correction to code

doc/src/DecisionTrees/DecisionTrees.tex

@@ -2065,29 +2065,19 @@ ada_clf = AdaBoostClassifier(
     algorithm="SAMME.R", learning_rate=0.5, random_state=42)
 ada_clf.fit(X_train, y_train)
 
-plot_decision_boundary(ada_clf, X, y)
+from sklearn.ensemble import AdaBoostClassifier
 
-m = len(X_train)
-
-plt.figure(figsize=(11, 4))
-for subplot, learning_rate in ((121, 1), (122, 0.5)):
-    sample_weights = np.ones(m)
-    plt.subplot(subplot)
-    for i in range(5):
-        svm_clf = SVC(kernel="rbf", C=0.05, gamma="auto", random_state=42)
-        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)
-        y_pred = svm_clf.predict(X_train)
-        sample_weights[y_pred != y_train] *= (1 + learning_rate)
-        plot_decision_boundary(svm_clf, X, y, alpha=0.2)
-        plt.title("learning_rate = {}".format(learning_rate), fontsize=16)
-    if subplot == 121:
-        plt.text(-0.7, -0.65, "1", fontsize=14)
-        plt.text(-0.6, -0.10, "2", fontsize=14)
-        plt.text(-0.5,  0.10, "3", fontsize=14)
-        plt.text(-0.4,  0.55, "4", fontsize=14)
-        plt.text(-0.3,  0.90, "5", fontsize=14)
-
-save_fig("boosting_plot")
+ada_clf = AdaBoostClassifier(
+    DecisionTreeClassifier(max_depth=1), n_estimators=200,
+    algorithm="SAMME.R", learning_rate=0.5, random_state=42)
+ada_clf.fit(X_train_scaled, y_train)
+y_pred = ada_clf.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = ada_clf.predict_proba(X_test_scaled)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
 plt.show()
 \end{minted}