8000 alphabetized and added attributes to naive_bayes.py · scikit-learn/scikit-learn@7c54edc · GitHub
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alphabetized and added attributes to naive_bayes.py
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‎sklearn/naive_bayes.py

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@@ -139,26 +139,23 @@ class GaussianNB(BaseNB):
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Attributes
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----------
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class_prior_ : array, shape (n_classes,)
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probability of each class.
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class_count_ : array, shape (n_classes,)
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number of training samples observed in each class.
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class_prior_ : array, shape (n_classes,)
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probability of each class.
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classes_ : array, shape (n_classes,)
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class labels known to the classifier
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theta_ : array, shape (n_classes, n_features)
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mean of each feature per class
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sigma_ : array, shape (n_classes, n_features)
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variance of each feature per class
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epsilon_ : float
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absolute additive value to variances
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classes_ : array-like, shape (n_classes,)
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Unique class labels.
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sigma_ : array, shape (n_classes, n_features)
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variance of each f 8000 eature per class
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theta_ : array, shape (n_classes, n_features)
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mean of each feature per class
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Examples
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--------
@@ -689,38 +686,36 @@ class MultinomialNB(BaseDiscreteNB):
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Attributes
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----------
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class_log_prior_ : array, shape (n_classes, )
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Smoothed empirical log probability for each class.
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intercept_ : array, shape (n_classes, )
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Mirrors ``class_log_prior_`` for interpreting MultinomialNB
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as a linear model.
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feature_log_prob_ : array, shape (n_classes, n_features)
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Empirical log probability of features
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given a class, ``P(x_i|y)``.
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coef_ : array, shape (n_classes, n_features)
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Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
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as a linear model.
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class_count_ : array, shape (n_classes,)
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Number of samples encountered for each class during fitting. This
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value is weighted by the sample weight when provided.
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class_log_prior_ : array, shape (n_classes, )
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Smoothed empirical log probability for each class.
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classes_ : array, shape (n_classes,)
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Class labels known to the classifier
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coef_ : array, shape (n_classes, n_features)
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Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
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as a linear model.
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feature_count_ : array, shape (n_classes, n_features)
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Number of samples encountered for each (class, feature)
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during fitting. This value is weighted by the sample weight when
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provided.
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feature_log_prob_ : array, shape (n_classes, n_features)
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Empirical log probability of features
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given a class, ``P(x_i|y)``.
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intercept_ : array, shape (n_classes, )
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Mirrors ``class_log_prior_`` for interpreting MultinomialNB
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as a linear model.
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n_features_ : int
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Number of features of each sample.
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classes_ : array-like, shape (n_classes,)
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Unique class labels.
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Examples
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--------
@@ -804,33 +799,30 @@ class ComplementNB(BaseDiscreteNB):
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Attributes
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----------
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class_log_prior_ : array, shape (n_classes, )
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Smoothed empirical log probability for each class. Only used in edge
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case with a single class in the training set.
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feature_log_prob_ : array, shape (n_classes, n_features)
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Empirical weights for class complements.
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class_count_ : array, shape (n_classes,)
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Number of samples encountered for each class during fitting. This
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value is weighted by the sample weight when provided.
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class_log_prior_ : array, shape (n_classes, )
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Smoothed empirical log probability for each class. Only used in edge
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case with a single class in the training set.
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classes_ : array, shape (n_classes,)
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Class labels known to the classifier
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feature_all_ : array, shape (n_features,)
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Number of samples encountered for each feature during fitting. This
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value is weighted by the sample weight when provided.
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feature_count_ : array, shape (n_classes, n_features)
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Number of samples encountered for each (class, feature) during fitting.
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This value is weighted by the sample weight when provided.
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n_features_ : int
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Number of features of each sample.
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feature_all_ : array, shape (n_features,)
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Number of samples encountered for each feature during fitting. This
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value is weighted by the sample weight when provided.
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feature_log_prob_ : array, shape (n_classes, n_features)
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Empirical weights for class complements.
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classes_ : array of shape (n_classes,)
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The classes labels.
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n_features_ : int
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Number of features of each sample.
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Examples
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--------
@@ -919,29 +911,27 @@ class BernoulliNB(BaseDiscreteNB):
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Attributes
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----------
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class_log_prior_ : array, shape = [n_classes]
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Log probability of each class (smoothed).
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feature_log_prob_ : array, shape = [n_classes, n_features]
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Empirical log probability of features given a class, P(x_i|y).
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class_count_ : array, shape = [n_classes]
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Number of samples encountered for each class during fitting. This
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value is weighted by the sample weight when provided.
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class_log_prior_ : array, shape = [n_classes]
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Log probability of each class (smoothed).
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classes_ : array, shape (n_classes,)
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Class labels known to the classifier
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feature_count_ : array, shape = [n_classes, n_features]
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Number of samples encountered for each (class, feature)
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during fitting. This value is weighted by the sample weight when
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provided.
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feature_log_prob_ : array, shape = [n_classes, n_features]
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Empirical log probability of features given a class, P(x_i|y).
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n_features_ : int
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Number of features of each sample.
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classes_ : array of shape (n_classes,)
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The classes labels.
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Examples
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--------
@@ -1045,23 +1035,26 @@ class CategoricalNB(BaseDiscreteNB):
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Attributes
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----------
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class_log_prior_ : array, shape (n_classes, )
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Smoothed empirical log probability for each class.
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feature_log_prob_ : list of arrays, len n_features
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category_count_ : list of arrays, len n_features
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Holds arrays of shape (n_classes, n_categories of respective feature)
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for each feature. Each array provides the empirical log probability
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of categories given the respective feature and class, ``P(x_i|y)``.
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for each feature. Each array provides the number of samples
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encountered for each class and category of the specific feature.
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class_count_ : array, shape (n_classes,)
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Number of samples encountered for each class during fitting. This
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value is weighted by the sample weight when provided.
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class_log_prior_ : array, shape (n_classes, )
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Smoothed empirical log probability for each class.
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category_count_ : list of arrays, len n_features
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classes_ : array, shape (n_classes,)
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Class labels known to the classifier
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feature_log_prob_ : list of arrays, len n_features
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Holds arrays of shape (n_classes, n_categories of respective feature)
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for each feature. Each array provides the number of samples
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encountered for each class and category of the specific feature.
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for each feature. Each array provides the empirical log probability
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of categories given the respective feature and class, ``P(x_i|y)``.
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n_features_ : int
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Number of features of each sample.
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