Clarify the 'not in the book' comments
Aurélien Geron
parent
dc64daaf65
commit
7f0c64b0f4
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@ -125,20 +125,13 @@
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"# Voting Classifiers"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Code to generate Figure 6–3. The law of large numbers:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 6–3\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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@ -272,20 +265,13 @@
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"bag_clf.fit(X_train, y_train)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Code to generate Figure 6–5. A single Decision Tree (left) versus a bagging ensemble of 500 trees (right):**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 6–5\n",
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"\n",
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"def plot_decision_boundary(clf, X, y, alpha=1.0):\n",
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" axes=[-1.5, 2.4, -1, 1.5]\n",
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@ -303,15 +289,8 @@
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" color=colors[idx], marker=markers[idx], linestyle=\"none\")\n",
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" plt.axis(axes)\n",
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" plt.xlabel(r\"$x_1$\")\n",
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" plt.ylabel(r\"$x_2$\", rotation=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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" plt.ylabel(r\"$x_2$\", rotation=0)\n",
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"\n",
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"tree_clf = DecisionTreeClassifier(random_state=42)\n",
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"tree_clf.fit(X_train, y_train)\n",
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"\n",
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@ -336,7 +315,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -348,7 +327,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -357,7 +336,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 16,
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"metadata": {
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"scrolled": true
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},
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@ -378,11 +357,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this code shows how to compute the 63% proba\n",
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"print(1 - (1 - 1 / 1000) ** 1000)\n",
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"print(1 - np.exp(-1))"
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]
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@ -396,7 +375,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -417,7 +396,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -428,11 +407,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this code verifies that the predictions are identical\n",
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"bag_clf.fit(X_train, y_train)\n",
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"y_pred_bag = bag_clf.predict(X_test)\n",
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"np.all(y_pred_bag == y_pred_rf) # same predictions"
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@ -447,7 +426,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -460,20 +439,13 @@
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" print(round(score, 2), name)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Code to generate Figure 6–6. MNIST pixel importance (according to a Random Forest classifier):**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 6–6\n",
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"\n",
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"from sklearn.datasets import fetch_openml\n",
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"\n",
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@ -500,20 +472,13 @@
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"## AdaBoost"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Code to generate Figure 6–8. Decision boundaries of consecutive predictors:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 6–8\n",
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"\n",
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"m = len(X_train)\n",
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"\n",
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@ -549,7 +514,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -563,11 +528,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_decision_boundary(ada_clf, X_train, y_train) # not in the book"
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"# not in the book – in case you're curious to see what the decision boundary\n",
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"# looks like for the AdaBoost classifier\n",
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"plot_decision_boundary(ada_clf, X_train, y_train)"
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]
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},
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{
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@ -586,7 +553,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -610,7 +577,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -621,7 +588,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -632,7 +599,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -640,20 +607,13 @@
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"sum(tree.predict(X_new) for tree in (tree_reg1, tree_reg2, tree_reg3))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Code to generate Figure 6–9. In this depiction of Gradient Boosting, the first predictor (top left) is trained normally, then each consecutive predictor (middle left and lower left) is trained on the previous predictor’s residuals; the right column shows the resulting ensemble’s predictions:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 6–9\n",
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"\n",
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"def plot_predictions(regressors, X, y, axes, style,\n",
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" label=None, data_style=\"b.\", data_label=None):\n",
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@ -715,7 +675,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -728,7 +688,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -740,27 +700,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"gbrt_best.n_estimators_"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Code to generate Figure 6–10. GBRT ensembles with not enough predictors (left) and too many (right):**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – this cell generates and saves Figure 6–10\n",
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"\n",
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"fix, axes = plt.subplots(ncols=2, figsize=(10,4), sharey=True)\n",
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"\n",
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@ -784,11 +737,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"execution_count": 35,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book (at least, not in this chapter: it's presented in chapter 2)\n",
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"# not in the book – at least not in this chapter, it's presented in chapter 2\n",
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"\n",
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"import tarfile\n",
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"import urllib.request\n",
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@ -817,7 +770,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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"# not in the book\n",
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"# not in the book – evaluate the RMSE stats for the hgb_reg model\n",
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"\n",
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"from sklearn.model_selection import cross_val_score\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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},
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{
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"metadata": {},
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"metadata": {},
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"metadata": {},
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|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1155,7 +1108,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1171,7 +1124,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1188,7 +1141,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1204,7 +1157,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1213,7 +1166,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1236,7 +1189,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1246,7 +1199,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1277,7 +1230,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1289,7 +1242,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1298,7 +1251,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1309,7 +1262,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1332,7 +1285,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1344,7 +1297,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"execution_count": 68,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1353,7 +1306,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1383,7 +1336,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 71,
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1406,7 +1359,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 72,
|
||||
"execution_count": 71,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1417,7 +1370,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 73,
|
||||
"execution_count": 72,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
|
|||
Loading…
Reference in New Issue