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Merged
merged 10 commits into from
Dec 26, 2019
Merged

DOC improve naive_bayes.py documentation #15943

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7c54edc
alphabetized and added attributes to naive_bayes.py
jigna-panchal Nov 2, 2019
0ed036b
removed extra whitespace and scipy.sparse.issparse (never used) from …
jigna-panchal Nov 2, 2019
7b87aef
Updated documentation for RandomTreesEmbedding attributes
jigna-panchal Nov 2, 2019
d590ff7
Revert "removed extra whitespace and scipy.sparse.issparse (never use…
jigna-panchal Nov 2, 2019
04d4078
Revert "Updated documentation for RandomTreesEmbedding attributes"
jigna-panchal Nov 2, 2019
ac6fa18
Cleared flake8 errors for naive_bayes.py
jigna-panchal Nov 3, 2019
436c1d1
not sure ? merge conflict
Dec 20, 2019
2d73938
fixed merge conflict, ran flake8
Dec 20, 2019
4693a27
removed duplicated examples section
Dec 21, 2019
80b1064
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn…
Dec 21, 2019
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127 changes: 63 additions & 64 deletions sklearn/naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,23 +139,23 @@ class GaussianNB(_BaseNB):

Attributes
----------
class_prior_ : array, shape (n_classes,)
probability of each class.

class_count_ : array, shape (n_classes,)
number of training samples observed in each class.

class_prior_ : array, shape (n_classes,)
probability of 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
epsilon_ : float
absolute additive value to variances

sigma_ : array, shape (n_classes, n_features)
variance of each feature per class

epsilon_ : float
absolute additive value to variances
theta_ : array, shape (n_classes, n_features)
mean of each feature per class

Examples
--------
Expand Down Expand Up @@ -685,33 +685,33 @@ class MultinomialNB(_BaseDiscreteNB):

Attributes
----------
class_count_ : array, shape (n_classes,)
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.

class_log_prior_ : array, shape (n_classes, )
Smoothed empirical log probability for each class.

intercept_ : array, shape (n_classes, )
Mirrors ``class_log_prior_`` for interpreting MultinomialNB
as a linear model.

feature_log_prob_ : array, shape (n_classes, n_features)
Empirical log probability of features
given a class, ``P(x_i|y)``.
classes_ : array, shape (n_classes,)
Class labels known to the classifier

coef_ : array, shape (n_classes, n_features)
Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
as a linear model.

class_count_ : array, shape (n_classes,)
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.

feature_log_prob_ : array, shape (n_classes, n_features)
Empirical log probability of features
given a class, ``P(x_i|y)``.

intercept_ : array, shape (n_classes, )
Mirrors ``class_log_prior_`` for interpreting MultinomialNB
as a linear model.

n_features_ : int
Number of features of each sample.

Expand Down Expand Up @@ -797,31 +797,31 @@ class ComplementNB(_BaseDiscreteNB):

Attributes
----------
class_log_prior_ : array, shape (n_classes, )
Smoothed empirical log probability for each class. Only used in edge
case with a single class in the training set.

feature_log_prob_ : array, shape (n_classes, n_features)
Empirical weights for class complements.

class_count_ : array, shape (n_classes,)
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.

class_log_prior_ : array, shape (n_classes, )
Smoothed empirical log probability for each class. Only used in edge
case with a single class in the training set.

classes_ : array, shape (n_classes,)
Class labels known to the classifier

feature_all_ : array, shape (n_features,)
Number of samples encountered for each feature during fitting. This
value is weighted by the sample weight when provided.

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.

feature_log_prob_ : array, shape (n_classes, n_features)
Empirical weights for class complements.

n_features_ : int
Number of features of each sample.

feature_all_ : array, shape (n_features,)
Number of samples encountered for each feature during fitting. This
value is weighted by the sample weight when provided.

Examples
--------
>>> import numpy as np
Expand Down Expand Up @@ -909,16 +909,13 @@ class BernoulliNB(_BaseDiscreteNB):

Attributes
----------
class_log_prior_ : array, shape = [n_classes]
Log probability of each class (smoothed).

feature_log_prob_ : array, shape = [n_classes, n_features]
Empirical log probability of features given a class, P(x_i|y).

class_count_ : array, shape = [n_classes]
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.

class_log_prior_ : array, shape = [n_classes]
Log probability of each class (smoothed).

classes_ : array, shape (n_classes,)
Class labels known to the classifier

Expand All @@ -927,26 +924,12 @@ class BernoulliNB(_BaseDiscreteNB):
during fitting. This value is weighted by the sample weight when
provided.

feature_log_prob_ : array, shape = [n_classes, n_features]
Empirical log probability of features given a class, P(x_i|y).

n_features_ : int
Number of features of each sample.

See Also
----------
MultinomialNB: The multinomial Naive Bayes classifier is \
suitable for classification with discrete features.

References
----------
C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
Information Retrieval. Cambridge University Press, pp. 234-265.
https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html

A. McCallum and K. Nigam (1998). A comparison of event models for naive
Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for
Text Categorization, pp. 41-48.

V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with
naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).

Examples
--------
Expand All @@ -960,6 +943,19 @@ class BernoulliNB(_BaseDiscreteNB):
BernoulliNB()
>>> print(clf.predict(X[2:3]))
[3]

References
----------
C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
Information Retrieval. Cambridge University Press, pp. 234-265.
https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html

A. McCallum and K. Nigam (1998). A comparison of event models for naive
Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for
Text Categorization, pp. 41-48.

V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with
naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).
"""

def __init__(self, alpha=1.0, binarize=.0, fit_prior=True,
Expand Down Expand Up @@ -1036,22 +1032,25 @@ class CategoricalNB(_BaseDiscreteNB):

Attributes
----------
class_log_prior_ : array, shape (n_classes, )
Smoothed empirical log probability for each class.

feature_log_prob_ : list of arrays, len n_features
category_count_ : list of arrays, len n_features
Holds arrays of shape (n_classes, n_categories of respective feature)
for each feature. Each array provides the empirical log probability
of categories given the respective feature and class, ``P(x_i|y)``.
for each feature. Each array provides the number of samples
encountered for each class and category of the specific feature.

class_count_ : array, shape (n_classes,)
Number of samples encountered for each class during fitting. This
value is weighted by the sample weight when provided.

category_count_ : list of arrays, len n_features
class_log_prior_ : array, shape (n_classes, )
Smoothed empirical log probability for each class.

classes_ : array, shape (n_classes,)
Class labels known to the classifier

feature_log_prob_ : list of arrays, len n_features
Holds arrays of shape (n_classes, n_categories of respective feature)
for each feature. Each array provides the number of samples
encountered for each class and category of the specific feature.
for each feature. Each array provides the empirical log probability
of categories given the respective feature and class, ``P(x_i|y)``.

n_features_ : int
Number of features of each sample.
Expand Down
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