Improve alignment between notebook and book section headers

05_support_vector_machines.ipynb

@@ -84,14 +84,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Large margin classification"
+    "# Linear SVM Classification"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after:"
+    "The next few code cells generate the first figures in chapter 5. The first actual code sample comes after.\n",
+    "\n",
+    "**Code to generate Figure 5–1. Large margin classification**"
    ]
   },
   {
@@ -175,7 +177,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sensitivity to feature scales"
+    "**Code to generate Figure 5–2. Sensitivity to feature scales**"
    ]
   },
   {
@@ -220,7 +222,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sensitivity to outliers"
+    "## Soft Margin Classification\n",
+    "**Code to generate Figure 5–3. Hard margin sensitivity to outliers**"
    ]
   },
   {
@@ -278,14 +281,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Large margin *vs* margin violations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is the first code example in chapter 5:"
+    "**This is the first code example in chapter 5:**"
    ]
   },
   {
@@ -325,7 +321,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's generate the graph comparing different regularization settings:"
+    "**Code to generate Figure 5–4. Large margin versus fewer margin violations**"
    ]
   },
   {
@@ -408,7 +404,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Non-linear classification"
+    "# Nonlinear SVM Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–5. Adding features to make a dataset linearly separable**"
    ]
   },
   {
@@ -471,6 +474,13 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Here is second code example in the chapter:**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -490,6 +500,13 @@
     "polynomial_svm_clf.fit(X, y)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–6. Linear SVM classifier using polynomial features**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -513,6 +530,20 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polynomial Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Next code example:**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -528,6 +559,13 @@
     "poly_kernel_svm_clf.fit(X, y)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–7. SVM classifiers with a polynomial kernel**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -564,6 +602,20 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Similarity Features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–8. Similarity features using the Gaussian RBF**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 18,
@@ -644,6 +696,20 @@
     "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gaussian RBF Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Next code example:**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -657,6 +723,13 @@
     "rbf_kernel_svm_clf.fit(X, y)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–9. SVM classifiers using an RBF kernel**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 21,
@@ -701,7 +774,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Regression\n"
+    "# SVM Regression"
    ]
   },
   {
@@ -716,6 +789,13 @@
     "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Next code example:**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 23,
@@ -728,6 +808,13 @@
     "svm_reg.fit(X, y)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–10. SVM Regression**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 24,
@@ -807,6 +894,13 @@
     "**Note**: to be future-proof, we set `gamma=\"scale\"`, as this will be the default value in Scikit-Learn 0.22."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Next code example:**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 27,
@@ -819,6 +913,13 @@
     "svm_poly_reg.fit(X, y)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–11. SVM Regression using a second-degree polynomial kernel**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 28,
@@ -855,7 +956,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Under the hood"
+    "# Under the Hood\n",
+    "## Decision Function and Predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Code to generate Figure 5–12. Decision function for the iris dataset**"
    ]
   },
   {
@@ -917,7 +1026,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Small weight vector results in a large margin"
+    "**Code to generate Figure 5–13. A smaller weight vector results in a larger margin**"
    ]
   },
   {
@@ -976,7 +1085,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Hinge loss"
+    "**Code to generate the Hinge Loss figure:**"
    ]
   },
   {