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Classes
RidgeCV

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.

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new RidgeCV(opts?: object): RidgeCV;

Parameters

NameTypeDescription
opts?object-
opts.alpha_per_target?booleanFlag 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. Default Value false
opts.alphas?ArrayLikeArray 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 positive.
opts.cv?numberDetermines the cross-validation splitting strategy. Possible inputs for cv are:
opts.fit_intercept?booleanWhether 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). Default Value true
opts.gcv_mode?"auto" | "svd" | "eigen"Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are: Default Value 'auto'
opts.scoring?stringA string (see model evaluation documentation) 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_values?booleanFlag 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). Default Value false

Returns

RidgeCV

Defined in: generated/linear_model/RidgeCV.ts:27 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/linear_model/RidgeCV.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/linear_model/RidgeCV.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/linear_model/RidgeCV.ts:23 (opens in a new tab)

id

string

Defined in: generated/linear_model/RidgeCV.ts:20 (opens in a new tab)

opts

any

Defined in: generated/linear_model/RidgeCV.ts:21 (opens in a new tab)

Accessors

alpha_

Estimated regularization parameter, or, if alpha\_per\_target=True, the estimated regularization parameter for each target.

Signature

alpha_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/RidgeCV.ts:345 (opens in a new tab)

best_score_

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

Signature

best_score_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/RidgeCV.ts:367 (opens in a new tab)

coef_

Weight vector(s).

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/RidgeCV.ts:300 (opens in a new tab)

cv_values_

Cross-validation values for each alpha (only available if store\_cv\_values=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.

Signature

cv_values_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/RidgeCV.ts:277 (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/linear_model/RidgeCV.ts:415 (opens in a new tab)

intercept_

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

Signature

intercept_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/RidgeCV.ts:322 (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/linear_model/RidgeCV.ts:390 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/RidgeCV.ts:75 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/RidgeCV.ts:79 (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/linear_model/RidgeCV.ts:133 (opens in a new tab)

fit()

Fit Ridge regression model with cv.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Training data. If using GCV, will be cast to float64 if necessary.
opts.sample_weight?number | ArrayLikeIndividual weights for each sample. If given a float, every sample will have the same weight.
opts.y?ArrayLikeTarget values. Will be cast to X’s dtype if necessary.

Returns

Promise<any>

Defined in: generated/linear_model/RidgeCV.ts:150 (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/linear_model/RidgeCV.ts:88 (opens in a new tab)

predict()

Predict using the linear model.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?anySamples.

Returns

Promise<any>

Defined in: generated/linear_model/RidgeCV.ts:197 (opens in a new tab)

score()

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.

Signature

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

Parameters

NameTypeDescription
optsobject-
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.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/linear_model/RidgeCV.ts:230 (opens in a new tab)