scikit-learn
diff --git a/‎sklearn/ensemble/bagging.py
Lines changed: 2 additions & 4 deletions b/‎sklearn/ensemble/bagging.py
Lines changed: 2 additions & 4 deletions
diff --git a/‎sklearn/ensemble/tests/test_bagging.py
Lines changed: 2 additions & 2 deletions b/‎sklearn/ensemble/tests/test_bagging.py
Lines changed: 2 additions & 2 deletions
diff --git a/‎sklearn/tests/test_metaestimators.py
Lines changed: 4 additions & 0 deletions b/‎sklearn/tests/test_metaestimators.py
Lines changed: 4 additions & 0 deletions
 from ..utils.random import sample_without_replacement
 from ..utils.validation import has_fit_parameter, check_is_fitted
 from ..utils.fixes import bincount
+from ..utils.metaestimators import if_delegate_has_method
 
 from .base import BaseEnsemble, _partition_estimators
 
@@ -52,7 +53,6 @@ def _parallel_build_estimators(n_estimators, ensemble, X, y, sample_weight,
     support_sample_weight = has_fit_parameter(ensemble.base_estimator_,
                                               "sample_weight")
 
-
     # Build estimators
     estimators = []
     estimators_samples = []
@@ -626,6 +626,7 @@ def predict_log_proba(self, X):
         else:
             return np.log(self.predict_proba(X))
 
+    @if_delegate_has_method(delegate='base_estimator')
     def decision_function(self, X):
         """Average of the decision functions of the base classifiers.
 
@@ -645,9 +646,6 @@ def decision_function(self, X):
 
         """
         check_is_fitted(self, "classes_")
-        # Trigger an exception if not supported
-        if not hasattr(self.base_estimator_, "decision_function"):
-            raise NotImplementedError
 
         # Check data
         X = check_array(X)
 
@@ -14,6 +14,7 @@
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_less
 from sklearn.utils.testing import assert_true
+from sklearn.utils.testing import assert_false
 from sklearn.utils.testing import assert_warns
 
 from sklearn.dummy import DummyClassifier, DummyRegressor
@@ -403,8 +404,7 @@ def test_error():
                   BaggingClassifier(base, max_features="foobar").fit, X, y)
 
     # Test support of decision_function
-    assert_raises(NotImplementedError,
-                  BaggingClassifier(base).fit(X, y).decision_function, X)
+    assert_false(hasattr(BaggingClassifier(base).fit(X, y), 'decision_function'))
 
 
 def test_parallel_classification():
 
@@ -11,6 +11,7 @@
 from sklearn.pipeline import Pipeline
 from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
 from sklearn.feature_selection import RFE, RFECV
+from sklearn.ensemble import BaggingClassifier
 
 
 class DelegatorData(object):
@@ -36,6 +37,9 @@ def __init__(self, name, construct, skip_methods=(),
                   skip_methods=['transform', 'inverse_transform', 'score']),
     DelegatorData('RFECV', RFECV,
                   skip_methods=['transform', 'inverse_transform', 'score']),
+    DelegatorData('BaggingClassifier', BaggingClassifier,
+                  skip_methods=['transform', 'inverse_transform', 'score',
+                                'predict_proba', 'predict_log_proba', 'predict'])
 ]