`sklearn.naive_bayes`.CategoricalNB¶

class sklearn.naive_bayes.CategoricalNB(alpha=1.0, fit_prior=True, class_prior=None)[source]¶

Naive Bayes classifier for categorical features

The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.

Read more in the User Guide.

Parameters

alphafloat, optional (default=1.0): Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
fit_priorboolean, optional (default=True): Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
class_priorarray-like, size (n_classes,), optional (default=None): Prior probabilities of the classes. If specified the priors are not adjusted according to the data.

Attributes

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 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.
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 empirical log probability of categories given the respective feature and class, P(x_i|y).
n_features_int: Number of features of each sample.

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 CategoricalNB
>>> clf = CategoricalNB()
>>> clf.fit(X, y)
CategoricalNB()
>>> print(clf.predict(X[2:3]))
[3]

Methods

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

__init__(self, alpha=1.0, fit_prior=True, class_prior=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

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

Fit Naive Bayes classifier according to X, y

Parameters

X{array-like, sparse matrix}, shape = [n_samples, n_features]: Training vectors, where n_samples is the number of samples and n_features is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, …, n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder.
yarray-like, shape = [n_samples]: Target values.
sample_weightarray-like, shape = [n_samples], (default=None): Weights applied to individual samples (1. for unweighted).

Returns

selfobject

get_params(self, 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

paramsmapping of string to any: Parameter names mapped to their values.

partial_fit(self, 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}, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features. Here, each feature of X is assumed to be from a different categorical distribution. It is further assumed that all categories of each feature are represented by the numbers 0, …, n - 1, where n refers to the total number of categories for the given feature. This can, for instance, be achieved with the help of OrdinalEncoder.

yarray-like, shape = [n_samples]

Target values.

classesarray-like, 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, shape = [n_samples], (default=None)

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

Returns

selfobject

predict(self, X)[source]¶

Perform classification on an array of test vectors X.

Parameters

Xarray-like of shape (n_samples, n_features)

Returns

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

predict_log_proba(self, X)[source]¶

Return log-probability estimates for the test vector X.

Parameters

Xarray-like of shape (n_samples, n_features)

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(self, X)[source]¶

Return probability estimates for the test vector X.

Parameters

Xarray-like of shape (n_samples, n_features)

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(self, 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(self, **params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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

selfobject: Estimator instance.

sklearn.naive_bayes.CategoricalNB¶

`sklearn.naive_bayes`.CategoricalNB¶