Facial Recognition (SVM).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facial recognition with support-vector machines (SVMs)\n",
    "\n",
    "[Support-vector machines](https://en.wikipedia.org/wiki/Support-vector_machine), or SVMs, often do better than other models at fitting data — especially data is that is highly non-linear. The diagram on the left below shows a set of data points that a conventional linear model will not fit. In other words, there is no line you can draw to separate the blue points from the green points. However, you can add a third dimension to each data point based on its distance from the center. In 3D space (below right), you can then draw a plane that cleanly separates the two classes. This is the essence of how SVMs work, although when they add dimensions to your data, they typically add more than one. In some cases, they add an infinite number of dimensions! And they use *kernel tricks* to  do the math without actually computing values for all those new dimensions.\n",
    "\n",
    "![](Images/svm.png)\n",
    "\n",
    "That's a simple way to visualize how support-vector machines work. Let's learn about SVM by using the [Labeled Faces in the Wild](http://vis-www.cs.umass.edu/lfw/) (LFW) dataset provided with Scikit-learn to build a facial-recognition model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset\n",
    "\n",
    "The first step is to import facial images from the dataset. LFW contains more than 13,000 facial images collected from the Web. Of the more than 5,000 people represented in the dataset, 1,680 have two or more facial images, while only five have 100 or more. We'll set the minimum number of faces per person to 100, which means that five sets of faces corresponding to five famous people will be imported. Each facial image is labeled with the name of the person to whom the face belongs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Colin Powell' 'Donald Rumsfeld' 'George W Bush' 'Gerhard Schroeder'\n",
      " 'Tony Blair']\n",
      "(1140, 62, 47)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_lfw_people\n",
    "\n",
    "faces = fetch_lfw_people(min_faces_per_person=100)\n",
    "print(faces.target_names)\n",
    "print(faces.images.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In total, 1,140 facial images were loaded. Each image measures 62 by 47 pixels for a total of 2,914 pixels per image. That means we're working with a dataset containing 2,914 feature columns. Use the following code to show the first 24 images in the dataset and the people to whom the faces belong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x720 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(3, 8, figsize=(18, 10))\n",
    "for i, axi in enumerate(ax.flat):\n",
    "    axi.imshow(faces.images[i], cmap='gist_gray')\n",
    "    axi.set(xticks=[], yticks=[], xlabel=faces.target_names[faces.target[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the balance in the dataset by generating a histogram showing how many facial images were imported for each person."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bpMfp7pZRegy6u2VemKWwAahluDf1o40Hjpd0D1W3TNv3o40WSTsCZwFTI2JXSa8CDo+If8tcWqtdJemnwAVp/xjg5xnraTlJfxMR35X08d6OR8SXW11TLhGxZe4ahqqW4c4Y7kcbRd8EPgGcDRARt0r6HlCrcI+IE9PF1denpgURcUnOmjLYPD2O2WAbDZL2BXaIiHMlbQ1sGRH35q6rL3Xvc3850BURT0naD3gVcF5EPJqzrhwk3RQRcyTdEhG7p7ZlEbFb5tLMspN0MjAb2CkidpT0UuAHEbFP5tL6VNcz94aLgdmSZlHNFXEp8D3gkKxV5fFw+mMXsH5e/gc3/pJy9Ohb3eAQbd63OlokTQLmArsAkxrtdbvInrwd2B24GSAi/iSprT/Z1D3cn0sTnb0DOCMizpB0S+6iMplP9QfuFZJWAvcCf5O3pNYZy32ro+h84PfAwcApwHuAO7JWlM/TERGSGic/m/f3gtzqfofqM5LeDbwPuCy1TchYTzZpWcQ3AR3AKyJi34i4L3NZWUjaV9LxaXtrSTNz15TJrIj4LPBERCwEDgX2ylxTLhdJOhuYLOmDVBfZv5m5po2q+5n78cDfAf87Iu5N/4jPz1xTS/U1IkKqRn3VaWQEbNi3CpwLTAS+C7Rt3+ooeiY9PippV+DPVDd51U5EfFHSQcBjVL8b/xIRV2Yua6NqHe4RcTvw4ab9e4HT8lWURaM7YidgDt0Lq7wNWJylorzGXN/qKFogaSvgs1S/F1sA/5K3pDwkzQWui4hP5K5loGod7pLupZeLaBGxfYZysoiIzwFIug7YIyIeT/v/ClyesbRcxlzf6miJiG+lzWuB2vyb6MN2wNmSZlBNV3Id8MuIWJazqI2pdbhTffxumAQcBbw4Uy25TQWebtp/OrXVTc++1RNo877VkdZXV11D3brqACLiZABJmwEfpLon5CtUq821pVqHe0Ss6dH0FUlLqedHz/OAxZIaN+wcCSzMV05rSdo0Ip4ai32ro6Cu3VB9kvTPVNddtgBuAf4R+GXWovpR95uY9mja3YTqTP7vI+LVmUrKKr0fjTszr4uI2gwLlXRzROwh6fyIeG/ueqy9SLoZeJaqq/Ja4DcR0dYzydb6zB34UtP2s8B9wNF5SmkLLwAeS7dXd0ia2c63V4+wiZL+J/C63uZ2r9HUx42bl44BHgF+RNUF8QbgD8DnI+LhjOVlkf7wv5Dq7P0gqovNqyJi38yl9anWZ+7WbSzeXj2S0rwh76H6476ox+FaTX0s6SKqYZCbA1sBv6MK+X2B3SKidnMzpaGgrwfeSPXv5AGqC6pt24Vb23CX9EbgkTRB1tF0n5l8vd0/bo0GSctIQwCb5pa5tW4zZEqaGxHn5K4jp8Y6B5LGU8299JKmY7+tY7elpMuo+th/CdwUEc/085LsatktI+lrVJOETZJ0J9VFkiuoPnJ9m+oMrm48BLDymKQtI+LxdBFtD6quiNpcfyCNmkpTc/ypx7F1GerJLiIOkzQR2BHYSdKd7R7wtQx3YP+I2Dn1La4EtomIdWkI3K2Za8ul9kMAk89GxA9SN82bgC8A36Bet913Svoq1aRpjW3S/rR8ZeWTPumfR3VdTsB0ScdFxHVZC9uIuob7kwAR8aSk+yNiXdoPSW3913i0eAjgeo0z00Op5nK/XFKt5rSnuoDasKTHsZ77dfFl4M0RcSesX9zmAuA1WavaiLqG+zbpRg01bZP2O/KVld2twKZp+7c5C8loZfoEcxBwmqRNqdkEe2mSMNvQhEawA0TEXZLaepLBWl5QTSND+tS4Jb9O0kXlLwC/oPoj93rgExHxw5x1tZqkFwBvAZZHxN1pgexXRsTPMpdmGUn6NvAc1SRyUF2XG9fOo6hqGe72fJJ+CxwUEavSfgfw85qOjNgKmE7TJ9uIuDlfRZZb+gQ3n2o4KFSjZtp6ZF1du2Xs+TZpBHuyhpp1RwBI+jzwfqphsY0znwAOyFVTLpKm9DJFR61I2gb4NDALWA68PyIey1vVwPjM3QCQ9AWq4aEXpKZjgFsj4lP5qmq9NDT2lRHxdL9PLpyku4FlVPPa/yRqGBaSrqB7FsjDgC0i4vi8VQ2Mw91QtTJHJ9V87us/dkbEJX2/qkySLqaaX2hVv08uXPq9eBPVsNg5wEXAdyLirqyFtVDPm7YacxDlrGmgahnuktYANwLXA78GboyIv+atKi9JyyPilbnryE3SbKqF0n8HrO9PjYjDsxXVBiTtT3UxcXOqkVQnRcRv8lY1+tK1qP2oBhkAXNO8HxF/yVLYANQ13F8I7A28Ln29hmpB6OuB6yPioozlZSFpIXBmRNyUu5acJN0GnE3Vv/pcoz0irs1WVCaSplAtkv5e4CHgHKp5d3ajmneo+LVlJd1H9XugXg5HOy/sU8tw7yndan888FFgZkS07QT8o0XS76kuGt0PPEH1yxw1nFvmpoiYk7uOdiDpLqo1hc+NiK4exz4VEXVbknJMqWW4pxkPG2ftjX/IS4EbqOZpvj9XbblIellv7XV7LyR9mao7ZhEbdsvUbiikJNXxImop6hruz1EtgHw61cfL2o+MAJA0jmppvebx3X/MV1HrSbqml+aIiDoOhdyRasWhGWz4O1G792Isqmu4vxZ4LdWZ+0yqyYB+k76WtPONCaNF0oeAk6n6Vht9zbXrlrFu6WLiN6g+1a6fDTIilmYrygasluHeU1rR/G3AR4DOiJiUt6LWk7QC2Ms3rajXxRci4pRW15KbpKUR0bYTY7XaWPtkW9s7VCW9gu5+932AyVR97t/IWFZODwBrcxfRBp5o2p5EdePKHZlqye1Hkv4BuIQNrz+07fC/0dLXJ1uqG//aUi3P3CU9DPyJqhvmeuDXEbEib1V5NM2IuQvVVL+Xs+E/5C/nqKtdpDlFfhoR++WupdUk9bZ+blsP/xstY/GTbV3P3F8eET5LrWyZHv+YviamL6u8gOru3dqpwzj2QRhzn2xrGe4O9m51nN54YyQtp3vCsHFU8/t/Pl9F+aT5yv+ean1hqKaDPrvdl5cbJfcAv5A0Zj7Z1jLc7fkkXQkcFRGPpv2tgAsj4uCshbXeYU3bz1L1sW7ax3NLdxYwAfh62n9vavtAtoryGXOfbGvZ527PJ2lZROzWo+2WiNg9U0ktJ2kasC3VbJhPp+leP0o1zetLsxbXQpLGp8WxN5g0Kx17Xpu1p9rN191M0lRJ50j6SdrfWdLc3HVlsk7Sdo2ddMdqbf7yS/oo1fS2ZwA3SPoA1SiZzWjjdTJHyeL0uE7SyxuNkranabx7HUj6Snr8kaRFPb8yl7dRde+W+Q7VXNWfSft3Ad+nmiCpbj4D/ErStXQvszcvb0ktNQ/YKSL+kv7I3QXsU9MbdhqTZP0jcI2ke9L+DKo5mOrk/PT4xaxVDEGtu2Uak0Q1dz/01j1RF5K2ppotM6imQX44c0kt03Oe7jp3P0jqAhoXCjejurAM1Vn7f7XzRUTrVvcz9yfStKYBIGlvxthwp+FK3S+PRsTaiHhY0hPAkcBOks6s0bw7nZK+2rS/bfN+RHw4Q025jAO24PnT3I6ne+hsLfQYPbXBIdp8eo66n7nvQdXHuivV4gwdwLsi4tashbWQpBuBt0fEnyTtBvwc+D9Ud949ExG1GBkh6biNHY+Iha2qJbextNrQaOtrttSGdp41tdbhDtXIAKo7MwXcWbcxvJJubZx9SPoi8FxEfFLSJsCydj4zsdFRt1FSAyVpKt1ThC9u96UYa90tI+kdPZp2lLQWWN7u/+NGUPNH7wOAfwKIiOeqJTSthg7MXUC7kXQ08AWqG7kEnCHpExHxw6yFbUStwx2YSzX1b2MO7/2opjedKemUiDi/rxcW5GpJFwEPAlsBVwNI2haoS3+7NanjxGAD8BlgTuOkT1IHVRemw71NjQf+R0Q8BOs/dp0H7AVcR/cwqJJ9FDiG6uadfZu6pV5C9xBRs7rbpMen+TW0+X1CdQ/36Y1gT1altr9IqkXfe1pG7cJe2m/JUE42ks5gIzdt1Wy0jD3fFZJ+ClyQ9o8BfpKxnn7VPdx/Ieky4Adp/52pbXPg0WxVWQ5L0uM+wM5UN7MBHAXcnqUiaxsR8Yl0jW7f1LQgIi7JWVN/PFpGeifd/8OuBy72osD1JekGqu6pZ9P+BOCXEbF33sosB0mzgKkRcX2P9n2BByPiD3kq619tz9zTklm3RcQrgItz12NtYyvghUDjouIWqc3q6SukEWQ9rE3H3tbKYgajtuEeEesk3Slpu3ZeB3G0beQOPABqOM79VOAWSddQDXl7A/CvWSuynKZGxPKejRGxPK293LZqG+7JVsBtkhbTtHZmRByer6SWa8xfPj89NkYIvSdDLdlFxLlpltC9UtOnIuLPOWuyrCZv5NhmrSpiKGrd5y7pjb21R8S1ra4lt97uSqzTbehpKoo+RcTNrarF2oekC4CrI+KbPdo/ABwUEcfkqax/tQ53GHu3FI8WScuA+Y0LR5JeB3y9LjNkpm6YvkREHNCyYqxtpHy4hOqGvsb0z7OpVmN6ezt/qqt1uPdyS/Hrgba+pXi0pDPXc4EXpaZHgRN8xmoGkvanmmAQqoEYV+esZyDqHu6/pfpotcEtxXWbxzuNHPpwRJwu6UVQ70XEJe1KNdZ9UqMtIs7LV5HZ4LX17bMtMOZuKR4NEbEOeHfaXlvzYD+ZahroM4D9gf8L1OkCuxWi7qNlxtwtxaPoeklnUt2Z2TxyqG7dMu8CXg3cEhHHpz7X72auyWzQat0tA+un/W3cofrLdr+leLT0cUGxdhcSJS2OiD0lLaU6c38cuCPd7GY2ZtT9zB2qK+CPRcTPJb1A0pYR8XjuolotIvbPXUObWCJpMvBNqt+N/wR+k7UisyGo9Zm7pA9SrXr/4oh4uaQdgG9ERC0XK5B0KLALG15IPCVfRXmlOxBfWKdlF60ctbt42MN8qlkAHwOIiLuBbbJWlImkb1Bdc/gQ1bDQo4CNrh9ZKknT0jj/7YDJkt6Quyazwap7t8xTEfF0Yzm5tJ5qXT/KvC4iXpXWVP2cpC9Rw4vLkk6j+iN3O7AuNQfV4i1mY0bdw/1aSZ8GNpN0EPAPwI8y15TLf6XHv0p6KdWw0G0z1pPLkcBOEfFU7kLMhqPu3TInAauB5cDfAj8G/jlrRflcli4kfgG4GbiP7iGidXIPMCF3EWbDVesLqtY7SZsCk+p4M5Oki6nGuV8FrD979zJ7NtbUsltG0hFAZ0R8Le3fCHSkw5+KiB/0+eKCpYuIM0i/F5LqeNv9ovRlNqbV8sxd0vXAsRHxQNpfBhwIbA6cW8ehkJLOB14OLKPpQmIdz1glTQR2TLt3RkQtFku3stTyzB2Y2Aj25FcRsQZYkxbHrqPZwM51Xz9W0n7AQqprDgKmSzouIjxaxsaUuob7BmtiRsSJTbsd1NPvgJcAD+YuJLMvAW+OiDsBJO1IdWH5NVmrMhukuob7jZI+2MvqKn8LLM5UUxaSfkQ1jntL4Pa05GDzhcS6zYg4oRHsABFxlySPnrExp6597tsA/58qxBqzHr4G2BQ4MiIeylRayzUtNRhU3RAbqNuSg5K+DTxH90yQ7wHGRcQJ+aoyG7xahnuDpAOo5lKBMbK6ymhIi3Xc5pkP1w8DnU/TTKFUyw36piYbU2od7tZN0qXAhyLij7lryS2tyEVErM5di9lQ1bXP3Z5vK+C21OfevFhHLfrcVU0wdDJwIunObUnrgDPqPDOmjV0Od2v4bO4CMvsY1QyhcyLiXgBJ2wNnSfpYRJyetTqzQXK3jK0n6WXADo2FS6guJNZi4RJJt1Atlv5wj/YO4GcRsXueysyGpu4Th1mSFi75IXB2appGNaKoLib0DHZY3+/uoZA25jjcraHuC5c8PcRjZm3Jfe7WUPeFS14t6bFe2kXTsoNmY4XD3RpqvXBJRIzLXYPZSPIFVQNA0ibAXODNVGerPwW+VfeJxMzGKoe7mVmBfEG15iQdIWl+0/6Nku5JX0flrM3Mhs7hbp9kw5WHNgXmAPsBf5ejIDMbPl9QNS9cYlYgn7mbFy4xK5DD3W5Md6duoI4Ll5iVxKNlas4Ll5iVyeFugBcuMSuNw93MrEDuczczK5DD3cysQA53M7MCOdzNzArkcDczK9B/A3sZjuPci6zNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "counts = Counter(faces.target)\n",
    "names = {}\n",
    "\n",
    "for key in counts.keys():\n",
    "    names[faces.target_names[key]] = counts[key]\n",
    "\n",
    "df = pd.DataFrame.from_dict(names, orient='index')\n",
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are far more images of George W. Bush than of anyone else in the dataset. Classification models are best trained with balanced datasets. Use the following code to reduce the dataset to 100 images of each person."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2914)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = np.zeros(faces.target.shape, dtype=np.bool)\n",
    "\n",
    "for target in np.unique(faces.target):\n",
    "    mask[np.where(faces.target == target)[0][:100]] = 1\n",
    "    \n",
    "x = faces.data[mask]\n",
    "y = faces.target[mask]\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`x` contains 500 facial images, and `y` contains the labels that go with them: 0 for Colin Powell, 1 for Donald Rumsfeld, and so on. Now let's see if an SVM can make sense of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use an SVM to classify facial images\n",
    "\n",
    "The next task is to train an SVM model to do image classification using the faces in the dataset. We'll train three different models: one that uses a linear kernel, one that uses a polynomial kernel, and one that uses an RBF kernel. In each case, we'll use [`GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) to optimize hyperparameters. Start with a linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
      "[CV] END ..............................................C=0.1; total time=   0.0s\n",
      "[CV] END ..............................................C=0.1; total time=   0.0s\n",
      "[CV] END ..............................................C=0.1; total time=   0.0s\n",
      "[CV] END ..............................................C=0.1; total time=   0.0s\n",
      "[CV] END ..............................................C=0.1; total time=   0.0s\n",
      "[CV] END ................................................C=1; total time=   0.0s\n",
      "[CV] END ................................................C=1; total time=   0.0s\n",
      "[CV] END ................................................C=1; total time=   0.0s\n",
      "[CV] END ................................................C=1; total time=   0.0s\n",
      "[CV] END ................................................C=1; total time=   0.0s\n",
      "[CV] END ...............................................C=10; total time=   0.0s\n",
      "[CV] END ...............................................C=10; total time=   0.0s\n",
      "[CV] END ...............................................C=10; total time=   0.0s\n",
      "[CV] END ...............................................C=10; total time=   0.0s\n",
      "[CV] END ...............................................C=10; total time=   0.0s\n",
      "[CV] END ..............................................C=100; total time=   0.0s\n",
      "[CV] END ..............................................C=100; total time=   0.0s\n",
      "[CV] END ..............................................C=100; total time=   0.0s\n",
      "[CV] END ..............................................C=100; total time=   0.0s\n",
      "[CV] END ..............................................C=100; total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.844"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "svc = SVC(kernel='linear')\n",
    "\n",
    "grid = {\n",
    "    'C': [0.1, 1, 10, 100]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=svc, param_grid=grid, cv=5, verbose=2)\n",
    "grid_search.fit(x, y) # Train the model with different parameters\n",
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model achieved a cross-validated accuracy of 84.4%. It's possible that accuracy can be improved by standardizing the image data. Run the same grid search again, but this time use [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) to apply unit variance to all the pixel values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
      "[CV] END .........................................svc__C=0.1; total time=   0.0s\n",
      "[CV] END .........................................svc__C=0.1; total time=   0.0s\n",
      "[CV] END .........................................svc__C=0.1; total time=   0.0s\n",
      "[CV] END .........................................svc__C=0.1; total time=   0.0s\n",
      "[CV] END .........................................svc__C=0.1; total time=   0.0s\n",
      "[CV] END ...........................................svc__C=1; total time=   0.0s\n",
      "[CV] END ...........................................svc__C=1; total time=   0.0s\n",
      "[CV] END ...........................................svc__C=1; total time=   0.0s\n",
      "[CV] END ...........................................svc__C=1; total time=   0.0s\n",
      "[CV] END ...........................................svc__C=1; total time=   0.0s\n",
      "[CV] END ..........................................svc__C=10; total time=   0.1s\n",
      "[CV] END ..........................................svc__C=10; total time=   0.1s\n",
      "[CV] END ..........................................svc__C=10; total time=   0.1s\n",
      "[CV] END ..........................................svc__C=10; total time=   0.1s\n",
      "[CV] END ..........................................svc__C=10; total time=   0.1s\n",
      "[CV] END .........................................svc__C=100; total time=   0.1s\n",
      "[CV] END .........................................svc__C=100; total time=   0.0s\n",
      "[CV] END .........................................svc__C=100; total time=   0.1s\n",
      "[CV] END .........................................svc__C=100; total time=   0.1s\n",
      "[CV] END .........................................svc__C=100; total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8560000000000001"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "svc = SVC(kernel='linear')\n",
    "pipe = make_pipeline(scaler, svc)\n",
    "\n",
    "grid = {\n",
    "    'svc__C': [0.1, 1, 10, 100]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=pipe, param_grid=grid, cv=5, verbose=2)\n",
    "grid_search.fit(x, y)\n",
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardizing the data produced an incremental improvement in accuracy. What value of `C` produced that accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svc__C': 0.1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is it possible that a polynomial kernel could outperform a linear kernel? There's an easy way to find out. Note the introduction of the `gamma` and `degree` parameters to the parameter grid. These parameters, along with `C`, can greatly influence a polynomial kernel's ability to fit to the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=1, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ............svc__C=0.1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ............svc__C=0.1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ............svc__C=0.1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ............svc__C=0.1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=0.1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=0.1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=0.1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=1, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ............svc__C=1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ............svc__C=1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ............svc__C=1, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END ..............svc__C=1, svc__degree=1, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ..............svc__C=1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ..............svc__C=1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ..............svc__C=1, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ............svc__C=1, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ...........svc__C=1, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..............svc__C=1, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=1, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ...........svc__C=10, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ...........svc__C=10, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ...........svc__C=10, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END ..........svc__C=10, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .............svc__C=10, svc__degree=1, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END .............svc__C=10, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END .............svc__C=10, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END .............svc__C=10, svc__degree=1, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ...........svc__C=10, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ..........svc__C=10, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .............svc__C=10, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.01; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.25; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=1, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=100, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=100, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END ..........svc__C=100, svc__degree=1, svc__gamma=0.5; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=1, svc__gamma=0.75; total time=   0.0s\n",
      "[CV] END ............svc__C=100, svc__degree=1, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ............svc__C=100, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ............svc__C=100, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END ............svc__C=100, svc__degree=1, svc__gamma=1; total time=   0.0s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=2, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=2, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=2, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=3, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=3, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=3, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=4, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=4, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=4, svc__gamma=1; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.25; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END ..........svc__C=100, svc__degree=5, svc__gamma=0.5; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END .........svc__C=100, svc__degree=5, svc__gamma=0.75; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=5, svc__gamma=1; total time=   0.1s\n",
      "[CV] END ............svc__C=100, svc__degree=5, svc__gamma=1; total time=   0.1s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8560000000000001"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svc = SVC(kernel='poly')\n",
    "pipe = make_pipeline(scaler, svc)\n",
    "\n",
    "grid = {\n",
    "    'svc__C': [0.1, 1, 10, 100],\n",
    "    'svc__gamma': [0.01, 0.25, 0.5, 0.75, 1],\n",
    "    'svc__degree': [1, 2, 3, 4, 5]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=pipe, param_grid=grid, cv=5, verbose=2)\n",
    "grid_search.fit(x, y)\n",
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The polynomial kernel achieved the same accuracy as the linear kernel. What parameter values led to this result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svc__C': 0.1, 'svc__degree': 1, 'svc__gamma': 0.25}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`best_params_` reveals that the optimum value of `degree` was 1, which means the polynomial kernel acted like a linear kernel. It's not surprising, then, that it achieved the same accuracy. Could an RBF kernel do better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=0.1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=0.1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ..........................svc__C=1, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ...........................svc__C=1, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.01; total time=   0.1s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END .........................svc__C=10, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ..........................svc__C=10, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.01; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.25; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=0.5; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END ........................svc__C=100, svc__gamma=0.75; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=1.0; total time=   0.2s\n",
      "[CV] END .........................svc__C=100, svc__gamma=1.0; total time=   0.2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.51"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svc = SVC(kernel='rbf')\n",
    "pipe = make_pipeline(scaler, svc)\n",
    "\n",
    "grid = {\n",
    "    'svc__C': [0.1, 1, 10, 100],\n",
    "    'svc__gamma': [0.01, 0.25, 0.5, 0.75, 1.0]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=pipe, param_grid=grid, cv=5, verbose=2)\n",
    "grid_search.fit(x, y)\n",
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RBF kernel didn't perform as well as the linear and polynomial kernels. There's a lesson here. The RBF kernel *often* fits to non-linear data better than other kernels, but it doesn't *always* fit better. That's why the best strategy with an SVM is to try different kernels with different parameter values. The best combination will vary from dataset to dataset. For the LFW dataset, it seems that a linear kernel is best. That's convenient, because the linear kernel is the fastest of all the kernels that Scikit provides."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the model's performance\n",
    "\n",
    "Confusion matrices are a great way to visual a model's accuracy. Let's split the dataset, train an optimized linear model with 80% of the images, test it with the remaining 20%, and show the results in a confusion matrix. The first step is to split the dataset. Note the `stratify=y` parameter, which ensures that the training dataset and the test dataset have the same proportion of samples of each class as the original dataset. In this example, the training dataset will contain 20 samples of each of the five people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, stratify=y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now train a linear SVM with the optimum `C` value revealed by the grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('svc', SVC(C=0.1, kernel='linear'))])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svc = SVC(kernel='linear', C=0.1)\n",
    "pipe = make_pipeline(scaler, svc)\n",
    "pipe.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross-validate the model to confirm that it's as accurate when trained with 80% of the dataset as it was when it was trained with the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8560000000000001"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cross_val_score(pipe, x, y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a confusion matrix to see how the model performs against the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x192d330a580>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(pipe, x_test, y_test, display_labels=faces.target_names, cmap='Blues', xticks_rotation='vertical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model correctly identified Colin Powell 19 times out of 20, Donald Rumsfeld 20 times out of 20, and so on. That's not bad. And it's a great example of support-vector machines at work. It would be challenging, perhaps impossible, to do this well using more conventional learning algorithms such as logistic regression."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}