sklearn.naive_bayes.CategoricalNB

class sklearn.naive_bayes.CategoricalNB(*, alpha=1.0, force_alpha='warn', fit_prior=True, class_prior=None, min_categories=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, default=1.0

Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing).

force_alphabool, default=False

If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0.

New in version 1.2.

Deprecated since version 1.2: The default value of force_alpha will change to True in v1.4.

fit_priorbool, default=True

Whether to learn class prior probabilities or not. If false, a uniform prior will be used.

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

Prior probabilities of the classes. If specified, the priors are not adjusted according to the data.

min_categoriesint or array-like of shape (n_features,), default=None

Minimum number of categories per feature.

  • integer: Sets the minimum number of categories per feature to n_categories for each features.

  • array-like: shape (n_features,) where n_categories[i] holds the minimum number of categories for the ith column of the input.

  • None (default): Determines the number of categories automatically from the training data.

New in version 0.24.

Attributes:
category_count_list of arrays of shape (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_ndarray of 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_ndarray of shape (n_classes,)

Smoothed empirical log probability for each class.

classes_ndarray of shape (n_classes,)

Class labels known to the classifier

feature_log_prob_list of arrays of shape (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_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

n_categories_ndarray of shape (n_features,), dtype=np.int64

Number of categories for each feature. This value is inferred from the data or set by the minimum number of categories.

New in version 0.24.

See also

BernoulliNB

Naive Bayes classifier for multivariate Bernoulli models.

ComplementNB

Complement Naive Bayes classifier.

GaussianNB

Gaussian Naive Bayes.

MultinomialNB

Naive Bayes classifier for multinomial models.

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(force_alpha=True)
>>> clf.fit(X, y)
CategoricalNB(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_metadata_routing()

Get metadata routing of this object.

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_fit_request(*[, sample_weight])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_partial_fit_request(*[, classes, ...])

Request metadata passed to the partial_fit method.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.
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. 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 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_metadata_routing()[source]

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

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. 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 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_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → CategoricalNB[source]

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.

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.

set_partial_fit_request(*, classes: Union[bool, None, str] = '$UNCHANGED$', sample_weight: Union[bool, None, str] = '$UNCHANGED$') → CategoricalNB[source]

Request metadata passed to the partial_fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to partial_fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for classes parameter in partial_fit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in partial_fit.

Returns:
selfobject

The updated object.

set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → CategoricalNB[source]

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.