diff --git a/03_classification.ipynb b/03_classification.ipynb
index 579c829..087766b 100644
--- a/03_classification.ipynb
+++ b/03_classification.ipynb
@@ -93,47 +93,14 @@
    },
    "outputs": [],
    "source": [
-    "from six.moves import urllib\n",
     "from sklearn.datasets import fetch_mldata\n",
-    "try:\n",
-    "    mnist = fetch_mldata('MNIST original')\n",
-    "except urllib.error.HTTPError as ex:\n",
-    "    print(\"Could not download MNIST data from mldata.org, trying alternative...\")\n",
-    "\n",
-    "    # Alternative method to load MNIST, if mldata.org is down\n",
-    "    from scipy.io import loadmat\n",
-    "    mnist_alternative_url = \"https://github.com/amplab/datascience-sp14/raw/master/lab7/mldata/mnist-original.mat\"\n",
-    "    mnist_path = \"./mnist-original.mat\"\n",
-    "    response = urllib.request.urlopen(mnist_alternative_url)\n",
-    "    with open(mnist_path, \"wb\") as f:\n",
-    "        content = response.read()\n",
-    "        f.write(content)\n",
-    "    mnist_raw = loadmat(mnist_path)\n",
-    "    mnist = {\n",
-    "        \"data\": mnist_raw[\"data\"].T,\n",
-    "        \"target\": mnist_raw[\"label\"][0],\n",
-    "        \"COL_NAMES\": [\"label\", \"data\"],\n",
-    "        \"DESCR\": \"mldata.org dataset: mnist-original\",\n",
-    "    }\n",
-    "    print(\"Success!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
+    "mnist = fetch_mldata('MNIST original')\n",
     "mnist"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -147,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -160,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -173,25 +140,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "some_digit = X[36000]\n",
+    "some_digit_image = some_digit.reshape(28, 28)\n",
+    "plt.imshow(some_digit_image, cmap = matplotlib.cm.binary,\n",
+    "           interpolation=\"nearest\")\n",
+    "plt.axis(\"off\")\n",
+    "\n",
+    "save_fig(\"some_digit_plot\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
    "source": [
     "def plot_digit(data):\n",
     "    image = data.reshape(28, 28)\n",
     "    plt.imshow(image, cmap = matplotlib.cm.binary,\n",
     "               interpolation=\"nearest\")\n",
-    "    plt.axis(\"off\")\n",
-    "\n",
-    "some_digit_index = 36000\n",
-    "some_digit = X[some_digit_index]\n",
-    "plot_digit(some_digit)\n",
-    "save_fig(\"some_digit_plot\")\n",
-    "plt.show()"
+    "    plt.axis(\"off\")"
    ]
   },
   {
@@ -218,13 +203,7 @@
     "        row_images.append(np.concatenate(rimages, axis=1))\n",
     "    image = np.concatenate(row_images, axis=0)\n",
     "    plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n",
-    "    plt.axis(\"off\")\n",
-    "\n",
-    "plt.figure(figsize=(9,9))\n",
-    "example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]\n",
-    "plot_digits(example_images, images_per_row=10)\n",
-    "save_fig(\"more_digits_plot\")\n",
-    "plt.show()"
+    "    plt.axis(\"off\")"
    ]
   },
   {
@@ -237,20 +216,24 @@
    },
    "outputs": [],
    "source": [
-    "y[some_digit_index]"
+    "plt.figure(figsize=(9,9))\n",
+    "example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]\n",
+    "plot_digits(example_images, images_per_row=10)\n",
+    "save_fig(\"more_digits_plot\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true,
+    "collapsed": false,
     "deletable": true,
     "editable": true
    },
    "outputs": [],
    "source": [
-    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
+    "y[36000]"
    ]
   },
   {
@@ -263,7 +246,22 @@
    },
    "outputs": [],
    "source": [
-    "shuffle_index = rnd.permutation(60000)\n",
+    "X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "shuffle_index = np.random.permutation(60000)\n",
     "X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]"
    ]
   },
@@ -279,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -293,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -309,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -322,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -336,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -364,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -382,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -396,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -411,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -426,7 +424,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "y_train_perfect_predictions = y_train_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "confusion_matrix(y_train_5, y_train_perfect_predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -441,7 +465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -454,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -467,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -480,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -494,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -507,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -521,7 +545,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "threshold = 0\n",
+    "y_some_digit_pred = (y_scores > threshold)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -529,14 +567,12 @@
    },
    "outputs": [],
    "source": [
-    "threshold = 0\n",
-    "y_some_digit_pred = (y_scores > threshold)\n",
     "y_some_digit_pred"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -551,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -559,12 +595,13 @@
    },
    "outputs": [],
    "source": [
-    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3, method=\"decision_function\")"
+    "y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,\n",
+    "                             method=\"decision_function\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -579,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -591,7 +628,7 @@
     "    plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
     "    plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
     "    plt.xlabel(\"Threshold\", fontsize=16)\n",
-    "    plt.legend(loc=\"center left\", fontsize=16)\n",
+    "    plt.legend(loc=\"upper left\", fontsize=16)\n",
     "    plt.ylim([0, 1])\n",
     "\n",
     "plt.figure(figsize=(8, 4))\n",
@@ -603,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -616,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -629,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 39,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -642,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -655,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -687,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 42,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -702,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 43,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -710,8 +747,8 @@
    },
    "outputs": [],
    "source": [
-    "def plot_roc_curve(fpr, tpr, **options):\n",
-    "    plt.plot(fpr, tpr, linewidth=2, **options)\n",
+    "def plot_roc_curve(fpr, tpr, label=None):\n",
+    "    plt.plot(fpr, tpr, linewidth=2, label=label)\n",
     "    plt.plot([0, 1], [0, 1], 'k--')\n",
     "    plt.axis([0, 1, 0, 1])\n",
     "    plt.xlabel('False Positive Rate', fontsize=16)\n",
@@ -725,7 +762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 44,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -740,7 +777,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "forest_clf = RandomForestClassifier(random_state=42)\n",
+    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
+    "                                    method=\"predict_proba\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
+    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -748,15 +811,9 @@
    },
    "outputs": [],
    "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "forest_clf = RandomForestClassifier(random_state=42)\n",
-    "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method=\"predict_proba\")\n",
-    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
-    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5, y_scores_forest)\n",
-    "\n",
     "plt.figure(figsize=(8, 6))\n",
     "plt.plot(fpr, tpr, \"b:\", linewidth=2, label=\"SGD\")\n",
-    "plot_roc_curve(fpr_forest, tpr_forest, label=\"Random Forest\")\n",
+    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\")\n",
     "plt.legend(loc=\"lower right\", fontsize=16)\n",
     "save_fig(\"roc_curve_comparison_plot\")\n",
     "plt.show()"
@@ -764,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -777,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -791,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -814,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -828,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -842,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -855,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -868,7 +925,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sgd_clf.classes_[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -884,7 +952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -897,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 63,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -911,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 64,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -924,7 +992,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 65,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -937,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 66,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -953,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 67,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -968,7 +1036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 68,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -981,8 +1049,17 @@
     "    fig = plt.figure(figsize=(8,8))\n",
     "    ax = fig.add_subplot(111)\n",
     "    cax = ax.matshow(conf_mx)\n",
-    "    fig.colorbar(cax)\n",
-    "\n",
+    "    fig.colorbar(cax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
     "plt.matshow(conf_mx, cmap=plt.cm.gray)\n",
     "save_fig(\"confusion_matrix_plot\", tight_layout=False)\n",
     "plt.show()"
@@ -990,7 +1067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 70,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -999,7 +1076,17 @@
    "outputs": [],
    "source": [
     "row_sums = conf_mx.sum(axis=1, keepdims=True)\n",
-    "norm_conf_mx = conf_mx / row_sums\n",
+    "norm_conf_mx = conf_mx / row_sums"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
     "np.fill_diagonal(norm_conf_mx, 0)\n",
     "plt.matshow(norm_conf_mx, cmap=plt.cm.gray)\n",
     "save_fig(\"confusion_matrix_errors_plot\", tight_layout=False)\n",
@@ -1008,7 +1095,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 72,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1023,14 +1110,10 @@
     "X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]\n",
     "\n",
     "plt.figure(figsize=(8,8))\n",
-    "plt.subplot(221)\n",
-    "plot_digits(X_aa[:25], images_per_row=5)\n",
-    "plt.subplot(222)\n",
-    "plot_digits(X_ab[:25], images_per_row=5)\n",
-    "plt.subplot(223)\n",
-    "plot_digits(X_ba[:25], images_per_row=5)\n",
-    "plt.subplot(224)\n",
-    "plot_digits(X_bb[:25], images_per_row=5)\n",
+    "plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)\n",
+    "plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)\n",
+    "plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)\n",
+    "plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)\n",
     "save_fig(\"error_analysis_digits_plot\")\n",
     "plt.show()"
    ]
@@ -1047,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 73,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1067,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 74,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1080,20 +1163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 77,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1101,6 +1171,7 @@
    },
    "outputs": [],
    "source": [
+    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3)\n",
     "f1_score(y_train, y_train_knn_pred, average=\"macro\")"
    ]
   },
@@ -1116,7 +1187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 78,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1134,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1151,20 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "knn_clf.fit(X_train_mod, y_train_mod)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 82,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1172,10 +1230,10 @@
    },
    "outputs": [],
    "source": [
+    "knn_clf.fit(X_train_mod, y_train_mod)\n",
     "clean_digit = knn_clf.predict([X_test_mod[some_index]])\n",
     "plot_digit(clean_digit)\n",
-    "save_fig(\"cleaned_digit_example_plot\")\n",
-    "plt.show()"
+    "save_fig(\"cleaned_digit_example_plot\")"
    ]
   },
   {
@@ -1435,7 +1493,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2+"
+   "version": "3.5.3"
   },
   "nav_menu": {},
   "toc": {