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_priorarraylike, 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_iy)
. 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 logprobability 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{arraylike, 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.
 yarraylike, shape = [n_samples]
Target values.
 sample_weightarraylike, 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 outofcore 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{arraylike, 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.
 yarraylike, shape = [n_samples]
Target values.
 classesarraylike, 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_weightarraylike, 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
 Xarraylike of shape (n_samples, n_features)
 Returns
 Cndarray of shape (n_samples,)
Predicted target values for X

predict_log_proba
(self, X)[source]¶ Return logprobability estimates for the test vector X.
 Parameters
 Xarraylike of shape (n_samples, n_features)
 Returns
 Carraylike of shape (n_samples, n_classes)
Returns the logprobability 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
 Xarraylike of shape (n_samples, n_features)
 Returns
 Carraylike 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 multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike 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.