diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst
index b3fdc07cd1e..8cd76e58037 100644
--- a/doc/modules/naive_bayes.rst
+++ b/doc/modules/naive_bayes.rst
@@ -93,7 +93,8 @@ are estimated using maximum likelihood.
     >>> from sklearn.naive_bayes import GaussianNB
     >>> gnb = GaussianNB()
     >>> y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)
-    >>> print("Number of mislabeled points out of a total %d points : %d" % (iris.data.shape[0],(iris.target != y_pred).sum()))
+    >>> print("Number of mislabeled points out of a total %d points : %d"
+    ...       % (iris.data.shape[0],(iris.target != y_pred).sum()))
     Number of mislabeled points out of a total 150 points : 6
 
 .. _multinomial_naive_bayes:
diff --git a/sklearn/cluster/mean_shift_.py b/sklearn/cluster/mean_shift_.py
index 7c78840cf0f..fb2f8a59404 100644
--- a/sklearn/cluster/mean_shift_.py
+++ b/sklearn/cluster/mean_shift_.py
@@ -216,7 +216,7 @@ class MeanShift(BaseEstimator, ClusterMixin):
         with bandwidth as the grid size and default values for
         other parameters.
 
-    bin_seeding : boolean
+    bin_seeding : boolean, optional
         If true, initial kernel locations are not locations of all
         points, but rather the location of the discretized version of
         points, where points are binned onto a grid whose coarseness