Clarify the 'not in the book' comments, and rename decision_tree_instability_plot to decision_tree_high_variance_plot

05_decision_trees.ipynb

@@ -202,13 +202,6 @@
     "# Making Predictions"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code to generate Figure 5–2. Decision Tree decision boundaries**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -218,7 +211,7 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "# not in the book\n",
+    "# not in the book – just formatting details\n",
     "from matplotlib.colors import ListedColormap\n",
     "custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
     "plt.figure(figsize=(8, 4))\n",
@@ -231,7 +224,7 @@
     "    plt.plot(X_iris[:, 0][y_iris == idx], X_iris[:, 1][y_iris == idx],\n",
     "             style, label=f\"Iris {name}\")\n",
     "\n",
-    "# not in the book\n",
+    "# not in the book – this section beautifies and saves Figure 5–2\n",
     "tree_clf_deeper = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
     "tree_clf_deeper.fit(X_iris, y_iris)\n",
     "th0, th1, th2a, th2b = tree_clf_deeper.tree_.threshold[[0, 2, 3, 6]]\n",
@@ -324,13 +317,6 @@
     "# Regularization Hyperparameters"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code to generate Figure 5–3. Regularization using min_samples_leaf:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -353,7 +339,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
+    "# not in the book – this cell generates and saves Figure 5–3\n",
     "\n",
     "def plot_decision_boundary(clf, X, y, axes, cmap):\n",
     "    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n",
@@ -443,20 +429,13 @@
     "tree_reg.fit(X_quad, y_quad)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code to generate Figure 5–4. A Decision Tree for regression:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
+    "# not in the book – we've already seen how to use export_graphviz()\n",
     "export_graphviz(\n",
     "    tree_reg,\n",
     "    out_file=str(IMAGES_PATH / \"regression_tree.dot\"),\n",
@@ -477,13 +456,6 @@
     "tree_reg2.fit(X_quad, y_quad)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code to generate Figure 5–5. Predictions of two Decision Tree regression models:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 21,
@@ -508,7 +480,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
+    "# not in the book – this cell generates and saves Figure 5–5\n",
     "\n",
     "def plot_regression_predictions(tree_reg, X, y, axes=[-0.5, 0.5, -0.05, 0.25]):\n",
     "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
@@ -546,20 +518,13 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code to generate Figure 5–6. Regularizing a Decision Tree regressor:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
+    "# not in the book – this cell generates and saves Figure 5–6\n",
     "\n",
     "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
     "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
@@ -606,20 +571,13 @@
     "Rotating the dataset also leads to completely different decision boundaries:"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code to generate Figure 5–7. Sensitivity to training set rotation**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
+    "# not in the book – this cell generates and saves Figure 5–7\n",
     "\n",
     "np.random.seed(6)\n",
     "X_square = np.random.rand(100, 2) - 0.5\n",
@@ -670,7 +628,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
+    "# not in the book – this cell generates and saves Figure 5–8\n",
     "\n",
     "plt.figure(figsize=(8, 4))\n",
     "\n",
@@ -727,20 +685,13 @@
     "tree_clf_tweaked.fit(X_iris, y_iris)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code to generate Figure 5–8. Sensitivity to training set details:**"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# not in the book\n",
+    "# not in the book – this cell generates and saves Figure 5–9\n",
     "\n",
     "plt.figure(figsize=(8, 4))\n",
     "y_pred = tree_clf_tweaked.predict(X_iris_all).reshape(lengths.shape)\n",
@@ -759,7 +710,7 @@
     "plt.ylabel(\"Petal width (cm)\")\n",
     "plt.axis([0, 7.2, 0, 3])\n",
     "plt.legend()\n",
-    "save_fig(\"decision_tree_instability_plot\")\n",
+    "save_fig(\"decision_tree_high_variance_plot\")\n",
     "\n",
     "plt.show()"
    ]