</td>\n",
       "      <td>0.988889</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.362637</td>\n",
       "      <td>0.879121</td>\n",
       "      <td>0.967033</td>\n",
       "      <td>0.989011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.000200224</td>\n",
       "      <td>0.000244195</td>\n",
       "      <td>0.000374942</td>\n",
       "      <td>0.000309912</td>\n",
       "      <td>0.000375044</td>\n",
       "      <td>0.000600473</td>\n",
       "      <td>0.000316507</td>\n",
       "      <td>6.97553e-07</td>\n",
       "      <td>2.13248e-07</td>\n",
       "      <td>3.56832e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>7.60611e-06</td>\n",
       "      <td>6.50319e-07</td>\n",
       "      <td>0.000245064</td>\n",
       "      <td>0.000374636</td>\n",
       "      <td>0.000200367</td>\n",
       "      <td>4.8628e-07</td>\n",
       "      <td>0.000200224</td>\n",
       "      <td>0.000242009</td>\n",
       "      <td>0.000200248</td>\n",
       "      <td>3.56832e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>0.000200129</td>\n",
       "      <td>1.31109e-06</td>\n",
       "      <td>0.000245321</td>\n",
       "      <td>0.00020001</td>\n",
       "      <td>0.000199821</td>\n",
       "      <td>0.000200344</td>\n",
       "      <td>0.000244582</td>\n",
       "      <td>3.8147e-07</td>\n",
       "      <td>0.000245009</td>\n",
       "      <td>0.000200152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000245224</td>\n",
       "      <td>0.000200226</td>\n",
       "      <td>0.000245126</td>\n",
       "      <td>0.000245165</td>\n",
       "      <td>0.000244856</td>\n",
       "      <td>0.000245165</td>\n",
       "      <td>0.000245165</td>\n",
       "      <td>0.000245087</td>\n",
       "      <td>0.000245165</td>\n",
       "      <td>0.000245282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0495662</td>\n",
       "      <td>0.0519227</td>\n",
       "      <td>0.0647906</td>\n",
       "      <td>0.0496678</td>\n",
       "      <td>0.0113708</td>\n",
       "      <td>0.0547783</td>\n",
       "      <td>0.0332185</td>\n",
       "      <td>0.0223995</td>\n",
       "      <td>0.0338387</td>\n",
       "      <td>0.0338387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00999451</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00285176</td>\n",
       "      <td>0.0503114</td>\n",
       "      <td>0.0121316</td>\n",
       "      <td>0.00548507</td>\n",
       "      <td>0.00702801</td>\n",
       "      <td>0.00550551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               0   \\\n",
       "mean_fit_time                                          0.00110087   \n",
       "mean_score_time                                       0.000400257   \n",
       "mean_test_score                                          0.366071   \n",
       "mean_train_score                                         0.366079   \n",
       "param_C                                                     0.001   \n",
       "param_gamma                                                 0.001   \n",
       "param_kernel                                                  rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "rank_test_score                                                27   \n",
       "split0_test_score                                           0.375   \n",
       "split0_train_score                                       0.363636   \n",
       "split1_test_score                                        0.347826   \n",
       "split1_train_score                                       0.370787   \n",
       "split2_test_score                                        0.363636   \n",
       "split2_train_score                                       0.366667   \n",
       "split3_test_score                                        0.363636   \n",
       "split3_train_score                                       0.366667   \n",
       "split4_test_score                                        0.380952   \n",
       "split4_train_score                                       0.362637   \n",
       "std_fit_time                                          0.000200224   \n",
       "std_score_time                                        0.000200129   \n",
       "std_test_score                                          0.0113708   \n",
       "std_train_score                                        0.00285176   \n",
       "\n",
       "                                                              1   \\\n",
       "mean_fit_time                                         0.00120034   \n",
       "mean_score_time                                      0.000499868   \n",
       "mean_test_score                                         0.366071   \n",
       "mean_train_score                                        0.366079   \n",
       "param_C                                                    0.001   \n",
       "param_gamma                                                 0.01   \n",
       "param_kernel                                                 rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}   \n",
       "rank_test_score                                               27   \n",
       "split0_test_score                                          0.375   \n",
       "split0_train_score                                      0.363636   \n",
       "split1_test_score                                       0.347826   \n",
       "split1_train_score                                      0.370787   \n",
       "split2_test_score                                       0.363636   \n",
       "split2_train_score                                      0.366667   \n",
       "split3_test_score                                       0.363636   \n",
       "split3_train_score                                      0.366667   \n",
       "split4_test_score                                       0.380952   \n",
       "split4_train_score                                      0.362637   \n",
       "std_fit_time                                         0.000244195   \n",
       "std_score_time                                       1.31109e-06   \n",
       "std_test_score                                         0.0113708   \n",
       "std_train_score                                       0.00285176   \n",
       "\n",
       "                                                             2   \\\n",
       "mean_fit_time                                        0.00190072   \n",
       "mean_score_time                                     0.000701141   \n",
       "mean_test_score                                        0.366071   \n",
       "mean_train_score                                       0.366079   \n",
       "param_C                                                   0.001   \n",
       "param_gamma                                                 0.1   \n",
       "param_kernel                                                rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                              27   \n",
       "split0_test_score                                         0.375   \n",
       "split0_train_score                                     0.363636   \n",
       "split1_test_score                                      0.347826   \n",
       "split1_train_score                                     0.370787   \n",
       "split2_test_score                                      0.363636   \n",
       "split2_train_score                                     0.366667   \n",
       "split3_test_score                                      0.363636   \n",
       "split3_train_score                                     0.366667   \n",
       "split4_test_score                                      0.380952   \n",
       "split4_train_score                                     0.362637   \n",
       "std_fit_time                                        0.000374942   \n",
       "std_score_time                                      0.000245321   \n",
       "std_test_score                                        0.0113708   \n",
       "std_train_score                                      0.00285176   \n",
       "\n",
       "                                                           3   \\\n",
       "mean_fit_time                                     0.000996685   \n",
       "mean_score_time                                   0.000400019   \n",
       "mean_test_score                                      0.366071   \n",
       "mean_train_score                                     0.366079   \n",
       "param_C                                                 0.001   \n",
       "param_gamma                                                 1   \n",
       "param_kernel                                              rbf   \n",
       "params              {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                            27   \n",
       "split0_test_score                                       0.375   \n",
       "split0_train_score                                   0.363636   \n",
       "split1_test_score                                    0.347826   \n",
       "split1_train_score                                   0.370787   \n",
       "split2_test_score                                    0.363636   \n",
       "split2_train_score                                   0.366667   \n",
       "split3_test_score                                    0.363636   \n",
       "split3_train_score                                   0.366667   \n",
       "split4_test_score                                    0.380952   \n",
       "split4_train_score                                   0.362637   \n",
       "std_fit_time                                      0.000309912   \n",
       "std_score_time                                     0.00020001   \n",
       "std_test_score                                      0.0113708   \n",
       "std_train_score                                    0.00285176   \n",
       "\n",
       "                                                            4   \\\n",
       "mean_fit_time                                       0.00140114   \n",
       "mean_score_time                                    0.000601006   \n",
       "mean_test_score                                       0.366071   \n",
       "mean_train_score                                      0.366079   \n",
       "param_C                                                  0.001   \n",
       "param_gamma                                                 10   \n",
       "param_kernel                                               rbf   \n",
       "params              {'C': 0.001, 'gamma': 10, 'kernel': 'rbf'}   \n",
       "rank_test_score                                             27   \n",
       "split0_test_score                                        0.375   \n",
       "split0_train_score                                    0.363636   \n",
       "split1_test_score                                     0.347826   \n",
       "split1_train_score                                    0.370787   \n",
       "split2_test_score                                     0.363636   \n",
       "split2_train_score                                    0.366667   \n",
       "split3_test_score                                     0.363636   \n",
       "split3_train_score                                    0.366667   \n",
       "split4_test_score                                     0.380952   \n",
       "split4_train_score                                    0.362637   \n",
       "std_fit_time                                       0.000375044   \n",
       "std_score_time                                     0.000199821   \n",
       "std_test_score                                       0.0113708   \n",
       "std_train_score                                     0.00285176   \n",
       "\n",
       "                                                             5   \\\n",
       "mean_fit_time                                        0.00170083   \n",
       "mean_score_time                                      0.00060091   \n",
       "mean_test_score                                        0.366071   \n",
       "mean_train_score                                       0.366079   \n",
       "param_C                                                   0.001   \n",
       "param_gamma                                                 100   \n",
       "param_kernel                                                rbf   \n",
       "params              {'C': 0.001, 'gamma': 100, 'kernel': 'rbf'}   \n",
       "rank_test_score                                              27   \n",
       "split0_test_score                                         0.375   \n",
       "split0_train_score                                     0.363636   \n",
       "split1_test_score                                      0.347826   \n",
       "split1_train_score                                     0.370787   \n",
       "split2_test_score                                      0.363636   \n",
       "split2_train_score                                     0.366667   \n",
       "split3_test_score                                      0.363636   \n",
       "split3_train_score                                     0.366667   \n",
       "split4_test_score                                      0.380952   \n",
       "split4_train_score                                     0.362637   \n",
       "std_fit_time                                        0.000600473   \n",
       "std_score_time                                      0.000200344   \n",
       "std_test_score                                        0.0113708   \n",
       "std_train_score                                      0.00285176   \n",
       "\n",
       "                                                              6   \\\n",
       "mean_fit_time                                         0.00100102   \n",
       "mean_score_time                                      0.000299549   \n",
       "mean_test_score                                         0.366071   \n",
       "mean_train_score                                        0.366079   \n",
       "param_C                                                     0.01   \n",
       "param_gamma                                                0.001   \n",
       "param_kernel                                                 rbf   \n",
       "params              {'C': 0.01, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "rank_test_score                                               27   \n",
       "split0_test_score                                          0.375   \n",
       "split0_train_score                                      0.363636   \n",
       "split1_test_score                                       0.347826   \n",
       "split1_train_score                                      0.370787   \n",
       "split2_test_score                                       0.363636   \n",
       "split2_train_score                                      0.366667   \n",
       "split3_test_score                                       0.363636   \n",
       "split3_train_score                                      0.366667   \n",
       "split4_test_score                                       0.380952   \n",
       "split4_train_score                                      0.362637   \n",
       "std_fit_time                                         0.000316507   \n",
       "std_score_time                                       0.000244582   \n",
       "std_test_score                                         0.0113708   \n",
       "std_train_score                                       0.00285176   \n",
       "\n",
       "                                                             7   \\\n",
       "mean_fit_time                                        0.00100117   \n",
       "mean_score_time                                     0.000500154   \n",
       "mean_test_score                                        0.366071   \n",
       "mean_train_score                                       0.366079   \n",
       "param_C                                                    0.01   \n",
       "param_gamma                                                0.01   \n",
       "param_kernel                                                rbf   \n",
       "params              {'C': 0.01, 'gamma': 0.01, 'kernel': 'rbf'}   \n",
       "rank_test_score                                              27   \n",
       "split0_test_score                                         0.375   \n",
       "split0_train_score                                     0.363636   \n",
       "split1_test_score                                      0.347826   \n",
       "split1_train_score                                     0.370787   \n",
       "split2_test_score                                      0.363636   \n",
       "split2_train_score                                     0.366667   \n",
       "split3_test_score                                      0.363636   \n",
       "split3_train_score                                     0.366667   \n",
       "split4_test_score                                      0.380952   \n",
       "split4_train_score                                     0.362637   \n",
       "std_fit_time                                        6.97553e-07   \n",
       "std_score_time                                       3.8147e-07   \n",
       "std_test_score                                        0.0113708   \n",
       "std_train_score                                      0.00285176   \n",
       "\n",
       "                                                            8   \\\n",
       "mean_fit_time                                       0.00100064   \n",
       "mean_score_time                                    0.000300074   \n",
       "mean_test_score                                       0.366071   \n",
       "mean_train_score                                      0.366079   \n",
       "param_C                                                   0.01   \n",
       "param_gamma                                                0.1   \n",
       "param_kernel                                               rbf   \n",
       "params              {'C': 0.01, 'gamma': 0.1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                             27   \n",
       "split0_test_score                                        0.375   \n",
       "split0_train_score                                    0.363636   \n",
       "split1_test_score                                     0.347826   \n",
       "split1_train_score                                    0.370787   \n",
       "split2_test_score                                     0.363636   \n",
       "split2_train_score                                    0.366667   \n",
       "split3_test_score                                     0.363636   \n",
       "split3_train_score                                    0.366667   \n",
       "split4_test_score                                     0.380952   \n",
       "split4_train_score                                    0.362637   \n",
       "std_fit_time                                       2.13248e-07   \n",
       "std_score_time                                     0.000245009   \n",
       "std_test_score                                       0.0113708   \n",
       "std_train_score                                     0.00285176   \n",
       "\n",
       "                                                          9   \\\n",
       "mean_fit_time                                     0.00100031   \n",
       "mean_score_time                                  0.000400305   \n",
       "mean_test_score                                     0.366071   \n",
       "mean_train_score                                    0.366079   \n",
       "param_C                                                 0.01   \n",
       "param_gamma                                                1   \n",
       "param_kernel                                             rbf   \n",
       "params              {'C': 0.01, 'gamma': 1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                           27   \n",
       "split0_test_score                                      0.375   \n",
       "split0_train_score                                  0.363636   \n",
       "split1_test_score                                   0.347826   \n",
       "split1_train_score                                  0.370787   \n",
       "split2_test_score                                   0.363636   \n",
       "split2_train_score                                  0.366667   \n",
       "split3_test_score                                   0.363636   \n",
       "split3_train_score                                  0.366667   \n",
       "split4_test_score                                   0.380952   \n",
       "split4_train_score                                  0.362637   \n",
       "std_fit_time                                     3.56832e-07   \n",
       "std_score_time                                   0.000200152   \n",
       "std_test_score                                     0.0113708   \n",
       "std_train_score                                   0.00285176   \n",
       "\n",
       "                                 ...                \\\n",
       "mean_fit_time                    ...                 \n",
       "mean_score_time                  ...                 \n",
       "mean_test_score                  ...                 \n",
       "mean_train_score                 ...                 \n",
       "param_C                          ...                 \n",
       "param_gamma                      ...                 \n",
       "param_kernel                     ...                 \n",
       "params                           ...                 \n",
       "rank_test_score                  ...                 \n",
       "split0_test_score                ...                 \n",
       "split0_train_score               ...                 \n",
       "split1_test_score                ...                 \n",
       "split1_train_score               ...                 \n",
       "split2_test_score                ...                 \n",
       "split2_train_score               ...                 \n",
       "split3_test_score                ...                 \n",
       "split3_train_score               ...                 \n",
       "split4_test_score                ...                 \n",
       "split4_train_score               ...                 \n",
       "std_fit_time                     ...                 \n",
       "std_score_time                   ...                 \n",
       "std_test_score                   ...                 \n",
       "std_train_score                  ...                 \n",
       "\n",
       "                                                           32  \\\n",
       "mean_fit_time                                     0.000504541   \n",
       "mean_score_time                                   0.000200224   \n",
       "mean_test_score                                      0.955357   \n",
       "mean_train_score                                     0.988788   \n",
       "param_C                                                   100   \n",
       "param_gamma                                               0.1   \n",
       "param_kernel                                              rbf   \n",
       "params              {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                             9   \n",
       "split0_test_score                                    0.958333   \n",
       "split0_train_score                                   0.988636   \n",
       "split1_test_score                                           1   \n",
       "split1_train_score                                   0.977528   \n",
       "split2_test_score                                           1   \n",
       "split2_train_score                                   0.977778   \n",
       "split3_test_score                                    0.863636   \n",
       "split3_train_score                                          1   \n",
       "split4_test_score                                    0.952381   \n",
       "split4_train_score                                          1   \n",
       "std_fit_time                                      7.60611e-06   \n",
       "std_score_time                                    0.000245224   \n",
       "std_test_score                                      0.0495662   \n",
       "std_train_score                                    0.00999451   \n",
       "\n",
       "                                                         33  \\\n",
       "mean_fit_time                                   0.000500345   \n",
       "mean_score_time                                 0.000400448   \n",
       "mean_test_score                                    0.946429   \n",
       "mean_train_score                                          1   \n",
       "param_C                                                 100   \n",
       "param_gamma                                               1   \n",
       "param_kernel                                            rbf   \n",
       "params              {'C': 100, 'gamma': 1, 'kernel': 'rbf'}   \n",
       "rank_test_score                                          11   \n",
       "split0_test_score                                  0.916667   \n",
       "split0_train_score                                        1   \n",
       "split1_test_score                                         1   \n",
       "split1_train_score                                        1   \n",
       "split2_test_score                                         1   \n",
       "split2_train_score                                        1   \n",
       "split3_test_score                                  0.863636   \n",
       "split3_train_score                                        1   \n",
       "split4_test_score                                  0.952381   \n",
       "split4_train_score                                        1   \n",
       "std_fit_time                                    6.50319e-07   \n",
       "std_score_time                                  0.000200226   \n",
       "std_test_score                                    0.0519227   \n",
       "std_train_score                                           0   \n",
       "\n",
       "                                                          34  \\\n",
       "mean_fit_time                                     0.00120111   \n",
       "mean_score_time                                  0.000300217   \n",
       "mean_test_score                                     0.919643   \n",
       "mean_train_score                                           1   \n",
       "param_C                                                  100   \n",
       "param_gamma                                               10   \n",
       "param_kernel                                             rbf   \n",
       "params              {'C': 100, 'gamma': 10, 'kernel': 'rbf'}   \n",
       "rank_test_score                                           17   \n",
       "split0_test_score                                      0.875   \n",
       "split0_train_score                                         1   \n",
       "split1_test_score                                   0.956522   \n",
       "split1_train_score                                         1   \n",
       "split2_test_score                                          1   \n",
       "split2_train_score                                         1   \n",
       "split3_test_score                                   0.818182   \n",
       "split3_train_score                                         1   \n",
       "split4_test_score                                   0.952381   \n",
       "split4_train_score                                         1   \n",
       "std_fit_time                                     0.000245064   \n",
       "std_score_time                                   0.000245126   \n",
       "std_test_score                                     0.0647906   \n",
       "std_train_score                                            0   \n",
       "\n",
       "                                                           35  \\\n",
       "mean_fit_time                                      0.00140104   \n",
       "mean_score_time                                   0.000200176   \n",
       "mean_test_score                                        0.5625   \n",
       "mean_train_score                                            1   \n",
       "param_C                                                   100   \n",
       "param_gamma                                               100   \n",
       "param_kernel                                              rbf   \n",
       "params              {'C': 100, 'gamma': 100, 'kernel': 'rbf'}   \n",
       "rank_test_score                                            24   \n",
       "split0_test_score                                    0.541667   \n",
       "split0_train_score                                          1   \n",
       "split1_test_score                                    0.478261   \n",
       "split1_train_score                                          1   \n",
       "split2_test_score                                    0.590909   \n",
       "split2_train_score                                          1   \n",
       "split3_test_score                                    0.590909   \n",
       "split3_train_score                                          1   \n",
       "split4_test_score                                    0.619048   \n",
       "split4_train_score                                          1   \n",
       "std_fit_time                                      0.000374636   \n",
       "std_score_time                                    0.000245165   \n",
       "std_test_score                                      0.0496678   \n",
       "std_train_score                                             0   \n",
       "\n",
       "                                                  36  \\\n",
       "mean_fit_time                            0.000600863   \n",
       "mean_score_time                          0.000299883   \n",
       "mean_test_score                             0.366071   \n",
       "mean_train_score                            0.366079   \n",
       "param_C                                        0.001   \n",
       "param_gamma                                      NaN   \n",
       "param_kernel                                  linear   \n",
       "params              {'C': 0.001, 'kernel': 'linear'}   \n",
       "rank_test_score                                   27   \n",
       "split0_test_score                              0.375   \n",
       "split0_train_score                          0.363636   \n",
       "split1_test_score                           0.347826   \n",
       "split1_train_score                          0.370787   \n",
       "split2_test_score                           0.363636   \n",
       "split2_train_score                          0.366667   \n",
       "split3_test_score                           0.363636   \n",
       "split3_train_score                          0.366667   \n",
       "split4_test_score                           0.380952   \n",
       "split4_train_score                          0.362637   \n",
       "std_fit_time                             0.000200367   \n",
       "std_score_time                           0.000244856   \n",
       "std_test_score                             0.0113708   \n",
       "std_train_score                           0.00285176   \n",
       "\n",
       "                                                 37  \\\n",
       "mean_fit_time                           0.000500631   \n",
       "mean_score_time                         0.000200176   \n",
       "mean_test_score                            0.848214   \n",
       "mean_train_score                           0.855069   \n",
       "param_C                                        0.01   \n",
       "param_gamma                                     NaN   \n",
       "param_kernel                                 linear   \n",
       "params              {'C': 0.01, 'kernel': 'linear'}   \n",
       "rank_test_score                                  21   \n",
       "split0_test_score                          0.916667   \n",
       "split0_train_score                         0.886364   \n",
       "split1_test_score                          0.826087   \n",
       "split1_train_score                          0.88764   \n",
       "split2_test_score                          0.818182   \n",
       "split2_train_score                         0.866667   \n",
       "split3_test_score                          0.772727   \n",
       "split3_train_score                         0.755556   \n",
       "split4_test_score                          0.904762   \n",
       "split4_train_score                         0.879121   \n",
       "std_fit_time                             4.8628e-07   \n",
       "std_score_time                          0.000245165   \n",
       "std_test_score                            0.0547783   \n",
       "std_train_score                           0.0503114   \n",
       "\n",
       "                                                38  \\\n",
       "mean_fit_time                          0.000400448   \n",
       "mean_score_time                        0.000300264   \n",
       "mean_test_score                           0.946429   \n",
       "mean_train_score                          0.966538   \n",
       "param_C                                        0.1   \n",
       "param_gamma                                    NaN   \n",
       "param_kernel                                linear   \n",
       "params              {'C': 0.1, 'kernel': 'linear'}   \n",
       "rank_test_score                                 11   \n",
       "split0_test_score                         0.958333   \n",
       "split0_train_score                        0.965909   \n",
       "split1_test_score                         0.913043   \n",
       "split1_train_score                        0.977528   \n",
       "split2_test_score                                1   \n",
       "split2_train_score                        0.944444   \n",
       "split3_test_score                         0.909091   \n",
       "split3_train_score                        0.977778   \n",
       "split4_test_score                         0.952381   \n",
       "split4_train_score                        0.967033   \n",
       "std_fit_time                           0.000200224   \n",
       "std_score_time                         0.000245165   \n",
       "std_test_score                           0.0332185   \n",
       "std_train_score                          0.0121316   \n",
       "\n",
       "                                              39  \\\n",
       "mean_fit_time                        0.000296259   \n",
       "mean_score_time                      0.000300169   \n",
       "mean_test_score                         0.973214   \n",
       "mean_train_score                        0.984368   \n",
       "param_C                                        1   \n",
       "param_gamma                                  NaN   \n",
       "param_kernel                              linear   \n",
       "params              {'C': 1, 'kernel': 'linear'}   \n",
       "rank_test_score                                1   \n",
       "split0_test_score                              1   \n",
       "split0_train_score                      0.988636   \n",
       "split1_test_score                       0.956522   \n",
       "split1_train_score                      0.977528   \n",
       "split2_test_score                              1   \n",
       "split2_train_score                      0.977778   \n",
       "split3_test_score                       0.954545   \n",
       "split3_train_score                      0.988889   \n",
       "split4_test_score                       0.952381   \n",
       "split4_train_score                      0.989011   \n",
       "std_fit_time                         0.000242009   \n",
       "std_score_time                       0.000245087   \n",
       "std_test_score                         0.0223995   \n",
       "std_train_score                       0.00548507   \n",
       "\n",
       "                                               40  \\\n",
       "mean_fit_time                         0.000400496   \n",
       "mean_score_time                       0.000200176   \n",
       "mean_test_score                          0.964286   \n",
       "mean_train_score                         0.988813   \n",
       "param_C                                        10   \n",
       "param_gamma                                   NaN   \n",
       "param_kernel                               linear   \n",
       "params              {'C': 10, 'kernel': 'linear'}   \n",
       "rank_test_score                                 3   \n",
       "split0_test_score                        0.958333   \n",
       "split0_train_score                       0.988636   \n",
       "split1_test_score                               1   \n",
       "split1_train_score                       0.988764   \n",
       "split2_test_score                               1   \n",
       "split2_train_score                       0.977778   \n",
       "split3_test_score                        0.909091   \n",
       "split3_train_score                       0.988889   \n",
       "split4_test_score                        0.952381   \n",
       "split4_train_score                              1   \n",
       "std_fit_time                          0.000200248   \n",
       "std_score_time                        0.000245165   \n",
       "std_test_score                          0.0338387   \n",
       "std_train_score                        0.00702801   \n",
       "\n",
       "                                                41  \n",
       "mean_fit_time                          0.000500345  \n",
       "mean_score_time                        0.000200272  \n",
       "mean_test_score                           0.964286  \n",
       "mean_train_score                          0.993258  \n",
       "param_C                                        100  \n",
       "param_gamma                                    NaN  \n",
       "param_kernel                                linear  \n",
       "params              {'C': 100, 'kernel': 'linear'}  \n",
       "rank_test_score                                  3  \n",
       "split0_test_score                         0.958333  \n",
       "split0_train_score                        0.988636  \n",
       "split1_test_score                                1  \n",
       "split1_train_score                        0.988764  \n",
       "split2_test_score                                1  \n",
       "split2_train_score                        0.988889  \n",
       "split3_test_score                         0.909091  \n",
       "split3_train_score                               1  \n",
       "split4_test_score                         0.952381  \n",
       "split4_train_score                               1  \n",
       "std_fit_time                           3.56832e-07  \n",
       "std_score_time                         0.000245282  \n",
       "std_test_score                           0.0338387  \n",
       "std_train_score                         0.00550551  \n",
       "\n",
       "[23 rows x 42 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "# мы выводим транспонированную таблицу для лучшего отображения на странице:\n",
    "display(results.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Применение различных стратегий перекрестной проверки с помощью решетчатого поиска"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Вложенная перекрестная проверка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки:  [ 0.96666667  1.          0.9         0.96666667  1.        ]\n",
      "Среднеее значение правильности перекрестной проверки:  0.966666666667\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5),\n",
    "                         iris.data, iris.target, cv=5)\n",
    "print(\"Значения правильности перекрестной проверки: \", scores)\n",
    "print(\"Среднеее значение правильности перекрестной проверки: \", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nested_cv(X, y, inner_cv, outer_cv, Classifier, parameter_grid):\n",
    "    outer_scores = []\n",
    "    # для каждого разбиения данных во внешней перекрестной проверке\n",
    "    # (метод split возвращает индексы)\t\n",
    "    for training_samples, test_samples in outer_cv.split(X, y):\n",
    "        # находим наилучшие параметры с помощью внутренней перекрестной проверки\n",
    "        best_parms = {}\n",
    "        best_score = -np.inf\n",
    "        # итерируем по параметрам\n",
    "        for parameters in parameter_grid:\n",
    "            # собираем значения правильности по всем внутренним разбиениям\n",
    "            cv_scores = []\n",
    "            # итерируем по разбиениям внутренней перекрестной проверки \n",
    "            for inner_train, inner_test in inner_cv.split(\n",
    "                    X[training_samples], y[training_samples]):\n",
    "                # строим классификатор с данными параметрами на внутреннем обучающем наборе\n",
    "                clf = Classifier(**parameters)\n",
    "                clf.fit(X[inner_train], y[inner_train])\n",
    "                # оцениваем качество на внутреннем тестовом наборе\n",
    "                score = clf.score(X[inner_test], y[inner_test])\n",
    "                cv_scores.append(score)\n",
    "            # вычисляем среднее значение правильности по внутренним блокам\n",
    "            mean_score = np.mean(cv_scores)\n",
    "            if mean_score > best_score:\n",
    "                # если лучше, чем предыдущие, запоминаем параметры\n",
    "                best_score = mean_score\n",
    "                best_params = parameters\n",
    "        # строим классификатор с лучшими параметрами на внешнем обучающем наборе\n",
    "        clf = Classifier(**best_params)\n",
    "        clf.fit(X[training_samples], y[training_samples])\n",
    "        # оцениваем качество на внешнем тестовом наборе\n",
    "        outer_scores.append(clf.score(X[test_samples], y[test_samples]))\n",
    "    return np.array(outer_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значения правильности перекрестной проверки: [ 0.96666667  1.          0.96666667  0.96666667  1.        ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ParameterGrid, StratifiedKFold\n",
    "scores = nested_cv(iris.data, iris.target, StratifiedKFold(5),\n",
    "          StratifiedKFold(5), SVC, ParameterGrid(param_grid))\n",
    "print(\"Значения правильности перекрестной проверки: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Распараллеливание перекрестной проверки и решетчатого поиска"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Метрики качества модели и их вычисление\n",
    "#### Помните о конечной цели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Метрики для бинарной классификации\n",
    "##### Типы ошибок\n",
    "##### Несбалансированные наборы данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "\n",
    "digits = load_digits()\n",
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уникальные спрогнозированные метки: [False]\n",
      "Правильность на тестовом наборе: 0.90\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequent = dummy_majority.predict(X_test)\n",
    "print(\"Уникальные спрогнозированные метки: {}\".format(np.unique(pred_most_frequent)))\n",
    "print(\"Правильность на тестовом наборе: {:.2f}\".format(dummy_majority.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Правильность на тестовом наборе: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "pred_tree = tree.predict(X_test)\n",
    "print(\"Правильность на тестовом наборе: {:.2f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "правильность dummy: 0.84\n",
      "правильность logreg: 0.98\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "dummy = DummyClassifier().fit(X_train, y_train)\n",
    "pred_dummy = dummy.predict(X_test)\n",
    "print(\"правильность dummy: {:.2f}\".format(dummy.score(X_test, y_test)))\n",
    "\n",
    "logreg = LogisticRegression(C=0.1).fit(X_train, y_train)\n",
    "pred_logreg = logreg.predict(X_test)\n",
    "print(\"правильность logreg: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Матрица ошибок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица ошибок:\n",
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion = confusion_matrix(y_test, pred_logreg)\n",
    "print(\"Матрица ошибок:\\n{}\".format(confusion))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kJSUluQ0EQFj0kAEAADhGQgYAAOAYCRkAAIBjJGQAAACOMakfAHxsyJAhrkMA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6931afe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_confusion_matrix_illustration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qRKdOncJOQ9LQkiVLGDduXKDYSy65hHPPPTfJGZVMhVQkjZRm8O99+/alIKPk\nWbt2bagjdkh6WrlyJdddd13gvgBDhw5NckbxqZCKpJGgg39v2rSJxx57LAUZJU/v3r3p3bt32GlI\nGlmwYAGXXnpp4Pvmdu7cOfB4pclULgqpmeWamTOzxv7zxmY2wszqxcSNMLO06NLo5xLeuD5SbgUd\n/HvMmDHs3LkzBRmJJNeWLVu45ZZbuPrqq9m7d2+geU4++WQeffTRJGcWTLkopMBLwGXAFv95Y2A4\nEHtPqJl+nEi5FXTw72+++YaxY8emICORsuecIz8/nz59+tCkSROmT59eqvmffvppGjZsmKTsSqdy\n2AkE4ZzbDMQd4dg5tx5Yn/yMRJJr4MCBTJ48Oe59RidOnMh///d/U79+/RRlJlI6hw8fZvv27Wzd\nupV169bx8ccf8/bbb7NixQq2bNkSfwFFGDlyZKDLxVIloSNSM5thZv8ys95mttbMvjOzt8zspzFx\nPzWzl8xsj5ntMLOZZlY3JuZGMyvwYzaa2QIz+z/+tI6Rpl2/efdv/mzv+a87P+72qN8bmVmhmfWI\nWU9lM/vazEZEvdbazP5mZnvNbIuZ/dHMzgjw/hub2Vz/Pe0ys9fNrHMxsc3N7A0z22pm+/33OMnM\nakXFZJnZg2a2zs/lYzN70sxO8qef6M/zub+d3jezR82sXHwhijV9+nScc6E90llpBv8OcuN7kbJU\nv379Eh+nnnoqJ598MjVq1CAzM5NTTz2Vc845h2uuuYbf//73zJ8//5iL6N13350WHYyilcUHcDbQ\nCxgFHAKGAS+YWXPnXKGZ/Qj4O/Ah8DugOnA/8JKZ/dw5d9DMfo3XLPswkA+cDvQEfgW8F7O+LcBA\n4BHgVmBDUUk55z4zs9eAbsCsqEmXA3X99WFmrYCXgSeACUBN4E7gRTPLds4VeVsN//xsPvANMADY\nBXQC+gB/KmKWKsC7/nq+BpoCDwC1gJv8mFHALf72+QxoCfQFGgA7gWlADl6z9tfAef70+4HwhoeX\npOjXrx8TJkzgiy++KDHuySefZMCAATRt2jRFmZWNdPswlOC2bt2a8nWaGWPGjOGee+5J+brjKYtC\n+iFwvnPuMICZfQy8jXeu8iXgbqAQaOec2+vH/AOvqFwLzAWuAD51zg2JWu6TZnZi7Mqcc9+Z2Tv+\n05XOuZKGBpgF/MHM6jnnvvJf6wa84TcDA4wBHsUrYhErgU+Bi4BXiln2XUAN4KfOuR3+a8+b2elF\nBTvn3sMHIMqvAAASLklEQVQrst/zj8oHRr10BTDfOTfZf77YzCbwQ8vBFcBDzrnIyYRFZjYOOOrs\nvJn1wvuCwxlnxD24ljQUGfz71ltvLTEuMvj37NmzU5RZ2Wjbtm3YKUg5cfLJJzNt2jSuvfbasFMp\nUll0NtobKaIAzrlVeB/sZ/svXQQsixRRP+Y9YB3wS/+lj4Afm9k4M2tlZlX9uF0J5vZHvKPk6wDM\nrDpwFT8cjVYHWgNDgX1Rj0/9+ZuVsOxLgJejimjkvW0qKtg8Pc3sFb/5+DtgBN4RcMRHwG/MrL+Z\ntTSzSs65A86576Km9zGz35nZmWZmzrndzrnC2PU556Y653KcczmnnHJKCW9D0llubi7NmzePGzdn\nzhzeey+28Sa9FRQUUFBQEHYakuYuu+wy3nvvvbQtopC8XrvfApFP79p4zZCxvvanAfwB74gwF6+5\n9Fsze87MTk4kCefct8AC4Eb/pauADGCe//xk//lwoFURjwUlLL4OP/QiDuJB4GlgDzAIuBLvSDha\nH+BFvCbuNcB2M8uzH+6w3hXvSH4K8AmwyczuKEUOUs5UrlyZvLy8uHGFhYUMGTIkblw66d+/P/37\n9w87DUlTLVq04IUXXuDll1/mRz/6UdjplKjMC6mZZeCdg4wUzx0cfZkK/ms7AJxzh51z9/uvnYV3\nzvHXeOcQi1KaniKzgJ+bWVO8Zt2/OOciF9/tiizLObeyiEdJJwJ2AKXpKtkbmOacu9I5N9059zIx\nPYydc9ucc92AE4ELgOnAEOAaf/p651xH4CS8o/llwGNm9l+lyEPKmaCDfy9cuJD8/PwUZCSSPBde\neCHPPfcca9as4aqrrgo7nUCScUR6Ld6518horK8D7czs+yZMM/u/eJ1tXvOftwBwno+cc48DbwCN\nillHpMm3bjHTo70EfAXcjtfRaGZkgnNuN7AK6BWdn59TzdiexTFeBy43s9rRL8Y+j5LF0R2j6sTM\nG9kO+51z/8D7QvEd0Mjv0dvEn77HOfc6EDnrXtx2kgrANPi3VGAZGRn84he/YPTo0XzwwQe8/vrr\ndO3alcqVy8/FCGWR6ZlmNhivybE5XjPpH51zq/3pE/B6oi4zs4l4HXSGAQXA837MGL8Zdybe9aL/\nB2hDTOecKB/j9WJ90F/mGc65Im9x4Zw7ZGZz8Hrifo1XWKPdi9dr9y2/Y88W4Fy8I8jOQHF3J3oU\nuBl43cwe8fNpC5yJV7BjvQj0M7Mv8Xr6dga6x8Tkm9liYDFeE/CV/usv4fV2/tDMnsLrAFWI19t3\nO7CimBylgmjXrh1t2rRh+fLlJca99tprLF26lPbt26cmMZESZGZmUrVqVapWrUqNGjU47bTTOP30\n02nQoAFnnXUW2dnZnHPOOVSrVi3sVBNSFoV0N16Hot8Dh/GK4ff9k51zG83sl8D/AE8BB4BFwF3O\nuYN+2B/wCt2DeM2W64E7onqnHsE5t9fMbgbGA88Cn3P0+cZos/zlz3HOHTH+lHNuuZn9Cq+4jwMy\ngX8Dz+AV7CI5574ws4uAsXhfFgzvy0FxedwOTAUm4Z1Dfh4Y6a834gHgerxzoJX85V3hnPvAv1Z0\nNF5x7YG3Hd8CLnXOFXUOWiqY0aNH06pVq7hxQ4YM4Yorrki7wcul4kj367BTrSwK6RbnXIlff/1e\nusXeus859yLeEVtx0xfhFaro1/4C/CXmtUl4hSp2/ndi54+Z/iZQ6q/wzrkP8a4dLWraCLxeuZHn\n2/CavWMNj4p5DCjyTuT+F4AHKP68sVRwP//5z7nqqqtYsKCkPnDwr3/9i3nz5tG1a9cUZXZsgjZX\ni6S78nKvXREBRo0aFWjw7/vvvz/tB/9u3bo1rVu3DjsNkYSpkIqUIy1btqR799hT60f75JNPePrp\np1OQ0bHLz89XL2OpEBIqpM65XOdcTlklIyLxBR38+8EHH0zrwb8HDx7M4MGDw05DJGE6IhUpZxo3\nbhxoQOwvv/ySSZOO6jIgImVMhVSkHBo6dGigwb8ffvhhdu1K9E6bIlISFVKRcqhevXqBbq+3Y8cO\nxo0bl4KMRI5fKqQi5dQ999xDnTp14sZNmDCBr776Km6ciBwbFVKRcqpWrVrcd999ceP27NmTloN/\nT5gwgQkTJoSdhkjCVEhFyrHbb7890MgYU6dOZcOGDclPqBSys7PJzs4OOw2RhKmQipRjkcG/4zlw\n4ADDhg2LG5dKy5YtY9myZWGnIZIwFVKRcq5nz56BBv9+5plneP/991OQUTB5eXmBxloVSXcqpCLl\nXOXKlQOdAy0sLGTUqFEpyEjk+KJCKlIB/OY3v+G//iv++O6bN29OQTYixxcVUpEKoDSDf4tI2VIh\nFakgLr/8ctq0aRN2GiLHnbIYj1RE0kTQwb/TwRNPPBF2CiJlQkekIhVIZPDv8qBFixa0aNEi7DRE\nEqZCKlLBBB38O2wLFy5k4cKFYachkrD0/28TkVIJOvh32MaPH8/48ePDTkMkYSqkIhVQ0MG/RSRx\nKqQiFVDQwb9FJHEqpCIV1NChQ6lRo0bYaYhUeCqkIhVUvXr1uOuuu8JOQ6TCUyEVqcAGDhxI7dq1\nw06jSLNmzWLWrFlhpyGSMBVSkQrsxBNPZNCgQWGnUaSGDRvSsGHDsNMQSZg558LOQZIsJyfHrVq1\nKuw0RI4wd+5cALp27RpyJiJFM7N3nHM58eJ0i0ARCcWUKVMAFVIp/9S0KyIikgAVUhERkQSokIqI\niCRAhVRERCQB6mwkIqGYP39+2CmIlAkVUhEJRd26dcNOQaRMqGlXREIxY8YMZsyYEXYaIglTIRWR\nUKiQSkWhQioiIpIAFVIREZEEqJCKiIgkQIVUREQkAbr8RURCsWTJkrBTECkTKqQiEorq1auHnYJI\nmVDTroiEYvLkyUyePDnsNEQSpkIqIqGYN28e8+bNCzsNkYSpkIqIiCRAhVRERCQBKqQiIiIJUCEV\nERFJgDnnws5BkszMvgY+CzuPCqIusC3sJESKof2zbDVyzp0SL0iFVKQUzGyVcy4n7DxEiqL9Mxxq\n2hUREUmACqmIiEgCVEhFSmdq2AmIlED7Zwh0jlRERCQBOiIVERFJgAqppB0ze8TMNkQ9H2FmgZtO\nzOxqM+uVrHzKCzNr7G+7ejGvl2p7SvG0r5bMzDaY2Yyo57lmdl0Rcc7MRqQyt7KkQirlwUzgslLE\nXw2U2YdTOdYYGA7Ui3m9tNtTgtO+eqQbgbFRz3OBowop3jabmYqEkkHjkUrac86tB9aHnUdFoe2Z\nPNq2R3LOvREwblmyc0kq55weehT5AGYA/wJ6A2uB74C3gJ9GxbQBHHAVMBfYDXwYNT0XWOPPux4Y\nB1SPWc/dwAZgL7DKX8eGqOm3e7vqEfOcAywB/gNsB14GLgZG+PlEP2aUZT7FbKvG/rp6AdOBb4Ct\n/vIzYmI7Av8A9gEbgWlAnZiYXwPv+zHvA4uitwFwMvAqsAnY7/+cBZwek88Rj9jtCTQCCoEeMeuv\nDHwNjIh6rTXwN3+7bAH+CJwR9n6qffWY99X+/nb4Fu9uSHkxcZWAwf7y9/u5XBcTc7q/323y39+/\ngKFR01dF3pP/N4p9vyP8aduifn8K+LSIvO/y/2Y1/edVgNHA5/77XwP8LpT9L+x/AD3S9xG1478D\n3AR0Az4CPgUq+TGRD6c9QB7QHrjSn3Y38BVwD17x6AdsBqZFrWMQcBiv+edKP+bDkj6cgJ/4/7Qr\n8JqOrgf+hFfAmgIv4X2YtvUfZ5dlPsVsq8iH0yFgsj9vHl6Ruikqrov/wTUC6ATc4uf616iYq/z5\nngKuAfr4H1DR2+AU/+9zC9AB+B3eh+1r/vSqwAA/p99FtkUx23M5sDTm/fzan7ep/7wV3ofYeD+/\nG/G+DHwAZGlfLZf76n684nyl/3d1HLmvjsUr4sP8/XCqv192jop5GygAbvD3i/HAv6OmRxfSs4HV\nwGtR7zeyf0UX0sjf6YKYvN8GZkY9/4u/vN/62+gB4CDQPeX7X9j/AHqk78P/cPqAqCMqIMffyS/3\nn0d2+iti5q2F98F7Gd6HeuTRE6/YZAEn+B8ysd+EH4nz4TQHr2hkxsx3elTeq5KVTzHbKvLhdEvM\n628Dz/q/G949j38Xk8Ml/rzN/JhPgDkxyznqSKeIHG72l1Mn5m/z05KW5X8QHQTqRb32DLAi6vlr\nwMiYvJv5y79U+2q53Fe7xby+CHjT/70OXqG9JyZmPv5RvB/jgBtiYk6M+v37Quo/Xw7MLyKn6EIa\n+T95LGr6j/11Rb4MXgLswjv/H72NpgOvp3r/0zlSiWevc+5w5IlzbpWZ7cX7dvlSVNwXMfO1Amrg\nNWMVpTFes2JN4LlS5nQJ3jfTg9EvOuc2lTBPMvOJ9lXM801Aff/35sAZwJP+I1YzIAM4E++IpERm\ndjVwK/B/gdpANX9STbwmxKD+CEzC6wQyycyq4x1d3O2vpzpes+5FwNBi8n6lFOtLFu2rpfNtzPO/\n88Pf9wK8gv2XmJgXgM5mdgqwA6+Jf4iZGd4Xr8+dc7sSyAnnnDOz2cBvzewu/2/aDe/v9qofdhne\nF46tRSxicyLrPxYqpHIsvsVrWixJXf9nJ4oejWIj3hEDeMWmNOrg/QOXRjLzKclBfugdH8mhF/Be\nEbEf4X3oA3xZ0kLN7Ba8pt/XgQfxzhOdA4zB+0YfmHPuWzNbgNf0OAmviGYA8/yQk/3nwyn6w/3f\npVlfimlfDe5boJaZZeF9MQPvPHm0yPPazrmvzawj8D/AbMDM7N/Afc65eSRmFt752bZ4X4JuAGY7\n5wr96XXxtkNRPYAPFvFaUqmQSqmYWQbeThz7DxZrp//zW+fcymKWFVnGiXjfboPawQ9HeUVxKc4n\nqEgO+0vIIfLBWSvOsm7DO6/aLmre2P/norZDcWYBi8ysKd63/7845yL57oosq7i805H21VKr76/z\ngJlFllkvZvmRS6l2ADjn3gF+ZWYnAefhdQiaZWavOeeK+gIRaJ90zn1kZquAbv7/RAuOvDxmJ3Aq\n8E7s0X4YdB2plNa1eF/AXo0T9yZeR4WjmijNrIn/6wd4nSUujgmJV0ReB7qYWWbMciPfonfxw7f6\nVOQT1Id4Ryd3+h/y0TmcambV8M4N7QZ+GTNvtZjnWRw9xmydmOeRJrbYbVGUl/CapW8HLifqQ8s5\ntxvvXFcvM6sZk3dNMwuy/DBoXw3Ib5rtwg/b6i28I7trY0KvAT7yj0ZPjtzswzm30zn3V7zWkSzg\ntGJWVdT7Lc5Mf32/xTuP/GHUtL/56+lXxHtpEvtasumIVOI508wGA+/ineMbDvzRObe6pJmcczvM\nLA/IM7OFeJ0uvgMuxWumqe2c+9LM5gOPmFkVvCJzI9CZkgciHwmsBP5mZlOAA3g9Dw/g/dP9A69Y\njcTr1XfAOfeXJOYTiHOu0MzuwTv6+7uZPYn3zbo1XnPvWc65LWY2FbjPP78X6dHZN2ZxLwL9zOxf\neM267fB690b72F/+g2Y2Ee9SlUeLye2Qmc0B7sQ7gnspJuRevGbdt8xsAt62ORfvcpPOpMdg0tpX\nS7ev3mFmJ/i55OJ16LnJ3ybbzewxYIS//AK8ntzX8ENzaiPgNTObDOTjXY7SD6+n9Jpi1vkP4AEz\nuwuvWfor59zfiol9Dq/ZuDfepTrRluLtj4/4hXM53jnjzn4e7YNtgjKS6t5NepSfB16Pwi/8nXYX\nXnPO40DVqJg2FNEzNGp6D7yeq3vxrq3MB/pHTa+N1xNwL94R0aN4HXE2RMUUdW1eK7xvpXv8vF4E\nfuVPy8C7BGU73tHdg2WZTzHvs7G/HTrGvD4fWB7zWge8jh278c5L/RPvQ7+KP70a3vnPb/F6Zj6P\n94HybdQyqvsxO/EK38zIdgIaR8VdidcLeD/wSXHb03/9Z/78jxbzHlv5+8JOf7uvwbtEorb21XK5\nry4E1vn7xjtAm5i4Snidjz7DK7bvA9dHTa+F10v4XT/3LXjFL3r/i+21G+kgtcvfj3r7r3/fazcm\nh4X+uusWMa0K3hHwJ37MZmAx8OtU738a/UWK5d8j86fOuZx4sZJcZvY48DPnXOuwc0lH2leDM7PG\neB3EOjnnFoWbTcWgpl2RNGNmbYEL8Zp1dwK/wLvM5ZYw8xKRoqmQiqSnLnhNcJl4BbWPc+7ZUDMS\nkSKpaVdERCQBuvxFREQkASqkIiIiCVAhFRERSYAKqYiISAJUSEVERBKgQioiIpKA/w/n2OkjiKOP\nqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b693352710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_binary_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Наиболее часто встречающийся класс:\n",
      "[[403   0]\n",
      " [ 47   0]]\n",
      "\n",
      "Дамми-модель:\n",
      "[[368  35]\n",
      " [ 46   1]]\n",
      "\n",
      "Дерево решений:\n",
      "[[390  13]\n",
      " [ 24  23]]\n",
      "\n",
      "Логистическая регрессия\n",
      "[[401   2]\n",
      " [  8  39]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Наиболее часто встречающийся класс:\")\n",
    "print(confusion_matrix(y_test, pred_most_frequent))\n",
    "print(\"\\nДамми-модель:\")\n",
    "print(confusion_matrix(y_test, pred_dummy))\n",
    "print(\"\\nДерево решений:\")\n",
    "print(confusion_matrix(y_test, pred_tree))\n",
    "print(\"\\nЛогистическая регрессия\")\n",
    "print(confusion_matrix(y_test, pred_logreg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Связь с правильностью\n",
    "\\begin{equation}\n",
    "\\text{Accuracy} = \\frac{\\text{TP} + \\text{TN}}{\\text{TP} + \\text{TN} + \\text{FP} + \\text{FN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Точность, полнота и F-мера\n",
    "\\begin{equation}\n",
    "\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "source": [
    "\\begin{equation}\n",
    "\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "\\text{F} = 2 \\cdot \\frac{\\text{precision} \\cdot \\text{recall}}{\\text{precision} + \\text{recall}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1-мера наибольшая частота: 0.00\n",
      "f1-мера дамми: 0.02\n",
      "f1-мера дерево: 0.55\n",
      "f1-мера логистическая регрессия: 0.89\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(\"f1-мера наибольшая частота: {:.2f}\".format(\n",
    "        f1_score(y_test, pred_most_frequent)))\n",
    "print(\"f1-мера дамми: {:.2f}\".format(f1_score(y_test, pred_dummy)))\n",
    "print(\"f1-мера дерево: {:.2f}\".format(f1_score(y_test, pred_tree)))\n",
    "print(\"f1-мера логистическая регрессия: {:.2f}\".format(\n",
    "        f1_score(y_test, pred_logreg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " не-девятка       0.90      1.00      0.94       403\n",
      "    девятка       0.00      0.00      0.00        47\n",
      "\n",
      "avg / total       0.80      0.90      0.85       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred_most_frequent,\n",
    "                            target_names=[\"не-девятка\", \"девятка\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " не-девятка       0.89      0.91      0.90       403\n",
      "    девятка       0.03      0.02      0.02        47\n",
      "\n",
      "avg / total       0.80      0.82      0.81       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_dummy,\n",
    "                            target_names=[\"не-девятка\", \"девятка\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      " не-девятка       0.98      1.00      0.99       403\n",
      "    девятка       0.95      0.83      0.89        47\n",
      "\n",
      "avg / total       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_logreg,\n",
    "                            target_names=[\"не-девятка\", \"девятка\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Принимаем во внимание неопределенность"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from mglearn.datasets import make_blobs \n",
    "X, y = make_blobs(n_samples=(400, 50), centers=2, cluster_std=[7.0, 2],        \n",
    "                  random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)                                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OrkII0d54ik0/6TGaAY7YVEdi09khFa0WJhnxzj57SvhJU6ZSPeA6wgZeR3BM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W6zeVR74uJfYdP2fxFy8yhHM9Wut6FLHOqzFsSZSuTkIWd6VH34t/M1t5Hc5aFaal6O9+5Trpoa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\nCSpx9tN9bi0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b691995ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_decision_threshold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.89      0.93       104\n",
      "          1       0.35      0.67      0.46         9\n",
      "\n",
      "avg / total       0.92      0.88      0.89       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_lower_threshold = svc.decision_function(X_test) > -.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.82      0.90       104\n",
      "          1       0.32      1.00      0.49         9\n",
      "\n",
      "avg / total       0.95      0.83      0.87       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lower_threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Кривые точности-полноты и ROC-кривые"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b6935def98>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G/NJK9pdWsr+kin0lx9ldVMHuomNU19ldbQf06k5S3wh+OKIvSXER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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6934bbe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# используем больший объем данных, чтобы получить более гладкую кривую\n",
    "X, y = make_blobs(n_samples=(4000, 500), centers=2, cluster_std=[7.0, 2],\n",
    "                  random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))\n",
    "# находим ближайший к нулю порог\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"порог 0\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision, recall, label=\"кривая точности-полноты\")\n",
    "plt.xlabel(\"Точность\")\n",
    "plt.ylabel(\"Полнота\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b69370aef0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z93U2y+O9Llo2LCyMqVOnUlpaypQpUy5oXP7Hp1v59f0n0JYypk6dKje7iQaR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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b69366ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=0, max_features=2)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# в RandomForestClassifier есть predict_proba, но нет decision_function\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(\n",
    "    y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(precision, recall, label=\"svc\")\n",
    "\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"порог 0 svc\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision_rf, recall_rf, label=\"rf\")\n",
    "\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(precision_rf[close_default_rf], recall_rf[close_default_rf], '^', c='k',\n",
    "         markersize=10, label=\"порог 0.5 rf\", fillstyle=\"none\", mew=2)\n",
    "plt.xlabel(\"Точность\")\n",
    "plt.ylabel(\"Полнота\")\n",
    "plt.legend(loc=\"best\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1-мера random forest: 0.610\n",
      "f1-мера svc: 0.656\n"
     ]
    }
   ],
   "source": [
    "print(\"f1-мера random forest: {:.3f}\".format(\n",
    "    f1_score(y_test, rf.predict(X_test))))\n",
    "print(\"f1-мера svc: {:.3f}\".format(f1_score(y_test, svc.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя точность random forest: 0.660\n",
      "Средняя точность svc: 0.666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "ap_rf = average_precision_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "ap_svc = average_precision_score(y_test, svc.decision_function(X_test))\n",
    "print(\"Средняя точность random forest: {:.3f}\".format(ap_rf))\n",
    "print(\"Средняя точность svc: {:.3f}\".format(ap_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Рабочая характеристика приемника (ROC) и AUC\n",
    "\\begin{equation}\n",
    "\\text{FPR} = \\frac{\\text{FP}}{\\text{FP} + \\text{TN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b6932d0a20>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OPwH2t/X3NRHfX6nYYmg6LtTdjwONx4V2tkxP0mF93P2wu/+m8dfAu0T+h+qp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\n8f8BmVlbi9aMjkwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6934ceeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svc.decision_function(X_test))\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC-кривая\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (полнота)\")\n",
    "# находим пороговое значение, ближайшее к нулю\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"порог 0\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b6935a8668>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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99hhPPvlknXGXl5cTHh7OwYMHmTBhAmeccQbr1n2/dE3btm0pLi4mJyeHV155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\niwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b692e52320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC-кривая SVC\")\n",
    "plt.plot(fpr_rf, tpr_rf, label=\"ROC-кривая RF\")\n",
    "\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (полнота)\")\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"порог 0 SVC\", fillstyle=\"none\", c='k', mew=2)\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(fpr_rf[close_default_rf], tpr[close_default_rf], '^', markersize=10,\n",
    "         label=\"порог 0.5 RF\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC для случайного леса: 0.937\n",
      "AUC для SVC: 0.916\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "rf_auc = roc_auc_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "svc_auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "print(\"AUC для случайного леса: {:.3f}\".format(rf_auc))\n",
    "print(\"AUC для SVC: {:.3f}\".format(svc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma = 1.00 правильность = 0.90 AUC = 0.50\n",
      "gamma = 0.05 правильность = 0.90 AUC = 1.00\n",
      "gamma = 0.01 правильность = 0.90 AUC = 1.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b6934c5be0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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frbXPl+a2Y2Ji7OrVq7ljageoVINpQ1Z4e/yAlXQgg8fnuopznQuLc1HqRIhD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6935f7c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "for gamma in [1, 0.05, 0.01]:\n",
    "    svc = SVC(gamma=gamma).fit(X_train, y_train)\n",
    "    accuracy = svc.score(X_test, y_test)\n",
    "    auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "    fpr, tpr, _ = roc_curve(y_test , svc.decision_function(X_test))\n",
    "    print(\"gamma = {:.2f} правильность = {:.2f} AUC = {:.2f}\".format(\n",
    "    gamma, accuracy, auc))\n",
    "    plt.plot(fpr, tpr, label=\"gamma={:.3f}\".format(gamma))\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.xlim(-0.01, 1)\n",
    "plt.ylim(0, 1.02)\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Метрики для мультиклассовой классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Правильность: 0.953\n",
      "Матрица ошибок:\n",
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 39  0  0  0  0  2  0  2  0]\n",
      " [ 0  0 41  3  0  0  0  0  0  0]\n",
      " [ 0  0  1 43  0  0  0  0  0  1]\n",
      " [ 0  0  0  0 38  0  0  0  0  0]\n",
      " [ 0  1  0  0  0 47  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 52  0  0  0]\n",
      " [ 0  1  0  1  1  0  0 45  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  1  0  1  0  0  1 44]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target, random_state=0)\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "pred = lr.predict(X_test)\n",
    "print(\"Правильность: {:.3f}\".format(accuracy_score(y_test, pred)))\n",
    "print(\"Матрица ошибок:\\n{}\".format(confusion_matrix(y_test, pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D9PR0bdeunQKalpam06ZN0379+umgQYN06dKlWqdOnVIbLQnY+JhKZeBKl4CN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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b693260f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_image = mglearn.tools.heatmap(\n",
    "    confusion_matrix(y_test, pred), xlabel='Спрогнозированная метка класса',\n",
    "    ylabel='Фактическая метка класса', xticklabels=digits.target_names,\n",
    "    yticklabels=digits.target_names, cmap=plt.cm.gray_r, fmt=\"%d\")\n",
    "plt.title(\"Матрица ошибок\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        37\n",
      "          1       0.89      0.91      0.90        43\n",
      "          2       0.95      0.93      0.94        44\n",
      "          3       0.90      0.96      0.92        45\n",
      "          4       0.97      1.00      0.99        38\n",
      "          5       0.98      0.98      0.98        48\n",
      "          6       0.96      1.00      0.98        52\n",
      "          7       1.00      0.94      0.97        48\n",
      "          8       0.93      0.90      0.91        48\n",
      "          9       0.96      0.94      0.95        47\n",
      "\n",
      "avg / total       0.95      0.95      0.95       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Микро-усредненная f1-мера: 0.953\n",
      "Mакро-усредненная f1-мера: 0.954\n"
     ]
    }
   ],
   "source": [
    "print(\"Микро-усредненная f1-мера: {:.3f}\".format\n",
    "       (f1_score(y_test, pred, average=\"micro\")))\n",
    "print(\"Mакро-усредненная f1-мера: {:.3f}\".format\n",
    "        (f1_score(y_test, pred, average=\"macro\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Метрики регрессии"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Использование метрик оценки для отбора модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Метрика качества по умолчанию: [ 0.89983306  0.89983306  0.89983306]\n",
      "Метрика качества явно заданная правильность: [ 0.89983306  0.89983306  0.89983306]\n",
      "Метрика качества AUC: [ 0.99372294  0.98957947  0.99594929]\n"
     ]
    }
   ],
   "source": [
    "# метрика качества классификационной модели по умолчанию – правильность\n",
    "print(\"Метрика качества по умолчанию: {}\".format(\n",
    "    cross_val_score(SVC(), digits.data, digits.target == 9)))\n",
    "# значение параметра scoring=\"accuracy\" не меняет результатов\n",
    "explicit_accuracy = cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                                    scoring=\"accuracy\")\n",
    "print(\"Метрика качества явно заданная правильность: {}\".format(explicit_accuracy))\n",
    "roc_auc = cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                          scoring=\"roc_auc\")\n",
    "print(\"Метрика качества AUC: {}\".format(roc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Решетчатый поиск c использованием правильности\n",
      "Наилучшие параметры: {'gamma': 0.0001}\n",
      "Наилучшее значение перекр проверки (правильность)): 0.970\n",
      "AUC на тестовом наборе: 0.992\n",
      "Правильность на тестовом наборе: 0.973\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target == 9, random_state=0)\n",
    "\n",
    "# задаем не самую удачную сетку параметров для иллюстрации:\n",
    "param_grid = {'gamma': [0.0001, 0.01, 0.1, 1, 10]}\n",
    "# используем метрику по умолчанию, то есть правильность:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Решетчатый поиск c использованием правильности\")\n",
    "print(\"Наилучшие параметры:\", grid.best_params_)\n",
    "print(\"Наилучшее значение перекр проверки (правильность)): {:.3f}\".format(grid.best_score_))\n",
    "print(\"AUC на тестовом наборе: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Правильность на тестовом наборе: {:.3f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Решетчатый поиск с использованием AUC\n",
      "Наилучшие параметры: {'gamma': 0.01}\n",
      "Наилучшее значение перекр проверки (AUC): 0.997\n",
      "AUC на тестовом наборе: 1.000\n",
      "Правильность на тестовом наборе: 1.000\n"
     ]
    }
   ],
   "source": [
    "# используем метрику качества AUC:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, scoring=\"roc_auc\")\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"\\nРешетчатый поиск с использованием AUC\")\n",
    "print(\"Наилучшие параметры:\", grid.best_params_)\n",
    "print(\"Наилучшее значение перекр проверки (AUC): {:.3f}\".format(grid.best_score_))\n",
    "print(\"AUC на тестовом наборе: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Правильность на тестовом наборе: {:.3f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Доступные объекты scorer:\n",
      "['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'completeness_score', 'explained_variance', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'median_absolute_error', 'mutual_info_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'normalized_mutual_info_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'v_measure_score']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.scorer import SCORERS\n",
    "print(\"Доступные объекты scorer:\\n{}\".format(sorted(SCORERS.keys())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выводы и перспективы"
   ]
  }
 ],
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