`sklearn.naive_bayes`.BernoulliNB¶

class sklearn.naive_bayes.BernoulliNB(*, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None)[source]¶

Naive Bayes classifier for multivariate Bernoulli models.

Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features.

See also

CategoricalNB: Naive Bayes classifier for categorical features.
ComplementNB: The Complement Naive Bayes classifier described in Rennie et al. (2003).
GaussianNB: Gaussian Naive Bayes (GaussianNB).
MultinomialNB: Naive Bayes classifier for multinomial models.

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

>>> import numpy as np
>>> rng = np.random.RandomState(1)
>>> X = rng.randint(5, size=(6, 100))
>>> Y = np.array([1, 2, 3, 4, 4, 5])
>>> from sklearn.naive_bayes import BernoulliNB
>>> clf = BernoulliNB()
>>> clf.fit(X, Y)
BernoulliNB()
>>> print(clf.predict(X[2:3]))
[3]

Methods

`fit`(X, y[, sample_weight])	Fit Naive Bayes classifier according to X, y.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit`(X, y[, classes, sample_weight])	Incremental fit on a batch of samples.
`predict`(X)	Perform classification on an array of test vectors X.
`predict_log_proba`(X)	Return log-probability estimates for the test vector X.
`predict_proba`(X)	Return probability estimates for the test vector X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.

property coef_¶: DEPRECATED: Attribute coef_ was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).

fit(X, y, sample_weight=None)[source]¶

Fit Naive Bayes classifier according to X, y.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features): Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,): Target values.
sample_weightarray-like of shape (n_samples,), default=None: Weights applied to individual samples (1. for unweighted).

Returns

selfobject: Returns the instance itself.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

property intercept_¶: DEPRECATED: Attribute intercept_ was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).

property n_features_¶: DEPRECATED: Attribute n_features_ was deprecated in version 1.0 and will be removed in 1.2. Use n_features_in_ instead.

partial_fit(X, y, classes=None, sample_weight=None)[source]¶

Incremental fit on a batch of samples.

This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.

This is especially useful when the whole dataset is too big to fit in memory at once.

This method has some performance overhead hence it is better to call partial_fit on chunks of data that are as large as possible (as long as fitting in the memory budget) to hide the overhead.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target values.

classesarray-like of shape (n_classes,), default=None

List of all the classes that can possibly appear in the y vector.

Must be provided at the first call to partial_fit, can be omitted in subsequent calls.

sample_weightarray-like of shape (n_samples,), default=None

Weights applied to individual samples (1. for unweighted).

Returns

selfobject: Returns the instance itself.

predict(X)[source]¶

Perform classification on an array of test vectors X.

Parameters

Xarray-like of shape (n_samples, n_features): The input samples.

Returns

Cndarray of shape (n_samples,): Predicted target values for X.

predict_log_proba(X)[source]¶

Return log-probability estimates for the test vector X.

Parameters

Xarray-like of shape (n_samples, n_features): The input samples.

Returns

Carray-like of shape (n_samples, n_classes): Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

predict_proba(X)[source]¶

Return probability estimates for the test vector X.

Parameters

Xarray-like of shape (n_samples, n_features): The input samples.

Returns

Carray-like of shape (n_samples, n_classes): Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

score(X, y, sample_weight=None)[source]¶

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

scorefloat: Mean accuracy of self.predict(X) wrt. y.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

Examples using `sklearn.naive_bayes.BernoulliNB`¶

sklearn.naive_bayes.BernoulliNB¶

Examples using sklearn.naive_bayes.BernoulliNB¶

`sklearn.naive_bayes`.BernoulliNB¶

Examples using `sklearn.naive_bayes.BernoulliNB`¶