scikit-learn
diff --git a/‎sklearn/feature_selection/rfe.py
Lines changed: 7 additions & 3 deletions b/‎sklearn/feature_selection/rfe.py
Lines changed: 7 additions & 3 deletions
diff --git a/‎sklearn/feature_selection/tests/test_rfe.py
Lines changed: 19 additions & 0 deletions b/‎sklearn/feature_selection/tests/test_rfe.py
Lines changed: 19 additions & 0 deletions
@@ -390,7 +390,7 @@ def __init__(self, estimator, step=1, cv=None, scoring=None, verbose=0,
         self.verbose = verbose
         self.n_jobs = n_jobs
 
-    def fit(self, X, y):
+    def fit(self, X, y, groups=None):
         """Fit the RFE model and automatically tune the number of selected
            features.
 
@@ -403,6 +403,10 @@ def fit(self, X, y):
         y : array-like, shape = [n_samples]
             Target values (integers for classification, real numbers for
             regression).
+
+        groups : array-like, shape = [n_samples], optional
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
         """
         X, y = check_X_y(X, y, "csr")
 
@@ -442,7 +446,7 @@ def fit(self, X, y):
 
         scores = parallel(
             func(rfe, self.estimator, X, y, train, test, scorer)
-            for train, test in cv.split(X, y))
+            for train, test in cv.split(X, y, groups))
 
         scores = np.sum(scores, axis=0)
         n_features_to_select = max(
@@ -465,5 +469,5 @@ def fit(self, X, y):
 
         # Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
         # here, the scores are normalized by get_n_splits(X, y)
-        self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y)
+        self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y, groups)
         return self
@@ -11,6 +11,7 @@
 from sklearn.svm import SVC, SVR
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import GroupKFold
 
 from sklearn.utils import check_random_state
 from sklearn.utils.testing import ignore_warnings
@@ -328,3 +329,21 @@ def test_rfe_cv_n_jobs():
     rfecv.fit(X, y)
     assert_array_almost_equal(rfecv.ranking_, rfecv_ranking)
     assert_array_almost_equal(rfecv.grid_scores_, rfecv_grid_scores)
+
+
+def test_rfe_cv_groups():
+    generator = check_random_state(0)
+    iris = load_iris()
+    number_groups = 4
+    groups = np.floor(np.linspace(0, number_groups, len(iris.target)))
+    X = iris.data
+    y = (iris.target > 0).astype(int)
+
+    est_groups = RFECV(
+        estimator=RandomForestClassifier(random_state=generator),
+        step=1,
+        scoring='accuracy',
+        cv=GroupKFold(n_splits=2)
+    )
+    est_groups.fit(X, y, groups=groups)
+    assert est_groups.n_features_ > 0