diff --git a/examples/ensemble/plot_adaboost_iris.py b/examples/ensemble/plot_adaboost_iris.py
deleted file mode 100644
index 48d0d402da0..00000000000
--- a/examples/ensemble/plot_adaboost_iris.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""
-========================================================================
-Plot the decision surfaces of boosted decision trees on the iris dataset
-========================================================================
-
-Plot the decision surfaces of boosted decision trees trained on pairs of
-features of the iris dataset.
-
-This plot compares the decision surfaces learned by a decision tree classifier
-(first column), by a boosted decision tree classifier (second column).
-
-In the first row, the classifiers are built using the sepal width and the sepal
-length features only, on the second row using the petal length and sepal length
-only, and on the third row using the petal width and the petal length only.
-"""
-print __doc__
-
-import numpy as np
-import pylab as pl
-
-from sklearn import clone
-from sklearn.datasets import load_iris
-from sklearn.ensemble import AdaBoostClassifier
-from sklearn.tree import DecisionTreeClassifier
-
-# Parameters
-n_classes = 3
-n_estimators = 30
-plot_colors = "bry"
-plot_step = 0.02
-
-# Load data
-iris = load_iris()
-
-plot_idx = 1
-
-for pair in ([0, 1], [0, 2], [2, 3]):
-    for model in (DecisionTreeClassifier(),
-                 AdaBoostClassifier(n_estimators=n_estimators)):
-         # We only take the two corresponding features
-        X = iris.data[:, pair]
-        y = iris.target
-
-        # Shuffle
-        idx = np.arange(X.shape[0])
-        np.random.seed(13)
-        np.random.shuffle(idx)
-        X = X[idx]
-        y = y[idx]
-
-        # Standardize
-        mean = X.mean(axis=0)
-        std = X.std(axis=0)
-        X = (X - mean) / std
-
-        # Train
-        clf = clone(model)
-        clf = model.fit(X, y)
-
-        # Plot the decision boundary
-        pl.subplot(3, 2, plot_idx)
-
-        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
-        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
-        xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
-                             np.arange(y_min, y_max, plot_step))
-
-        if isinstance(model, DecisionTreeClassifier):
-            Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
-            Z = Z.reshape(xx.shape)
-            cs = pl.contourf(xx, yy, Z,
-                    cmap=pl.cm.Paired)
-        else:
-            norm = sum(model.boost_weights_)
-            for weight, tree in zip(model.boost_weights_, model.estimators_):
-                Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
-                Z = Z.reshape(xx.shape)
-                cs = pl.contourf(xx, yy, Z, alpha=weight / norm,
-                        cmap=pl.cm.Paired)
-
-        #pl.xlabel("%s / %s" % (iris.feature_names[pair[0]],
-        #                       model.__class__.__name__))
-        #pl.ylabel(iris.feature_names[pair[1]])
-        pl.axis("tight")
-
-        # Plot the training points
-        for i, c in zip(xrange(n_classes), plot_colors):
-            idx = np.where(y == i)
-            pl.scatter(X[idx, 0], X[idx, 1], c=c, label=iris.target_names[i],
-                    cmap=pl.cm.Paired)
-
-        pl.axis("tight")
-
-        plot_idx += 1
-
-pl.set_cmap(pl.cm.Paired)
-pl.suptitle("Decision surfaces of a decision tree and of "
-            "a boosted decision tree.")
-pl.show()
diff --git a/examples/ensemble/plot_forest_iris.py b/examples/ensemble/plot_forest_iris.py
index ad3644edc39..d360ce8ab42 100644
--- a/examples/ensemble/plot_forest_iris.py
+++ b/examples/ensemble/plot_forest_iris.py
@@ -7,8 +7,8 @@ Plot the decision surfaces of forests of randomized trees trained on pairs of
 features of the iris dataset.
 
 This plot compares the decision surfaces learned by a decision tree classifier
-(first column), by a random forest classifier (second column) and by an extra-
-trees classifier (third column).
+(first column), by a random forest classifier (second column), by an extra-
+trees classifier (third column) and by an AdaBoost classifier (fourth column).
 
 In the first row, the classifiers are built using the sepal width and the sepal
 length features only, on the second row using the petal length and sepal length
@@ -21,7 +21,7 @@ import pylab as pl
 
 from sklearn import clone
 from sklearn.datasets import load_iris
-from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
+from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier
 from sklearn.tree import DecisionTreeClassifier
 
 # Parameters
@@ -38,7 +38,8 @@ plot_idx = 1
 for pair in ([0, 1], [0, 2], [2, 3]):
     for model in (DecisionTreeClassifier(),
                   RandomForestClassifier(n_estimators=n_estimators),
-                  ExtraTreesClassifier(n_estimators=n_estimators)):
+                  ExtraTreesClassifier(n_estimators=n_estimators),
+                  AdaBoostClassifier(n_estimators=n_estimators)):
          # We only take the two corresponding features
         X = iris.data[:, pair]
         y = iris.target
@@ -60,7 +61,7 @@ for pair in ([0, 1], [0, 2], [2, 3]):
         clf = model.fit(X, y)
 
         # Plot the decision boundary
-        pl.subplot(3, 3, plot_idx)
+        pl.subplot(3, 4, plot_idx)
 
         x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
         y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
@@ -89,6 +90,5 @@ for pair in ([0, 1], [0, 2], [2, 3]):
 
         plot_idx += 1
 
-pl.suptitle("Decision surfaces of a decision tree, of a random forest, and of "
-            "an extra-trees classifier")
+pl.suptitle("Decision surfaces of DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier and AdaBoostClassifier")
 pl.show()