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Replace handson-ml2 with handson-ml3, and fix figure chapter numbers

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This commit is contained in:
Aurélien Geron
2021-11-23 15:42:16 +13:00
parent e38983d595
commit 5bb0366125
23 changed files with 137 additions and 137 deletions
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24
06_decision_trees.ipynb
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@@ -4,14 +4,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"**Chapter 5 – Decision Trees**"
"**Chapter 6 – Decision Trees**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_This notebook contains all the sample code and solutions to the exercises in chapter 5._"
"_This notebook contains all the sample code and solutions to the exercises in chapter 6._"
]
},
{
@@ -20,10 +20,10 @@
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/06_decision_trees.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" <a href=\"https://colab.research.google.com/github/ageron/handson-ml3/blob/main/06_decision_trees.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/06_decision_trees.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml3/blob/main/06_decision_trees.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
" </td>\n",
"</table>"
]
@@ -146,7 +146,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"**This code example generates Figure 5–1. Iris Decision Tree:**"
"**This code example generates Figure 6–1. Iris Decision Tree:**"
]
},
{
@@ -224,7 +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 – this section beautifies and saves Figure 5–2\n",
"# not in the book – this section beautifies and saves Figure 6–2\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",
@@ -339,7 +339,7 @@
"metadata": {},
"outputs": [],
"source": [
"# not in the book – this cell generates and saves Figure 5–3\n",
"# not in the book – this cell generates and saves Figure 6–3\n",
"\n",
"def plot_decision_boundary(clf, X, y, axes, cmap):\n",
" x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n",
@@ -480,7 +480,7 @@
"metadata": {},
"outputs": [],
"source": [
"# not in the book – this cell generates and saves Figure 5–5\n",
"# not in the book – this cell generates and saves Figure 6–5\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",
@@ -524,7 +524,7 @@
"metadata": {},
"outputs": [],
"source": [
"# not in the book – this cell generates and saves Figure 5–6\n",
"# not in the book – this cell generates and saves Figure 6–6\n",
"\n",
"tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
"tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
@@ -577,7 +577,7 @@
"metadata": {},
"outputs": [],
"source": [
"# not in the book – this cell generates and saves Figure 5–7\n",
"# not in the book – this cell generates and saves Figure 6–7\n",
"\n",
"np.random.seed(6)\n",
"X_square = np.random.rand(100, 2) - 0.5\n",
@@ -628,7 +628,7 @@
"metadata": {},
"outputs": [],
"source": [
"# not in the book – this cell generates and saves Figure 5–8\n",
"# not in the book – this cell generates and saves Figure 6–8\n",
"\n",
"plt.figure(figsize=(8, 4))\n",
"\n",
@@ -691,7 +691,7 @@
"metadata": {},
"outputs": [],
"source": [
"# not in the book – this cell generates and saves Figure 5–9\n",
"# not in the book – this cell generates and saves Figure 6–9\n",
"\n",
"plt.figure(figsize=(8, 4))\n",
"y_pred = tree_clf_tweaked.predict(X_iris_all).reshape(lengths.shape)\n",