DOC coef_ and intercept_ doc for BernoulliNB
@@ -935,6 +935,10 @@ class BernoulliNB(_BaseDiscreteNB):
classes_ : ndarray of shape (n_classes,)
Class labels known to the classifier
+ coef_ : ndarray of shape (n_classes, n_features)
+ Mirrors ``feature_log_prob_`` for interpreting `BernoulliNB`
+ as a linear model.
+
feature_count_ : ndarray of shape (n_classes, n_features)
Number of samples encountered for each (class, feature)
during fitting. This value is weighted by the sample weight when
@@ -943,6 +947,10 @@ class BernoulliNB(_BaseDiscreteNB):
feature_log_prob_ : ndarray of shape (n_classes, n_features)
Empirical log probability of features given a class, P(x_i|y).
+ intercept_ : ndarray of shape (n_classes,)
+ Mirrors ``class_log_prior_`` for interpreting `BernoulliNB`
+ as a linear model.
+
n_features_ : int
Number of features of each sample.
