diff --git a/03_classification.ipynb b/03_classification.ipynb
index 499d157..846af67 100644
--- a/03_classification.ipynb
+++ b/03_classification.ipynb
@@ -82,7 +82,7 @@
    "outputs": [],
    "source": [
     "# Not in the book\n",
-    "mnist.keys()"
+    "print(mnist.DESCR)"
    ]
   },
   {
@@ -91,7 +91,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(mnist.DESCR)"
+    "# Not in the book\n",
+    "mnist.keys()"
    ]
   },
   {
@@ -100,7 +101,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mnist.data"
+    "X, y = mnist.data, mnist.target\n",
+    "X"
    ]
   },
   {
@@ -109,7 +111,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mnist.target"
+    "X.shape"
    ]
   },
   {
@@ -118,10 +120,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "\n",
-    "X, y = mnist.data, mnist.target\n",
-    "X.shape"
+    "mnist.target"
    ]
   },
   {
@@ -180,17 +179,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib as mpl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "def plot_digit(image_data):\n",
     "    image = image_data.reshape(28, 28)\n",
-    "    plt.imshow(image, cmap=mpl.cm.binary, interpolation=\"nearest\")\n",
+    "    plt.imshow(image, cmap=\"binary\")\n",
     "    plt.axis(\"off\")\n",
     "\n",
     "some_digit = X[0]\n",
     "plot_digit(some_digit)\n",
-    "\n",
     "save_fig(\"some_digit_plot\")\n",
     "plt.show()"
    ]
@@ -210,6 +207,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# not in the book\n",
     "plt.figure(figsize=(9, 9))\n",
     "for idx, image_data in enumerate(X[:100]):\n",
     "    plt.subplot(10, 10, idx + 1)\n",
@@ -241,7 +239,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_train_5 = (y_train == '5')\n",
+    "y_train_5 = (y_train == '5')  # True for all 5s, False for all other digits\n",
     "y_test_5 = (y_test == '5')"
    ]
   },
@@ -266,17 +264,6 @@
     "sgd_clf.predict([some_digit])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import cross_val_score\n",
-    "\n",
-    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -291,6 +278,17 @@
     "## Measuring Accuracy Using Cross-Validation"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring=\"accuracy\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -298,20 +296,21 @@
    "outputs": [],
    "source": [
     "from sklearn.model_selection import StratifiedKFold\n",
+    "from sklearn.base import clone\n",
     "\n",
-    "skfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
-    "\n",
+    "skfolds = StratifiedKFold(n_splits=3)  # add shuffle=True is the dataset is not\n",
+    "                                       # already shuffled\n",
     "for train_index, test_index in skfolds.split(X_train, y_train_5):\n",
+    "    clone_clf = clone(sgd_clf)\n",
     "    X_train_folds = X_train[train_index]\n",
     "    y_train_folds = y_train_5[train_index]\n",
     "    X_test_fold = X_train[test_index]\n",
     "    y_test_fold = y_train_5[test_index]\n",
     "\n",
-    "    sgd_clf_cv = SGDClassifier(random_state=42)\n",
-    "    sgd_clf_cv.fit(X_train_folds, y_train_folds)\n",
-    "    y_pred = sgd_clf_cv.predict(X_test_fold)\n",
+    "    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))"
+    "    print(n_correct / len(y_pred))  # prints 0.95035, 0.96035 and 0.9604"
    ]
   },
   {
@@ -324,7 +323,7 @@
     "\n",
     "dummy_clf = DummyClassifier()\n",
     "dummy_clf.fit(X_train, y_train_5)\n",
-    "np.any(dummy_clf.predict(X_train))"
+    "print(any(dummy_clf.predict(X_train)))  # prints False: no 5s detected"
    ]
   },
   {
@@ -399,7 +398,10 @@
    "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])"
    ]
   },
@@ -418,6 +420,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Not in the book\n",
+    "# Recall = TP / (FN + TP)\n",
     "cm[1, 1] / (cm[1, 0] + cm[1, 1])"
    ]
   },
@@ -483,14 +487,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "threshold = 8000\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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshold = 3000\n",
     "y_some_digit_pred = (y_scores > threshold)\n",
     "y_some_digit_pred"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -511,21 +526,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "recall_90_precision = recalls[np.argmax(precisions >= 0.90)]\n",
-    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]\n",
-    "\n",
     "plt.figure(figsize=(8, 4))  # not in the book\n",
     "plt.plot(thresholds, precisions[:-1], \"b--\", label=\"Precision\", linewidth=2)\n",
     "plt.plot(thresholds, recalls[:-1], \"g-\", label=\"Recall\", linewidth=2)\n",
-    "plt.vlines(threshold_90_precision, 0, 1.0, \"k\", \"dotted\", label=\"threshold\")\n",
+    "plt.vlines(threshold, 0, 1.0, \"k\", \"dotted\", label=\"threshold\")\n",
     "\n",
-    "# not in the book (just beautifies the figure)\n",
-    "plt.plot(threshold_90_precision, recall_90_precision, \"go\")\n",
-    "plt.plot(threshold_90_precision, 0.90, \"bo\")\n",
+    "# not in the book\n",
+    "# beautify the figure\n",
+    "idx = (thresholds >= threshold).argmax()  # first index ≥ threshold\n",
+    "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.xlabel(\"Threshold\")\n",
@@ -535,33 +549,34 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(y_train_pred == (y_scores > 0)).all()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.figure(figsize=(8, 6))  # not in the book\n",
+    "import matplotlib.patches as patches  # not in the book\n",
     "\n",
-    "plt.plot(recalls, precisions, linewidth=2)\n",
-    "plt.plot([recall_90_precision, recall_90_precision], [0., 0.9], \"k:\")\n",
-    "plt.plot([0.0, recall_90_precision], [0.9, 0.9], \"k:\")\n",
-    "plt.plot([recall_90_precision], [0.9], \"ko\")\n",
+    "plt.figure(figsize=(6, 5))  # not in the book\n",
+    "\n",
+    "plt.plot(recalls, precisions, linewidth=2, label=\"Precision/Recall curve\")\n",
     "\n",
     "# not in the book (just beautifies the figure)\n",
+    "plt.plot([recalls[idx], recalls[idx]], [0., precisions[idx]], \"k:\")\n",
+    "plt.plot([0.0, recalls[idx]], [precisions[idx], precisions[idx]], \"k:\")\n",
+    "plt.plot([recalls[idx]], [precisions[idx]], \"ko\",\n",
+    "         label=\"Point at threshold 3,000\")\n",
+    "plt.gca().add_patch(patches.FancyArrowPatch(\n",
+    "    (0.79, 0.60), (0.61, 0.78),\n",
+    "    connectionstyle=\"arc3,rad=.2\",\n",
+    "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
+    "    color=\"#444444\"))\n",
+    "plt.text(0.56, 0.62, \"Higher\\nthreshold\", fontsize=14, color=\"#333333\")\n",
     "plt.xlabel(\"Recall\")\n",
     "plt.ylabel(\"Precision\")\n",
     "plt.axis([0, 1, 0, 1])\n",
     "plt.grid(True)\n",
+    "plt.legend(loc=\"lower left\")\n",
     "save_fig(\"precision_vs_recall_plot\")\n",
     "\n",
     "plt.show()"
@@ -573,7 +588,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)]"
+    "idx_for_90_precision = (precisions >= 0.90).argmax()\n",
+    "threshold_for_90_precision = thresholds[idx_for_90_precision]\n",
+    "threshold_for_90_precision"
    ]
   },
   {
@@ -582,7 +599,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "threshold_90_precision"
+    "y_train_pred_90 = (y_scores >= threshold_for_90_precision)"
    ]
   },
   {
@@ -591,7 +608,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_train_pred_90 = (y_scores >= threshold_90_precision)"
+    "precision_score(y_train_5, y_train_pred_90)"
    ]
   },
   {
@@ -600,16 +617,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "precision_score(y_train_5, y_train_pred_90)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "recall_score(y_train_5, y_train_pred_90)"
+    "recall_at_90_precision = recall_score(y_train_5, y_train_pred_90)\n",
+    "recall_at_90_precision"
    ]
   },
   {
@@ -621,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -632,29 +641,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_roc_curve(fpr, tpr, label=None):\n",
-    "    plt.plot(fpr, tpr, linewidth=2, label=label)  # ROC curve\n",
-    "    plt.plot([0, 1], [0, 1], 'k--')  # dashed diagonal\n",
+    "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",
-    "    # not in the book (just beautifies the figure)\n",
-    "    plt.axis([0, 1, 0, 1])\n",
-    "    plt.xlabel('False Positive Rate (Fall-Out)')\n",
-    "    plt.ylabel('True Positive Rate (Recall)')\n",
-    "    plt.grid(True)\n",
-    "\n",
-    "\n",
-    "plt.figure(figsize=(8, 6))  # Not in the book\n",
-    "plot_roc_curve(fpr, tpr)\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",
     "\n",
     "# not in the book (just beautifies the figure)\n",
-    "fpr_90 = fpr[np.argmax(tpr >= recall_90_precision)]\n",
-    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")\n",
-    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")\n",
-    "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n",
+    "plt.gca().add_patch(patches.FancyArrowPatch(\n",
+    "    (0.20, 0.89), (0.07, 0.70),\n",
+    "    connectionstyle=\"arc3,rad=.4\",\n",
+    "    arrowstyle=\"Simple, tail_width=1.5, head_width=8, head_length=10\",\n",
+    "    color=\"#444444\"))\n",
+    "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.axis([0, 1, 0, 1])\n",
+    "plt.legend(loc=\"lower right\")\n",
     "save_fig(\"roc_curve_plot\")\n",
     "\n",
     "plt.show()"
@@ -662,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -671,15 +681,30 @@
     "roc_auc_score(y_train_5, y_scores)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** the following cell may take a few minutes to run."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.ensemble import RandomForestClassifier\n",
     "\n",
-    "forest_clf = RandomForestClassifier(random_state=42)\n",
+    "forest_clf = RandomForestClassifier(random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,\n",
     "                                    method=\"predict_proba\")"
    ]
@@ -690,7 +715,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_probas_forest.shape"
+    "y_probas_forest[:2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are _estimated probabilities_. Among the images that the model classified as positive with a probability between 50% and 60%, there are actually about 94% positive images:"
    ]
   },
   {
@@ -699,8 +731,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_scores_forest = y_probas_forest[:, 1]  # 2nd column = proba of positive class\n",
-    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)"
+    "# Not in the code\n",
+    "idx_50_to_60 = (y_probas_forest[:, 1] > 0.50) & (y_probas_forest[:, 1] < 0.60)\n",
+    "print(f\"{(y_train_5[idx_50_to_60]).sum() / idx_50_to_60.sum():.1%}\")"
    ]
   },
   {
@@ -709,21 +742,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
-    "recall_for_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)]\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, \"Random Forest\")\n",
-    "plt.plot([fpr_90, fpr_90], [0., recall_90_precision], \"r:\")\n",
-    "plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], \"r:\")\n",
-    "plt.plot([fpr_90], [recall_90_precision], \"ro\")\n",
-    "plt.plot([fpr_90, fpr_90], [0., recall_for_forest], \"r:\")\n",
-    "plt.plot([fpr_90], [recall_for_forest], \"ro\")\n",
-    "plt.grid(True)\n",
-    "plt.legend(loc=\"lower right\")\n",
-    "save_fig(\"roc_curve_comparison_plot\")\n",
-    "plt.show()"
+    "y_scores_forest = y_probas_forest[:, 1]\n",
+    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,\n",
+    "                                                      y_scores_forest)"
    ]
   },
   {
@@ -732,7 +753,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "roc_auc_score(y_train_5, y_scores_forest)"
+    "plt.figure(figsize=(6, 5))  # not in the book\n",
+    "\n",
+    "plt.plot(fpr_forest, tpr_forest, \"b-\", linewidth=2, label=\"Random Forest\")\n",
+    "plt.plot(fpr, tpr, \"--\", linewidth=2, label=\"SGD\")\n",
+    "plt.plot([0, 1], [0, 1], 'k:', label=\"Random classifier\")\n",
+    "\n",
+    "# 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.axis([0, 1, 0, 1])\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "save_fig(\"roc_curve_comparison_plot\")\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -741,8 +776,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)\n",
-    "precision_score(y_train_5, y_train_pred_forest)"
+    "roc_auc_score(y_train_5, y_scores_forest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could use `cross_val_predict(forest_clf, X_train, y_train_5, cv=3)` to compute `y_train_pred_forest`, but since we already have the estimated probabilities, we can just use the default threshold of 50% probability to get the same predictions much faster:"
    ]
   },
   {
@@ -750,6 +791,16 @@
    "execution_count": 54,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "y_train_pred_forest = y_probas_forest[:, 1] > 0.5\n",
+    "precision_score(y_train_5, y_train_pred_forest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "recall_score(y_train_5, y_train_pred_forest)"
    ]
@@ -770,25 +821,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.svm import SVC\n",
     "\n",
-    "svm_clf = SVC(gamma=\"auto\", random_state=42)\n",
-    "svm_clf.fit(X_train[:2000], y_train[:2000])  # y_train, not y_train_5\n",
-    "svm_clf.predict([some_digit])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
-    "np.round(some_digit_scores, 2)"
+    "svm_clf = SVC(random_state=42)\n",
+    "svm_clf.fit(X_train[:2000], y_train[:2000])  # y_train, not y_train_5"
    ]
   },
   {
@@ -797,10 +837,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Not in the book\n",
-    "svm_clf.decision_function_shape = \"ovo\"\n",
-    "some_digit_scores_ovo = svm_clf.decision_function([some_digit])\n",
-    "np.round(some_digit_scores_ovo, 2)"
+    "svm_clf.predict([some_digit])"
    ]
   },
   {
@@ -809,8 +846,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class_id = np.argmax(some_digit_scores)\n",
-    "class_id"
+    "some_digit_scores = svm_clf.decision_function([some_digit])\n",
+    "some_digit_scores.round(2)"
    ]
   },
   {
@@ -819,7 +856,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "svm_clf.classes_"
+    "class_id = some_digit_scores.argmax()\n",
+    "class_id"
    ]
   },
   {
@@ -828,7 +866,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "svm_clf.classes_[class_id]"
+    "svm_clf.classes_"
    ]
   },
   {
@@ -837,11 +875,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.multiclass import OneVsRestClassifier\n",
-    "\n",
-    "ovr_clf = OneVsRestClassifier(SVC(gamma=\"auto\", random_state=42))\n",
-    "ovr_clf.fit(X_train[:2000], y_train[:2000])\n",
-    "ovr_clf.predict([some_digit])"
+    "svm_clf.classes_[class_id]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want `decision_function()` to return all 45 scores, you can set the `decision_function_shape` hyperparameter to `\"ovo\"`. The default value is `\"ovr\"`, but don't let this confuse you: `SVC` always uses OvO for training. This hyperparameter only affects whether or not the 45 scores get aggregated or not:"
    ]
   },
   {
@@ -850,7 +891,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(ovr_clf.estimators_)"
+    "# 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)"
    ]
   },
   {
@@ -859,9 +903,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sgd_clf = SGDClassifier(random_state=42)\n",
-    "sgd_clf.fit(X_train[:2000], y_train[:2000])\n",
-    "sgd_clf.predict([some_digit])"
+    "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.predict([some_digit])"
    ]
   },
   {
@@ -870,7 +916,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.round(sgd_clf.decision_function([some_digit]))"
+    "len(ovr_clf.estimators_)"
    ]
   },
   {
@@ -879,8 +925,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cross_val_score(sgd_clf, X_train[:2000], y_train[:2000],\n",
-    "                cv=3, scoring=\"accuracy\")"
+    "sgd_clf = SGDClassifier(random_state=42)\n",
+    "sgd_clf.fit(X_train, y_train)\n",
+    "sgd_clf.predict([some_digit])"
    ]
   },
   {
@@ -888,13 +935,37 @@
    "execution_count": 66,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "sgd_clf.decision_function([some_digit]).round()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning:** the following two cells make take a few minutes each to run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring=\"accuracy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from sklearn.preprocessing import StandardScaler\n",
     "\n",
     "scaler = StandardScaler()\n",
-    "X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))\n",
-    "cross_val_score(sgd_clf, X_train_scaled[:2000], y_train[:2000],\n",
-    "                cv=3, scoring=\"accuracy\")"
+    "X_train_scaled = scaler.fit_transform(X_train.astype(\"float64\"))\n",
+    "cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring=\"accuracy\")"
    ]
   },
   {
@@ -913,38 +984,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import ConfusionMatrixDisplay\n",
     "\n",
+    "y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)\n",
     "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred)\n",
-    "save_fig(\"confusion_matrix_plot\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "error_idx = y_train_pred != y_train\n",
-    "y_train_pred_error = y_train_pred[error_idx]\n",
-    "y_train_error = y_train[error_idx]\n",
-    "ConfusionMatrixDisplay.from_predictions(y_train_error, y_train_pred_error,\n",
-    "                                        normalize=\"pred\", values_format=\".0%\")\n",
-    "save_fig(\"confusion_matrix_errors_plot\", tight_layout=False)\n",
     "plt.show()"
    ]
   },
@@ -953,6 +1000,62 @@
    "execution_count": 70,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
+    "                                        normalize=\"true\", values_format=\".0%\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_weight = (y_train_pred != y_train)\n",
+    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred,\n",
+    "                                        sample_weight=sample_weight,\n",
+    "                                        normalize=\"true\", values_format=\".0%\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's put all plots in a single figure for the book:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 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",
+    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[0, 1],\n",
+    "                                        normalize=\"true\", values_format=\".0%\")\n",
+    "axs[0, 1].set_title(\"CM normalized by row\")\n",
+    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1, 0],\n",
+    "                                        sample_weight=sample_weight,\n",
+    "                                        normalize=\"true\", values_format=\".0%\")\n",
+    "axs[1, 0].set_title(\"Errors normalized by row\")\n",
+    "ConfusionMatrixDisplay.from_predictions(y_train, y_train_pred, ax=axs[1, 1],\n",
+    "                                        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",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cl_a, cl_b = '3', '5'\n",
     "X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]\n",
@@ -963,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1011,14 +1114,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "\n",
     "y_train_large = (y_train >= '7')\n",
-    "y_train_odd = (y_train.astype(np.uint8) % 2 == 1)\n",
+    "y_train_odd = (y_train.astype('int8') % 2 == 1)\n",
     "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
     "\n",
     "knn_clf = KNeighborsClassifier()\n",
@@ -1027,7 +1131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1043,14 +1147,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "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,
    "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# not in the book\n",
+    "f1_score(y_multilabel, y_train_knn_pred, average=\"weighted\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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.predict([some_digit])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1060,11 +1195,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.random.seed(42)\n",
+    "np.random.seed(42)  # as always, this is to make this 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",
@@ -1075,10 +1210,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# not in the book\n",
     "some_index = 0\n",
     "plt.subplot(121); plot_digit(X_test_mod[some_index])\n",
     "plt.subplot(122); plot_digit(y_test_mod[some_index])\n",
@@ -1088,7 +1224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1099,6 +1235,36 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extra Material — Calibrating Estimated Probabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1129,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1155,7 +1321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1170,7 +1336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1179,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1195,7 +1361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1234,7 +1400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1243,7 +1409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1262,7 +1428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1295,7 +1461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1320,7 +1486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1338,7 +1504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1347,7 +1513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1363,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1379,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 98,
    "metadata": {
     "tags": []
    },
@@ -1407,14 +1573,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Exercise: _Tackle the Titanic dataset. A great place to start is on [Kaggle](https://www.kaggle.com/c/titanic)._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The goal is to predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and so on."
+    "Exercise: _Tackle the Titanic dataset. A great place to start is on [Kaggle](https://www.kaggle.com/c/titanic). Alternatively, you can download the data from https://homl.info/titanic.tgz and unzip this tarball like you did for the housing data in Chapter 2. This will give you two CSV files: _train.csv_ and _test.csv_ which you can load using `pandas.read_csv()`. The goal is to train a classifier that can predict the `Survived` column based on the other columns._"
    ]
   },
   {
@@ -1426,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1450,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1473,7 +1632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1497,6 +1656,13 @@
     "* **Embarked**: where the passenger embarked the Titanic"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The goal is to predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and so on."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1506,7 +1672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1523,7 +1689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1532,7 +1698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1562,7 +1728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1587,7 +1753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1603,7 +1769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1612,7 +1778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1621,7 +1787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1644,7 +1810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1666,7 +1832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1675,7 +1841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1695,7 +1861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1719,7 +1885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1736,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1752,7 +1918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1769,7 +1935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1786,7 +1952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1810,7 +1976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1837,7 +2003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1871,7 +2037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1881,7 +2047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1915,7 +2081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1942,7 +2108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1958,7 +2124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1968,7 +2134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1977,7 +2143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1993,7 +2159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2007,7 +2173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2024,7 +2190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2033,7 +2199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2049,7 +2215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2068,7 +2234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2084,7 +2250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2093,7 +2259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2116,7 +2282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2133,7 +2299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2149,7 +2315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2172,7 +2338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2196,7 +2362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2215,7 +2381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2231,7 +2397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2255,7 +2421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2271,7 +2437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2292,7 +2458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2314,7 +2480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2335,7 +2501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2388,7 +2554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2413,7 +2579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2446,7 +2612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2457,7 +2623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2473,7 +2639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2489,7 +2655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2505,7 +2671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2528,7 +2694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [