diff --git a/03_classification.ipynb b/03_classification.ipynb
index 846af67..ba65a7e 100644
--- a/03_classification.ipynb
+++ b/03_classification.ipynb
@@ -39,7 +39,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures."
+    "This project requires Python 3.8 or above:"
    ]
   },
   {
@@ -48,20 +48,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Python ≥3.8 is required\n",
     "import sys\n",
-    "assert sys.version_info >= (3, 8)\n",
     "\n",
-    "# Scikit-Learn ≥1.0.1 is required\n",
-    "import sklearn\n",
-    "assert sklearn.__version__ >= \"1.0.1\""
+    "assert sys.version_info >= (3, 8)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# MNIST"
+    "It also requires Scikit-Learn ≥ 1.0.1:"
    ]
   },
   {
@@ -70,9 +66,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.datasets import fetch_openml\n",
+    "import sklearn\n",
     "\n",
-    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)"
+    "assert sklearn.__version__ >= \"1.0.1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like in the previous chapter, let's define the default font sizes to make the figures prettier:"
    ]
   },
   {
@@ -81,8 +84,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Not in the book\n",
-    "print(mnist.DESCR)"
+    "import matplotlib as mpl\n",
+    "\n",
+    "mpl.rc('font', size=12)\n",
+    "mpl.rc('axes', labelsize=14, titlesize=14)\n",
+    "mpl.rc('legend', fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And let's create the `images/classification` folder (if it doesn't already exist), and define the `save_fig()` function which is used through this notebook to save the figures in high-res for the book:"
    ]
   },
   {
@@ -91,78 +104,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Not in the book\n",
-    "mnist.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X, y = mnist.data, mnist.target\n",
-    "X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mnist.target"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "28 * 28"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell is not shown in the book. It's just here to define the default font size for the figures, and to define the `save_fig()` function which is used to save the figures in high-resolution for the book, just like we did in the previous chapter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# To plot pretty figures\n",
     "from pathlib import Path\n",
-    "import matplotlib as mpl\n",
     "\n",
-    "mpl.rc('font', size=12)\n",
-    "mpl.rc('axes', labelsize=14, titlesize=14)\n",
-    "mpl.rc('legend', fontsize=14)\n",
-    "\n",
-    "# To save the figures in high-res for the book\n",
     "IMAGES_PATH = Path() / \"images\" / \"classification\"\n",
     "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
     "\n",
@@ -173,11 +116,95 @@
     "    plt.savefig(path, format=fig_extension, dpi=resolution)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MNIST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "mnist = fetch_openml('mnist_784', version=1, as_frame=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# not in the book\n",
+    "print(mnist.DESCR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# not in the book\n",
+    "mnist.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y = mnist.data, mnist.target\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "28 * 28"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -188,13 +215,13 @@
     "\n",
     "some_digit = X[0]\n",
     "plot_digit(some_digit)\n",
-    "save_fig(\"some_digit_plot\")\n",
+    "save_fig(\"some_digit_plot\")  # not in the book\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,12 +337,12 @@
     "    clone_clf.fit(X_train_folds, y_train_folds)\n",
     "    y_pred = clone_clf.predict(X_test_fold)\n",
     "    n_correct = sum(y_pred == y_test_fold)\n",
-    "    print(n_correct / len(y_pred))  # prints 0.95035, 0.96035 and 0.9604"
+    "    print(n_correct / len(y_pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,12 +350,12 @@
     "\n",
     "dummy_clf = DummyClassifier()\n",
     "dummy_clf.fit(X_train, y_train_5)\n",
-    "print(any(dummy_clf.predict(X_train)))  # prints False: no 5s detected"
+    "print(any(dummy_clf.predict(X_train)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,35 +410,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import precision_score, recall_score\n",
     "\n",
-    "precision_score(y_train_5, y_train_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Not in the book\n",
-    "cm = confusion_matrix(y_train_5, y_train_pred)\n",
-    "\n",
-    "# Precision = TP / (FP + TP)\n",
-    "cm[1, 1] / (cm[0, 1] + cm[1, 1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "recall_score(y_train_5, y_train_pred)"
+    "precision_score(y_train_5, y_train_pred)  # == 3530 / (687 + 3530)"
    ]
   },
   {
@@ -420,14 +425,38 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Not in the book\n",
+    "# not in the book\n",
+    "\n",
+    "cm = confusion_matrix(y_train_5, y_train_pred)\n",
+    "\n",
+    "# Precision = TP / (FP + TP)\n",
+    "cm[1, 1] / (cm[0, 1] + cm[1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recall_score(y_train_5, y_train_pred)  # == 3530 / (1891 + 3530)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# not in the book\n",
+    "\n",
     "# Recall = TP / (FN + TP)\n",
     "cm[1, 1] / (cm[1, 0] + cm[1, 1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -454,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -474,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -483,18 +512,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Not in the book\n",
+    "# not in the book\n",
     "# Using threshold 0, we get exactly the same predictions as with predict()\n",
     "(y_train_pred == (y_scores > 0)).all()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -505,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -515,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,7 +570,7 @@
     "plt.plot(thresholds[idx], precisions[idx], \"bo\")\n",
     "plt.plot(thresholds[idx], recalls[idx], \"go\")\n",
     "plt.axis([-50000, 50000, 0, 1])\n",
-    "plt.grid(True)\n",
+    "plt.grid()\n",
     "plt.xlabel(\"Threshold\")\n",
     "plt.legend(loc=\"center right\")\n",
     "save_fig(\"precision_recall_vs_threshold_plot\")\n",
@@ -551,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -575,7 +604,7 @@
     "plt.xlabel(\"Recall\")\n",
     "plt.ylabel(\"Precision\")\n",
     "plt.axis([0, 1, 0, 1])\n",
-    "plt.grid(True)\n",
+    "plt.grid()\n",
     "plt.legend(loc=\"lower left\")\n",
     "save_fig(\"precision_vs_recall_plot\")\n",
     "\n",
@@ -584,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -613,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -630,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -641,14 +670,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
     "idx_for_threshold_at_90 = (thresholds <= threshold_for_90_precision).argmax()\n",
     "tpr_90, fpr_90 = tpr[idx_for_threshold_at_90], fpr[idx_for_threshold_at_90]\n",
     "\n",
-    "plt.figure(figsize=(6, 5))  # Not in the book\n",
+    "plt.figure(figsize=(6, 5))  # not in the book\n",
     "plt.plot(fpr, tpr, linewidth=2, label=\"ROC curve\")\n",
     "plt.plot([0, 1], [0, 1], 'k:', label=\"Random classifier's ROC curve\")\n",
     "plt.plot([fpr_90], [tpr_90], \"ko\", label=\"Threshold for 90% precision\")\n",
@@ -662,7 +691,7 @@
     "plt.text(0.12, 0.71, \"Higher\\nthreshold\", fontsize=14, color=\"#333333\")\n",
     "plt.xlabel('False Positive Rate (Fall-Out)')\n",
     "plt.ylabel('True Positive Rate (Recall)')\n",
-    "plt.grid(True)\n",
+    "plt.grid()\n",
     "plt.axis([0, 1, 0, 1])\n",
     "plt.legend(loc=\"lower right\")\n",
     "save_fig(\"roc_curve_plot\")\n",
@@ -672,7 +701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -690,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -701,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -711,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -738,7 +767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -749,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -762,7 +791,7 @@
     "# not in the book\n",
     "plt.xlabel('False Positive Rate (Fall-Out)')\n",
     "plt.ylabel('True Positive Rate (Recall)')\n",
-    "plt.grid(True)\n",
+    "plt.grid()\n",
     "plt.axis([0, 1, 0, 1])\n",
     "plt.legend(loc=\"lower right\")\n",
     "save_fig(\"roc_curve_comparison_plot\")\n",
@@ -772,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -788,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -798,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -821,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -833,7 +862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -842,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -852,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -862,7 +891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -871,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -887,11 +916,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Not in the book\n",
+    "# not in the book\n",
     "svm_clf.decision_function_shape = \"ovo\"\n",
     "some_digit_scores_ovo = svm_clf.decision_function([some_digit])\n",
     "some_digit_scores_ovo.round(2)"
@@ -899,20 +928,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.multiclass import OneVsRestClassifier\n",
     "\n",
     "ovr_clf = OneVsRestClassifier(SVC(random_state=42))\n",
-    "ovr_clf.fit(X_train[:2000], y_train[:2000])\n",
+    "ovr_clf.fit(X_train[:2000], y_train[:2000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "ovr_clf.predict([some_digit])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -921,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -932,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -948,7 +985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -957,7 +994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -984,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -997,7 +1034,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1008,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1028,11 +1065,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Not in the book\n",
+    "# not in the book\n",
     "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(9, 8))\n",
     "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0, 0])\n",
     "axs[0, 0].set_title(\"Confusion matrix\")\n",
@@ -1047,13 +1084,13 @@
     "                                        sample_weight=sample_weight,\n",
     "                                        normalize=\"pred\", values_format=\".0%\")\n",
     "axs[1, 1].set_title(\"Errors normalized by column\")\n",
-    "save_fig(\"confusion_matrix_plot\")\n",
+    "save_fig(\"confusion_matrix_plot\")  # not in the book\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1066,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1114,8 +1151,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
+   "execution_count": 78,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1131,7 +1170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1147,25 +1186,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
+    "y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)\n",
     "f1_score(y_multilabel, y_train_knn_pred, average=\"macro\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1175,14 +1206,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.multioutput import ClassifierChain\n",
     "\n",
     "chain_clf = ClassifierChain(SVC(), cv=3, random_state=42)\n",
-    "chain_clf.fit(X_train[:2000], y_multilabel[:2000])\n",
+    "chain_clf.fit(X_train[:2000], y_multilabel[:2000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "chain_clf.predict([some_digit])"
    ]
   },
@@ -1195,11 +1234,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.random.seed(42)  # as always, this is to make this example reproducible\n",
+    "np.random.seed(42)  # to make this code example reproducible\n",
     "noise = np.random.randint(0, 100, (len(X_train), 784))\n",
     "X_train_mod = X_train + noise\n",
     "noise = np.random.randint(0, 100, (len(X_test), 784))\n",
@@ -1210,7 +1249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1224,14 +1263,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 86,
    "metadata": {},
    "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",
+    "save_fig(\"cleaned_digit_example_plot\")  # not in the book\n",
     "plt.show()"
    ]
   },
@@ -1244,7 +1283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1295,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1321,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1336,7 +1375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1345,7 +1384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1361,7 +1400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1400,7 +1439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1409,7 +1448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1428,7 +1467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1461,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1486,7 +1525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1504,7 +1543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1513,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1529,7 +1568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1545,7 +1584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 101,
    "metadata": {
     "tags": []
    },
@@ -1585,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1609,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1632,7 +1671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1672,7 +1711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1689,7 +1728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1698,7 +1737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1728,7 +1767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1753,7 +1792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1769,7 +1808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1778,7 +1817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1787,7 +1826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1810,7 +1849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1832,7 +1871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1841,7 +1880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1861,7 +1900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1885,7 +1924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1902,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1918,7 +1957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1935,7 +1974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1952,7 +1991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1976,7 +2015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2003,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2037,7 +2076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2047,7 +2086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2081,7 +2120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2108,7 +2147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2124,7 +2163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2134,7 +2173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2143,7 +2182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2159,7 +2198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2173,7 +2212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2190,7 +2229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2199,7 +2238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2234,7 +2273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2250,7 +2289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2259,7 +2298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2282,7 +2321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2299,7 +2338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2315,7 +2354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2338,7 +2377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2362,7 +2401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2381,7 +2420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2397,7 +2436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2421,7 +2460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2437,7 +2476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2458,7 +2497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2480,7 +2519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2501,7 +2540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2554,7 +2593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2579,7 +2618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2612,7 +2651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2623,7 +2662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2639,7 +2678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2655,7 +2694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2671,7 +2710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2694,7 +2733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [