diff --git a/examples/ensemble/plot_adaboost_twoclass.py b/examples/ensemble/plot_adaboost_twoclass.py
index bb3f18b9ebb..bee02156bc5 100644
--- a/examples/ensemble/plot_adaboost_twoclass.py
+++ b/examples/ensemble/plot_adaboost_twoclass.py
@@ -58,7 +58,7 @@ pl.legend(loc='upper right')
 pl.xlabel("Decision Boundary")
 
 # Plot the class probabilities
-class_proba = bdt.predict_proba(X)[:, 0]
+class_proba = bdt.predict_proba(X)[:, -1]
 pl.subplot(132)
 for i, n, c in zip(xrange(2), class_names, plot_colors):
     pl.hist(class_proba[y == i],
diff --git a/sklearn/ensemble/weight_boosting.py b/sklearn/ensemble/weight_boosting.py
index 1a0e616101c..1237fefc10e 100644
--- a/sklearn/ensemble/weight_boosting.py
+++ b/sklearn/ensemble/weight_boosting.py
@@ -695,7 +695,7 @@ class AdaBoostClassifier(BaseWeightBoosting, ClassifierMixin):
             if i == n_estimators:
                 break
 
-            purities = estimator.predict_proba(X)[:, 0]
+            purities = estimator.predict_proba(X)[:, -1]
             norm += weight
 
             if output is None: