diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
index 2f6df281d..158d61887 100644
--- a/python-package/xgboost/sklearn.py
+++ b/python-package/xgboost/sklearn.py
@@ -212,10 +212,12 @@ class XGBModel(XGBModelBase):
             self.best_iteration = self._Booster.best_iteration
         return self
 
-    def predict(self, data):
+    def predict(self, data, output_margin=False, ntree_limit=0):
         # pylint: disable=missing-docstring,invalid-name
         test_dmatrix = DMatrix(data, missing=self.missing)
-        return self.booster().predict(test_dmatrix)
+        return self.booster().predict(test_dmatrix,
+                                      output_margin=output_margin,
+                                      ntree_limit=ntree_limit)
 
     def evals_result(self):
         """Return the evaluation results.
@@ -366,9 +368,11 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
 
         return self
 
-    def predict(self, data):
+    def predict(self, data, output_margin=False, ntree_limit=0):
         test_dmatrix = DMatrix(data, missing=self.missing)
-        class_probs = self.booster().predict(test_dmatrix)
+        class_probs = self.booster().predict(test_dmatrix,
+                                             output_margin=output_margin,
+                                             ntree_limit=ntree_limit)
         if len(class_probs.shape) > 1:
             column_indexes = np.argmax(class_probs, axis=1)
         else:
@@ -376,9 +380,11 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
             column_indexes[class_probs > 0.5] = 1
         return self._le.inverse_transform(column_indexes)
 
-    def predict_proba(self, data):
+    def predict_proba(self, data, output_margin=False, ntree_limit=0):
         test_dmatrix = DMatrix(data, missing=self.missing)
-        class_probs = self.booster().predict(test_dmatrix)
+        class_probs = self.booster().predict(test_dmatrix,
+                                             output_margin=output_margin,
+                                             ntree_limit=ntree_limit)
         if self.objective == "multi:softprob":
             return class_probs
         else: