Class: RidgeCV

Ridge regression with built-in cross-validation.

See glossary entry for cross-validation estimator.

By default, it performs efficient Leave-One-Out Cross-Validation.

Constructors

new RidgeCV()

new RidgeCV(opts?): RidgeCV

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.alpha_per_target`?	`boolean`	Flag indicating whether to optimize the alpha value (picked from the `alphas` parameter list) for each target separately (for multi-output settings: multiple prediction targets). When set to `true`, after fitting, the `alpha_` attribute will contain a value for each target. When set to `false`, a single alpha is used for all targets.
`opts.alphas`?	`ArrayLike`	Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to `1 / (2C)` in other linear models such as `LogisticRegression` or `LinearSVC`. If using Leave-One-Out cross-validation, alphas must be strictly positive.
`opts.cv`?	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.fit_intercept`?	`boolean`	Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
`opts.gcv_mode`?	`"auto"` \| `"svd"` \| `"eigen"`	Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are:
`opts.scoring`?	`string`	A string (see The scoring parameter: defining model evaluation rules) or a scorer callable object / function with signature `scorer(estimator, X, y)`. If `undefined`, the negative mean squared error if cv is ‘auto’ or `undefined` (i.e. when using leave-one-out cross-validation), and r2 score otherwise.
`opts.store_cv_results`?	`boolean`	Flag indicating if the cross-validation values corresponding to each alpha should be stored in the `cv_values_` attribute (see below). This flag is only compatible with `cv=None` (i.e. using Leave-One-Out Cross-Validation).
`opts.store_cv_values`?	`boolean`	Flag indicating if the cross-validation values corresponding to each alpha should be stored in the `cv_values_` attribute (see below). This flag is only compatible with `cv=None` (i.e. using Leave-One-Out Cross-Validation).

Returns RidgeCV

Defined in generated/linear_model/RidgeCV.ts:27

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/linear_model/RidgeCV.ts:25
`_isInitialized`	`boolean`	`false`	generated/linear_model/RidgeCV.ts:24
`_py`	`PythonBridge`	`undefined`	generated/linear_model/RidgeCV.ts:23
`id`	`string`	`undefined`	generated/linear_model/RidgeCV.ts:20
`opts`	`any`	`undefined`	generated/linear_model/RidgeCV.ts:21

Accessors

alpha_

Get Signature

get alpha_(): Promise<number | ArrayLike>

Estimated regularization parameter, or, if alpha_per_target=True, the estimated regularization parameter for each target.

Returns Promise<number | ArrayLike>

Defined in generated/linear_model/RidgeCV.ts:444

best_score_

Get Signature

get best_score_(): Promise<number | ArrayLike>

Score of base estimator with best alpha, or, if alpha_per_target=True, a score for each target.

Returns Promise<number | ArrayLike>

Defined in generated/linear_model/RidgeCV.ts:466

coef_

Get Signature

get coef_(): Promise<ArrayLike>

Weight vector(s).

Returns Promise<ArrayLike>

Defined in generated/linear_model/RidgeCV.ts:399

cv_results_

Get Signature

get cv_results_(): Promise<ArrayLike[]>

Cross-validation values for each alpha (only available if store_cv_results=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors if scoring is None otherwise it will contain standardized per point prediction values.

Returns Promise<ArrayLike[]>

Defined in generated/linear_model/RidgeCV.ts:376

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/RidgeCV.ts:514

intercept_

Get Signature

get intercept_(): Promise<number | ArrayLike>

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

Returns Promise<number | ArrayLike>

Defined in generated/linear_model/RidgeCV.ts:421

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/RidgeCV.ts:489

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/linear_model/RidgeCV.ts:80

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/RidgeCV.ts:131

fit()

fit(opts): Promise<any>

Fit Ridge regression model with cv.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.params`?	`any`	Parameters to be passed to the underlying scorer.
`opts.sample_weight`?	`number` \| `ArrayLike`	Individual weights for each sample. If given a float, every sample will have the same weight.
`opts.X`?	`ArrayLike`[]	Training data. If using GCV, will be cast to float64 if necessary.
`opts.y`?	`ArrayLike`	Target values. Will be cast to X’s dtype if necessary.

Returns Promise<any>

Defined in generated/linear_model/RidgeCV.ts:148

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

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRouter` encapsulating routing information.

Returns Promise<any>

Defined in generated/linear_model/RidgeCV.ts:197

init()

init(py): Promise<void>

Initializes the underlying Python resources.

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

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/linear_model/RidgeCV.ts:93

predict()

predict(opts): Promise<any>

Predict using the linear model.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Samples.

Returns Promise<any>

Defined in generated/linear_model/RidgeCV.ts:229

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

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`[]	Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n_samples, n_samples_fitted)`, where `n_samples_fitted` is the number of samples used in the fitting for the estimator.
`opts.y`?	`ArrayLike`	True values for `X`.

Returns Promise<number>

Defined in generated/linear_model/RidgeCV.ts:262

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

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`string` \| `boolean`	Metadata routing for `sample_weight` parameter in `fit`.

Returns Promise<any>

Defined in generated/linear_model/RidgeCV.ts:308

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

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`string` \| `boolean`	Metadata routing for `sample_weight` parameter in `score`.

Returns Promise<any>

Defined in generated/linear_model/RidgeCV.ts:344

RidgeClassifierCV RobustScaler