Documentaion for scikit-learn#10772 MultinomialNB alpha>0

Bhaskar Bharath · Bhaskar Bharath · commit 66d6c74e75ba · 2018-03-07T22:44:00.000-05:00
diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst
@@ -127,7 +127,7 @@ in the training set :math:`T`,
 and :math:`N_{y} = \sum_{i=1}^{|T|} N_{yi}` is the total count of
 all features for class :math:`y`.
 
-The smoothing priors :math:`\alpha \ge 0` accounts for
+The smoothing priors :math:`\alpha > 0` accounts for
 features not present in the learning samples and prevents zero probabilities
 in further computations.
 Setting :math:`\alpha = 1` is called Laplace smoothing,
