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Gaussian Naive Bayes (GaussianNB)

Can perform online updates to model parameters via partial_fit.For details on algorithm used to update feature means and variance online,see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

Read more in the User Guide.

Parameters

priorsarray-like of shape (n_classes,)

Prior probabilities of the classes. If specified the priors are notadjusted according to the data.

var_smoothingfloat, default=1e-9

Portion of the largest variance of all features that is added tovariances for calculation stability.

Attributes

class_count_ndarray of shape (n_classes,)

number of training samples observed in each class.

class_prior_ndarray of shape (n_classes,)

probability of each class.

classes_ndarray of shape (n_classes,)

class labels known to the classifier

epsilon_float

absolute additive value to variances

sigma_ndarray of shape (n_classes, n_features)

variance of each feature per class

theta_ndarray of shape (n_classes, n_features)

mean of each feature per class

Examples

Methods

`fit`(X, y[, sample_weight])	Fit Gaussian Naive Bayes 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)	3d optical mouse rating 5v 100ma driver download mac. 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.

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

Fit Gaussian Naive Bayes according to X, y

Parameters

Xarray-like of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samplesand 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).

New in version 0.17: Gaussian Naive Bayes supports fitting with sample_weight.

Returns

selfobject

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators. King gordy discography.

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 consecutivelyon different chunks of a dataset so as to implement out-of-coreor online learning.

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

This method has some performance and numerical stability overhead,hence it is better to call partial_fit on chunks of data that areas large as possible (as long as fitting in the memory budget) tohide the overhead.

Parameters

Xarray-like of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples andn_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 omittedin subsequent calls.

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

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

Returns

selfobject

predict(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(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 inthe model. The columns correspond to the classes in sortedorder, 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)

Returns

Carray-like of shape (n_samples, n_classes)