diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
index 0a1d8d8e2..50089969b 100644
--- a/python-package/xgboost/sklearn.py
+++ b/python-package/xgboost/sklearn.py
@@ -495,12 +495,19 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
         feature_importances_ : array of shape = [n_features]
 
         """
-        fs = self.booster().get_fscore()
-        keys = [int(k.replace('f', '')) for k in fs.keys()]
-        fs_dict = dict(zip(keys, fs.values()))
-        all_features_dict = dict.fromkeys(range(0, self._features_count), 0)
-        all_features_dict.update(fs_dict)
-        all_features = np.fromiter(all_features_dict.values(), np.float32)
+        b = self.booster()
+        fs = b.get_fscore()
+        if b.feature_names is None:
+            keys = [int(k.replace('f', '')) for k in fs.keys()]
+            all_features_dict = dict.fromkeys(range(0, self._features_count), 0)
+            fs_dict = dict(zip(keys, fs.values()))
+            all_features_dict.update(fs_dict)
+            all_features = np.fromiter(all_features_dict.values(),
+                                       dtype=np.float32)
+        else:
+            all_features = [fs.get(f, 0.) for f in b.feature_names]
+            all_features = np.array(all_features, dtype=np.float32)
+
         return all_features / all_features.sum()
 
 
diff --git a/tests/python/test_with_sklearn.py b/tests/python/test_with_sklearn.py
index 716feee2b..d14838fd7 100644
--- a/tests/python/test_with_sklearn.py
+++ b/tests/python/test_with_sklearn.py
@@ -1,3 +1,4 @@
+import random
 import xgboost as xgb
 import numpy as np
 from sklearn.metrics import mean_squared_error
@@ -48,6 +49,37 @@ def test_multiclass_classification():
         check_pred(preds4, labels)
 
 
+def test_feature_importances():
+    digits = load_digits(2)
+    y = digits['target']
+    X = digits['data']
+    xgb_model = xgb.XGBClassifier(seed=0).fit(X, y)
+
+    exp = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.00833333, 0.,
+                    0., 0., 0., 0., 0., 0., 0., 0.025, 0.14166667, 0., 0., 0.,
+                    0., 0., 0., 0.00833333, 0.25833333, 0., 0., 0., 0.,
+                    0.03333334, 0.03333334, 0., 0.32499999, 0., 0., 0., 0.,
+                    0.05, 0.06666667, 0., 0., 0., 0., 0., 0., 0., 0.04166667,
+                    0., 0., 0., 0., 0., 0., 0., 0.00833333, 0., 0., 0., 0.,
+                    0.], dtype=np.float32)
+
+    np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
+
+    # numeric columns
+    import pandas as pd
+    y = pd.Series(digits['target'])
+    X = pd.DataFrame(digits['data'])
+    xgb_model = xgb.XGBClassifier(seed=0).fit(X, y)
+    np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
+
+    # string columns, the feature order must be kept
+    chars = list('abcdefghijklmnopqrstuvwxyz')
+    X.columns = ["".join(random.sample(chars, 5)) for x in range(64)]
+
+    xgb_model = xgb.XGBClassifier(seed=0).fit(X, y)
+    np.testing.assert_almost_equal(xgb_model.feature_importances_, exp)
+
+
 def test_boston_housing_regression():
     boston = load_boston()
     y = boston['target']