paulha
diff --git a/‎doc/whats_new.rst
Lines changed: 5 additions & 0 deletions b/‎doc/whats_new.rst
Lines changed: 5 additions & 0 deletions
diff --git a/‎sklearn/grid_search.py
Lines changed: 6 additions & 2 deletions b/‎sklearn/grid_search.py
Lines changed: 6 additions & 2 deletions
diff --git a/‎sklearn/tests/test_grid_search.py
Lines changed: 18 additions & 0 deletions b/‎sklearn/tests/test_grid_search.py
Lines changed: 18 additions & 0 deletions
@@ -19,6 +19,11 @@ New features
 Enhancements
 ............
 
+   - Added ``classes_`` attribute to :class:`model_selection.GridSearchCV`
+     that matches the ``classes_`` attribute of ``best_estimator_``. (`#7661
+     <https://github.com/scikit-learn/scikit-learn/pull/7661>`_) by `Alyssa
+     Batula`_ and `Dylan Werner-Meier`_.
+
    - The ``min_weight_fraction_leaf`` constraint in tree construction is now
      more efficient, taking a fast path to declare a node a leaf if its weight
      is less than 2 * the minimum. Note that the constructed tree will be
 
@@ -387,6 +387,10 @@ def __init__(self, estimator, scoring=None,
     def _estimator_type(self):
         return self.estimator._estimator_type
 
+    @property
+    def classes_(self):
+        return self.best_estimator_.classes_
+
     def score(self, X, y=None):
         """Returns the score on the given data, if the estimator has been refit.
 
@@ -688,7 +692,7 @@ class GridSearchCV(BaseSearchCV):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, 
+        either binary or multiclass,
         :class:`sklearn.model_selection.StratifiedKFold` is used. In all
         other cases, :class:`sklearn.model_selection.KFold` is used.
 
@@ -900,7 +904,7 @@ class RandomizedSearchCV(BaseSearchCV):
         - An iterable yielding train/test splits.
 
         For integer/None inputs, if the estimator is a classifier and ``y`` is
-        either binary or multiclass, 
+        either binary or multiclass,
         :class:`sklearn.model_selection.StratifiedKFold` is used. In all
         other cases, :class:`sklearn.model_selection.KFold` is used.
 
 
@@ -42,6 +42,7 @@
 from sklearn.metrics import f1_score
 from sklearn.metrics import make_scorer
 from sklearn.metrics import roc_auc_score
+from sklearn.linear_model import Ridge
 
 from sklearn.exceptions import ChangedBehaviorWarning
 from sklearn.exceptions import FitFailedWarning
@@ -785,3 +786,20 @@ def test_parameters_sampler_replacement():
     sampler = ParameterSampler(params_distribution, n_iter=7)
     samples = list(sampler)
     assert_equal(len(samples), 7)
+
+
+def test_classes__property():
+    # Test that classes_ property matches best_esimator_.classes_
+    X = np.arange(100).reshape(10, 10)
+    y = np.array([0] * 5 + [1] * 5)
+    Cs = [.1, 1, 10]
+
+    grid_search = GridSearchCV(LinearSVC(random_state=0), {'C': Cs})
+    grid_search.fit(X, y)
+    assert_array_equal(grid_search.best_estimator_.classes_,
+                       grid_search.classes_)
+
+    # Test that regressors do not have a classes_ attribute
+    grid_search = GridSearchCV(Ridge(), {'alpha': [1.0, 2.0]})
+    grid_search.fit(X, y)
+    assert_false(hasattr(grid_search, 'classes_'))