DOC coef_ and intercept_ documentation for ComplementNB
@@ -700,7 +700,7 @@ class MultinomialNB(_BaseDiscreteNB):
Class labels known to the classifier
coef_ : ndarray of shape (n_classes, n_features)
- Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
+ Mirrors ``feature_log_prob_`` for interpreting `MultinomialNB`
as a linear model.
feature_count_ : ndarray of shape (n_classes, n_features)
@@ -713,7 +713,7 @@ class MultinomialNB(_BaseDiscreteNB):
given a class, ``P(x_i|y)``.
intercept_ : ndarray of shape (n_classes,)
- Mirrors ``class_log_prior_`` for interpreting MultinomialNB
+ Mirrors ``class_log_prior_`` for interpreting `MultinomialNB`
as a linear model.
n_features_ : int
@@ -815,6 +815,10 @@ class ComplementNB(_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 `ComplementNB`
+ as a linear model.
+
feature_all_ : ndarray of shape (n_features,)
Number of samples encountered for each feature during fitting. This
value is weighted by the sample weight when provided.
@@ -826,6 +830,10 @@ class ComplementNB(_BaseDiscreteNB):
feature_log_prob_ : ndarray of shape (n_classes, n_features)
Empirical weights for class complements.
+ intercept_ : ndarray of shape (n_classes,)
+ Mirrors ``class_log_prior_`` for interpreting `ComplementNB`
+ as a linear model.
+
n_features_ : int
Number of features of each sample.
