Class: GaussianNB

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:

Python Reference

Constructors

new GaussianNB()

new GaussianNB(opts?): GaussianNB

Parameters

Returns GaussianNB

Defined in generated/naive_bayes/GaussianNB.ts:23

Properties

Accessors

class_count_

Get Signature

get class_count_(): Promise<ArrayLike>

number of training samples observed in each class.

Returns Promise<ArrayLike>

Defined in generated/naive_bayes/GaussianNB.ts:533

class_prior_

Get Signature

get class_prior_(): Promise<ArrayLike>

probability of each class.

Returns Promise<ArrayLike>

Defined in generated/naive_bayes/GaussianNB.ts:558

classes_

Get Signature

get classes_(): Promise<ArrayLike>

class labels known to the classifier.

Returns Promise<ArrayLike>

Defined in generated/naive_bayes/GaussianNB.ts:583

epsilon_

Get Signature

get epsilon_(): Promise<number>

absolute additive value to variances.

Returns Promise<number>

Defined in generated/naive_bayes/GaussianNB.ts:606

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/naive_bayes/GaussianNB.ts:654

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/naive_bayes/GaussianNB.ts:629

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Returns void

Defined in generated/naive_bayes/GaussianNB.ts:40

theta_

Get Signature

get theta_(): Promise<ArrayLike[]>

mean of each feature per class.

Returns Promise<ArrayLike[]>

Defined in generated/naive_bayes/GaussianNB.ts:702

var_

Get Signature

get var_(): Promise<ArrayLike[]>

Variance of each feature per class.

Returns Promise<ArrayLike[]>

Defined in generated/naive_bayes/GaussianNB.ts:679

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/naive_bayes/GaussianNB.ts:92

fit()

fit(opts): Promise<any>

Fit Gaussian Naive Bayes according to X, y.

Parameters

Returns Promise<any>

Defined in generated/naive_bayes/GaussianNB.ts:109

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Returns Promise<any>

Defined in generated/naive_bayes/GaussianNB.ts:153

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Returns Promise<void>

Defined in generated/naive_bayes/GaussianNB.ts:53

partial_fit()

partial_fit(opts): Promise<any>

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 and numerical stability 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

Returns Promise<any>

Defined in generated/naive_bayes/GaussianNB.ts:193

predict()

predict(opts): Promise<ArrayLike>

Perform classification on an array of test vectors X.

Parameters

Returns Promise<ArrayLike>

Defined in generated/naive_bayes/GaussianNB.ts:242

predict_joint_log_proba()

predict_joint_log_proba(opts): Promise<ArrayLike[]>

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

Returns Promise<ArrayLike[]>

Defined in generated/naive_bayes/GaussianNB.ts:276

predict_log_proba()

predict_log_proba(opts): Promise<ArrayLike[]>

Return log-probability estimates for the test vector X.

Parameters

Returns Promise<ArrayLike[]>

Defined in generated/naive_bayes/GaussianNB.ts:310

predict_proba()

predict_proba(opts): Promise<ArrayLike[]>

Return probability estimates for the test vector X.

Parameters

Returns Promise<ArrayLike[]>

Defined in generated/naive_bayes/GaussianNB.ts:342

score()

score(opts): Promise<number>

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

Returns Promise<number>

Defined in generated/naive_bayes/GaussianNB.ts:376

set_fit_request()

set_fit_request(opts): Promise<any>

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:

Parameters

Returns Promise<any>

Defined in generated/naive_bayes/GaussianNB.ts:422

set_partial_fit_request()

set_partial_fit_request(opts): Promise<any>

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:

Parameters

Returns Promise<any>

Defined in generated/naive_bayes/GaussianNB.ts:458

set_score_request()

set_score_request(opts): Promise<any>

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:

Parameters

Returns Promise<any>

Defined in generated/naive_bayes/GaussianNB.ts:501