Class: ARDRegression

Bayesian ARD regression.

Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization)

Read more in the User Guide.

Python Reference

Constructors

new ARDRegression()

new ARDRegression(opts?): ARDRegression

Parameters

Returns ARDRegression

Defined in generated/linear_model/ARDRegression.ts:25

Properties

Accessors

alpha_

Get Signature

get alpha_(): Promise<number>

estimated precision of the noise.

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:435

coef_

Get Signature

get coef_(): Promise<ArrayLike>

Coefficients of the regression model (mean of distribution)

Returns Promise<ArrayLike>

Defined in generated/linear_model/ARDRegression.ts:412

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/linear_model/ARDRegression.ts:650

intercept_

Get Signature

get intercept_(): Promise<number>

Independent term in decision function. Set to 0.0 if fit_intercept \= False.

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:550

lambda_

Get Signature

get lambda_(): Promise<ArrayLike>

estimated precisions of the weights.

Returns Promise<ArrayLike>

Defined in generated/linear_model/ARDRegression.ts:458

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:625

n_iter_

Get Signature

get n_iter_(): Promise<number>

The actual number of iterations to reach the stopping criterion.

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:527

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Returns void

Defined in generated/linear_model/ARDRegression.ts:107

scores_

Get Signature

get scores_(): Promise<number>

if computed, value of the objective function (to be maximized)

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:504

sigma_

Get Signature

get sigma_(): Promise<ArrayLike[]>

estimated variance-covariance matrix of the weights

Returns Promise<ArrayLike[]>

Defined in generated/linear_model/ARDRegression.ts:481

X_offset_

Get Signature

get X_offset_(): Promise<number>

If fit_intercept=True, offset subtracted for centering data to a zero mean. Set to np.zeros(n_features) otherwise.

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:575

X_scale_

Get Signature

get X_scale_(): Promise<number>

Set to np.ones(n_features).

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:600

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/linear_model/ARDRegression.ts:159

fit()

fit(opts): Promise<any>

Fit the model according to the given training data and parameters.

Iterative procedure to maximize the evidence

Parameters

Returns Promise<any>

Defined in generated/linear_model/ARDRegression.ts:178

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/linear_model/ARDRegression.ts:217

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/linear_model/ARDRegression.ts:120

predict()

predict(opts): Promise<ArrayLike>

Predict using the linear model.

In addition to the mean of the predictive distribution, also its standard deviation can be returned.

Parameters

Returns Promise<ArrayLike>

Defined in generated/linear_model/ARDRegression.ts:253

score()

score(opts): Promise<number>

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true \- y_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y_true \- y_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

Returns Promise<number>

Defined in generated/linear_model/ARDRegression.ts:294

set_predict_request()

set_predict_request(opts): Promise<any>

Request metadata passed to the predict 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/linear_model/ARDRegression.ts:340

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/linear_model/ARDRegression.ts:378