[DOC] Describe classes_ attribute for GaussianNB, MultinomialNB, etc.
@@ -133,6 +133,9 @@ class GaussianNB(BaseNB):
class_count_ : array, shape (n_classes,)
number of training samples observed in each class.
+ classes_ : array, shape (n_classes,)
+ class labels known to the classifier
+
theta_ : array, shape (n_classes, n_features)
mean of each feature per class
@@ -679,6 +682,9 @@ class MultinomialNB(BaseDiscreteNB):
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.
+ classes_ : array, shape (n_classes,)
+ Class labels known to the classifier
+
feature_count_ : array, shape (n_classes, n_features)
Number of samples encountered for each (class, feature)
during fitting. This value is weighted by the sample weight when
@@ -777,6 +783,9 @@ class ComplementNB(BaseDiscreteNB):
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.
+ classes_ : array, shape (n_classes,)
+ Class labels known to the classifier
+
feature_count_ : array, shape (n_classes, n_features)
Number of samples encountered for each (class, feature) during fitting.
This value is weighted by the sample weight when provided.
@@ -882,6 +891,9 @@ class BernoulliNB(BaseDiscreteNB):
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.
+ classes_ : array, shape (n_classes,)
+ Class labels known to the classifier
+
feature_count_ : array, shape = [n_classes, n_features]
Number of samples encountered for each (class, feature)
during fitting. This value is weighted by the sample weight when
