Making the results NaiveBayes example more explicit

Author: ankit-maverick

doc/modules/naive_bayes.rst

@@ -93,8 +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 : %d" % (iris.target != y_pred).sum())
-    Number of mislabeled points : 6
+    >>> 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: