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Classes
GaussianNB

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 (opens in a new tab)

Constructors

constructor()

Signature

new GaussianNB(opts?: object): GaussianNB;

Parameters

NameTypeDescription
opts?object-
opts.priors?ArrayLikePrior probabilities of the classes. If specified, the priors are not adjusted according to the data.
opts.var_smoothing?numberPortion of the largest variance of all features that is added to variances for calculation stability. Default Value 1e-9

Returns

GaussianNB

Defined in: generated/naive_bayes/GaussianNB.ts:23 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/naive_bayes/GaussianNB.ts:21 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/naive_bayes/GaussianNB.ts:20 (opens in a new tab)

_py

PythonBridge

Defined in: generated/naive_bayes/GaussianNB.ts:19 (opens in a new tab)

id

string

Defined in: generated/naive_bayes/GaussianNB.ts:16 (opens in a new tab)

opts

any

Defined in: generated/naive_bayes/GaussianNB.ts:17 (opens in a new tab)

Accessors

class_count_

number of training samples observed in each class.

Signature

class_count_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/GaussianNB.ts:408 (opens in a new tab)

class_prior_

probability of each class.

Signature

class_prior_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/GaussianNB.ts:433 (opens in a new tab)

classes_

class labels known to the classifier.

Signature

classes_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/GaussianNB.ts:458 (opens in a new tab)

epsilon_

absolute additive value to variances.

Signature

epsilon_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/naive_bayes/GaussianNB.ts:481 (opens in a new tab)

feature_names_in_

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

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/GaussianNB.ts:529 (opens in a new tab)

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/naive_bayes/GaussianNB.ts:504 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/naive_bayes/GaussianNB.ts:40 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/naive_bayes/GaussianNB.ts:44 (opens in a new tab)

theta_

mean of each feature per class.

Signature

theta_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/GaussianNB.ts:577 (opens in a new tab)

var_

Variance of each feature per class.

Signature

var_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/GaussianNB.ts:554 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

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

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/naive_bayes/GaussianNB.ts:95 (opens in a new tab)

fit()

Fit Gaussian Naive Bayes according to X, y.

Signature

fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training vectors, where n\_samples is the number of samples and n\_features is the number of features.
opts.sample_weight?ArrayLikeWeights applied to individual samples (1. for unweighted).
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/naive_bayes/GaussianNB.ts:112 (opens in a new tab)

init()

Initializes the underlying Python resources.

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

Signature

init(py: PythonBridge): Promise<void>;

Parameters

NameType
pyPythonBridge

Returns

Promise<void>

Defined in: generated/naive_bayes/GaussianNB.ts:53 (opens in a new tab)

partial_fit()

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.

Signature

partial_fit(opts: object): Promise<any>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training vectors, where n\_samples is the number of samples and n\_features is the number of features.
opts.classes?ArrayLikeList 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.
opts.sample_weight?ArrayLikeWeights applied to individual samples (1. for unweighted).
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/naive_bayes/GaussianNB.ts:165 (opens in a new tab)

predict()

Perform classification on an array of test vectors X.

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike>

Defined in: generated/naive_bayes/GaussianNB.ts:223 (opens in a new tab)

predict_joint_log_proba()

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.

Signature

predict_joint_log_proba(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/GaussianNB.ts:258 (opens in a new tab)

predict_log_proba()

Return log-probability estimates for the test vector X.

Signature

predict_log_proba(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/GaussianNB.ts:293 (opens in a new tab)

predict_proba()

Return probability estimates for the test vector X.

Signature

predict_proba(opts: object): Promise<ArrayLike[]>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The input samples.

Returns

Promise<ArrayLike[]>

Defined in: generated/naive_bayes/GaussianNB.ts:326 (opens in a new tab)

score()

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.

Signature

score(opts: object): Promise<number>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Test samples.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue labels for X.

Returns

Promise<number>

Defined in: generated/naive_bayes/GaussianNB.ts:361 (opens in a new tab)