diff --git a/sklearn/linear_model/stochastic_gradient.py b/sklearn/linear_model/stochastic_gradient.py
index b3f8a34fe6a8d..c6ad4c2754c51 100644
--- a/sklearn/linear_model/stochastic_gradient.py
+++ b/sklearn/linear_model/stochastic_gradient.py
@@ -802,8 +802,6 @@ def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
             average=average, n_iter=n_iter)
 
     def _check_proba(self):
-        check_is_fitted(self, "t_")
-
         if self.loss not in ("log", "modified_huber"):
             raise AttributeError("probability estimates are not available for"
                                  " loss=%r" % self.loss)
@@ -848,6 +846,8 @@ def predict_proba(self):
         return self._predict_proba
 
     def _predict_proba(self, X):
+        check_is_fitted(self, "t_")
+
         if self.loss == "log":
             return self._predict_proba_lr(X)
 
diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py
index 80b3ca394f990..9f372f706ca71 100644
--- a/sklearn/linear_model/tests/test_sgd.py
+++ b/sklearn/linear_model/tests/test_sgd.py
@@ -1,5 +1,6 @@
 import pickle
 import unittest
+import pytest
 
 import numpy as np
 import scipy.sparse as sp
@@ -467,6 +468,29 @@ def test_set_coef_multiclass(self):
         # Provided intercept_ does match dataset.
         clf = self.factory().fit(X2, Y2, intercept_init=np.zeros((3,)))
 
+    def test_sgd_predict_proba_method_access(self):
+        # Checks that SGDClassifier predict_proba and predict_log_proba methods
+        # can either be accessed or raise an appropriate error message
+        # otherwise. See
+        # https://github.com/scikit-learn/scikit-learn/issues/10938 for more
+        # details.
+        for loss in SGDClassifier.loss_functions:
+            clf = SGDClassifier(loss=loss)
+            if loss in ('log', 'modified_huber'):
+                assert hasattr(clf, 'predict_proba')
+                assert hasattr(clf, 'predict_log_proba')
+            else:
+                message = ("probability estimates are not "
+                           "available for loss={!r}".format(loss))
+                assert not hasattr(clf, 'predict_proba')
+                assert not hasattr(clf, 'predict_log_proba')
+                with pytest.raises(AttributeError,
+                                   message=message):
+                    clf.predict_proba
+                with pytest.raises(AttributeError,
+                                   message=message):
+                    clf.predict_log_proba
+
     def test_sgd_proba(self):
         # Check SGD.predict_proba