MNIST_LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Logistic Regression (MNIST) </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.\n",
    "<br>\n",
    "It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST database of handwritten digits is available on the following website: [MNIST Dataset](http://yann.lecun.com/exdb/mnist/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four Files are available on this site:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[train-images-idx3-ubyte.gz:  training set images (9912422 bytes)](http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz) \n",
    "<br>\n",
    "[train-labels-idx1-ubyte.gz:  training set labels (28881 bytes)](http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz)\n",
    "<br>\n",
    "[t10k-images-idx3-ubyte.gz:   test set images (1648877 bytes)](http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz) \n",
    "<br>\n",
    "[t10k-labels-idx1-ubyte.gz:   test set labels (4542 bytes)](http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Used for Confusion Matrix\n",
    "from sklearn import metrics\n",
    "import seaborn as sns\n",
    "\n",
    "# Used for Loading MNIST\n",
    "from struct import unpack\n",
    "\n",
    "import wget\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the data via command line (you can see this on the youtube video) or you can get them from the website or my github. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wget.download(\"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\")\n",
    "os.system (\"mv train-images-idx3-ubyte.gz data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "wget.download(\"http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\")\n",
    "os.system (\"mv train-labels-idx1-ubyte.gz data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !wget -O data/t10k-images-idx3-ubyte.gz http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
    "wget.download(\"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\")\n",
    "os.system (\"mv t10k-images-idx3-ubyte.gz data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !wget -O data/t10k-labels-idx1-ubyte.gz http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
    "wget.download(\"http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\")\n",
    "os.system (\"mv t10k-labels-idx1-ubyte.gz data/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>If you cant unzip the file, you can try gzip or download it from [my github](https://github.com/mGalarnyk/Python_Tutorials/tree/master/Sklearn/Logistic_Regression/data)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# decompress gzipped file\n",
    "#!info gzip\n",
    "os.system(\"gzip -d data/*.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadmnist(imagefile, labelfile):\n",
    "\n",
    "    # Open the images with gzip in read binary mode\n",
    "    images = open(imagefile, 'rb')\n",
    "    labels = open(labelfile, 'rb')\n",
    "\n",
    "    # Get metadata for images\n",
    "    images.read(4)  # skip the magic_number\n",
    "    number_of_images = images.read(4)\n",
    "    number_of_images = unpack('>I', number_of_images)[0]\n",
    "    rows = images.read(4)\n",
    "    rows = unpack('>I', rows)[0]\n",
    "    cols = images.read(4)\n",
    "    cols = unpack('>I', cols)[0]\n",
    "\n",
    "    # Get metadata for labels\n",
    "    labels.read(4)\n",
    "    N = labels.read(4)\n",
    "    N = unpack('>I', N)[0]\n",
    "\n",
    "    # Get data\n",
    "    x = np.zeros((N, rows*cols), dtype=np.uint8)  # Initialize numpy array\n",
    "    y = np.zeros(N, dtype=np.uint8)  # Initialize numpy array\n",
    "    for i in range(N):\n",
    "        for j in range(rows*cols):\n",
    "            tmp_pixel = images.read(1)  # Just a single byte\n",
    "            tmp_pixel = unpack('>B', tmp_pixel)[0]\n",
    "            x[i][j] = tmp_pixel\n",
    "        tmp_label = labels.read(1)\n",
    "        y[i] = unpack('>B', tmp_label)[0]\n",
    "\n",
    "    images.close()\n",
    "    labels.close()\n",
    "    return (x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img, train_lbl = loadmnist('data/train-images-idx3-ubyte'\n",
    "                                 , 'data/train-labels-idx1-ubyte')\n",
    "test_img, test_lbl = loadmnist('data/t10k-images-idx3-ubyte'\n",
    "                               , 'data/t10k-labels-idx1-ubyte')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(train_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_lbl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(test_img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(test_lbl.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Showing Training Digits and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdf2877710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for index, (image, label) in enumerate(zip(train_img[0:5], train_lbl[0:5])):\n",
    "    plt.subplot(1, 5, index + 1)\n",
    "    plt.imshow(np.reshape(image, (28,28)), cmap=plt.cm.gray)\n",
    "    plt.title('Training: %i\\n' % label, fontsize = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  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   0   0   0   0   0\n",
      "   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   0   0   0   0   0\n",
      "   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   0   0   0   0   0\n",
      "   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   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   3  18  18  18 126 136 175  26 166 255\n",
      " 247 127   0   0   0   0   0   0   0   0   0   0   0   0  30  36  94 154\n",
      " 170 253 253 253 253 253 225 172 253 242 195  64   0   0   0   0   0   0\n",
      "   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251  93  82\n",
      "  82  56  39   0   0   0   0   0   0   0   0   0   0   0   0  18 219 253\n",
      " 253 253 253 253 198 182 247 241   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0  14   1 154 253  90   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   0 139 253 190   2   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0  11 190 253  70   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  35 241\n",
      " 225 160 108   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0  81 240 253 253 119  25   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  45 186 253 253 150  27   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252 253 187\n",
      "   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   0 249 253 249  64   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
      " 253 207   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0  39 148 229 253 253 253 250 182   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253\n",
      " 253 201  78   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "   0   0  23  66 213 253 253 253 253 198  81   2   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0  18 171 219 253 253 253 253 195\n",
      "  80   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "  55 172 226 253 253 253 253 244 133  11   0   0   0   0   0   0   0   0\n",
      "   0   0   0   0   0   0   0   0   0   0 136 253 253 253 212 135 132  16\n",
      "   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   0   0   0   0   0\n",
      "   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   0   0   0   0   0\n",
      "   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]\n"
     ]
    }
   ],
   "source": [
    "# This is how the computer sees the number 5\n",
    "print(train_img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Logistic Regression on Entire Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Logistic Regression Sklearn Documentation](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) <br>\n",
    "One thing I like to mention is the importance of parameter tuning. While it may not have mattered much for the toy digits dataset, it can make a major difference on larger and more complex datasets you have. <b>Please see the parameter: solver</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 1: </b> Import the model you want to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In sklearn, all machine learning models are implemented as Python classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 2:</b> Make an instance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all parameters not specified are set to their defaults\n",
    "# default solver is incredibly slow thats why we change it\n",
    "logisticRegr = LogisticRegression(solver = 'lbfgs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3:</b> Training the model on the data, storing the information learned from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model is learning the relationship between x (digits) and y (labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='lbfgs', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logisticRegr.fit(train_img, train_lbl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4:</b> Predict the labels of new data (new images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uses the information the model learned during the model training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7], dtype=uint8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a NumPy Array\n",
    "# Predict for One Observation (image)\n",
    "logisticRegr.predict(test_img[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 6, 9], dtype=uint8)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict for Multiple Observations (images) at Once\n",
    "logisticRegr.predict(test_img[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measuring Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "accuracy (fraction of correct predictions): correct predictions / total number of data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically, how the model performs on new data (test set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9176\n"
     ]
    }
   ],
   "source": [
    "score = logisticRegr.score(test_img, test_lbl)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note: Seaborn needs to be installed for this portion </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda install seaborn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on test data\n",
    "predictions = logisticRegr.predict(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = metrics.confusion_matrix(test_lbl, predictions)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdf1fa57d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm_normalized, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {:.3f}'.format(score) \n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display Misclassified images with Predicted Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 0\n",
    "misclassifiedIndexes = []\n",
    "for label, predict in zip(test_lbl, predictions):\n",
    "    if label != predict: \n",
    "        misclassifiedIndexes.append(index)\n",
    "    index +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdee495c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for plotIndex, badIndex in enumerate(misclassifiedIndexes[0:5]):\n",
    "    plt.subplot(1, 5, plotIndex + 1)\n",
    "    plt.imshow(np.reshape(test_img[badIndex], (28,28)), cmap=plt.cm.gray)\n",
    "    plt.title('Predicted: {}, Actual: {}'.format(predictions[badIndex], test_lbl[badIndex]), fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking Performance Based on Training Set Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A confusion matrix is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = LogisticRegression(solver = 'lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efdea084690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (24,8));\n",
    "plt.tight_layout()\n",
    "\n",
    "for plotIndex, sample_size in enumerate([100, 1000, 60000]):\n",
    "    X_train = train_img[:sample_size].reshape(sample_size, 784)\n",
    "    y_train = train_lbl[:sample_size]\n",
    "    regr.fit(X_train, y_train)\n",
    "    predicted = regr.predict(test_img)\n",
    "    cm = metrics.confusion_matrix(test_lbl, predicted)\n",
    "    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    sns.heatmap(cm_normalized, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[plotIndex], cbar = False);\n",
    "    accuracyString = '{:g} Training Samples Score: {:.3f}'.format(sample_size, regr.score(test_img, test_lbl)) \n",
    "    axes[plotIndex].set_title(accuracyString, size = 25);\n",
    "\n",
    "axes[0].set_ylabel('Actual label', fontsize = 30);\n",
    "axes[1].set_xlabel('Predicted label', fontsize = 30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<b>if this tutorial doesn't cover what you are looking for, please leave a comment on the youtube video and I will try to cover what you are interested in. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[youtube video](https://www.youtube.com/watch?v=71iXeuKFcQM)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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