`sklearn.naive_bayes`.ComplementNB¶

class sklearn.naive_bayes.ComplementNB(*, alpha=1.0, force_alpha='warn', fit_prior=True, class_prior=None, norm=False)[source]¶

The Complement Naive Bayes classifier described in Rennie et al. (2003).

The Complement Naive Bayes classifier was designed to correct the “severe assumptions” made by the standard Multinomial Naive Bayes classifier. It is particularly suited for imbalanced data sets.

See also

BernoulliNB: Naive Bayes classifier for multivariate Bernoulli models.
CategoricalNB: Naive Bayes classifier for categorical features.
GaussianNB: Gaussian Naive Bayes.
MultinomialNB: Naive Bayes classifier for multinomial models.

References

Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In ICML (Vol. 3, pp. 616-623). https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf

Examples

>>> import numpy as np
>>> rng = np.random.RandomState(1)
>>> X = rng.randint(5, size=(6, 100))
>>> y = np.array([1, 2, 3, 4, 5, 6])
>>> from sklearn.naive_bayes import ComplementNB
>>> clf = ComplementNB(force_alpha=True)
>>> clf.fit(X, y)
ComplementNB(force_alpha=True)
>>> 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_joint_log_proba`(X)	Return joint log probability estimates for the test vector 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.

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.

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_joint_log_proba(X)[source]¶

Return joint log probability estimates for the test vector X.

For each row x of X and class y, the joint log probability is given by log P(x, y) = log P(y) + log P(x|y), where log P(y) is the class prior probability and log P(x|y) is the class-conditional probability.

Parameters:

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

Returns:

Cndarray of shape (n_samples, n_classes): Returns the joint 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_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) w.r.t. 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.ComplementNB`¶

Sample pipeline for text feature extraction and evaluation

Classification of text documents using sparse features

sklearn.naive_bayes.ComplementNB¶

Examples using sklearn.naive_bayes.ComplementNB¶

`sklearn.naive_bayes`.ComplementNB¶

Examples using `sklearn.naive_bayes.ComplementNB`¶