Class: RidgeClassifierCV

Ridge classifier with built-in cross-validation.

See glossary entry for cross-validation estimator.

By default, it performs Leave-One-Out Cross-Validation. Currently, only the n_features > n_samples case is handled efficiently.

Constructors

new RidgeClassifierCV()

new RidgeClassifierCV(opts?): RidgeClassifierCV

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`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.class_weight`?	`any`	Weights associated with classes in the form `{class_label: weight}`. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes \* np.bincount(y))`.
`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.scoring`?	`string`	A string (see The scoring parameter: defining model evaluation rules) or a scorer callable object / function with signature `scorer(estimator, X, y)`.
`opts.store_cv_results`?	`boolean`	Flag indicating if the cross-validation results corresponding to each alpha should be stored in the `cv_results_` 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 RidgeClassifierCV

Defined in generated/linear_model/RidgeClassifierCV.ts:27

Properties

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

Accessors

alpha_

Get Signature

get alpha_(): Promise<number>

Estimated regularization parameter.

Returns Promise<number>

Defined in generated/linear_model/RidgeClassifierCV.ts:512

best_score_

Get Signature

get best_score_(): Promise<number>

Score of base estimator with best alpha.

Returns Promise<number>

Defined in generated/linear_model/RidgeClassifierCV.ts:539

coef_

Get Signature

get coef_(): Promise<ArrayLike[]>

Coefficient of the features in the decision function.

coef_ is of shape (1, n_features) when the given problem is binary.

Returns Promise<ArrayLike[]>

Defined in generated/linear_model/RidgeClassifierCV.ts:458

cv_results_

Get Signature

get cv_results_(): Promise<ArrayLike[][]>

Cross-validation results for each alpha (only 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/RidgeClassifierCV.ts:429

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/RidgeClassifierCV.ts:593

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/RidgeClassifierCV.ts:485

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/RidgeClassifierCV.ts:566

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/RidgeClassifierCV.ts:73

Methods

decision_function()

decision_function(opts): Promise<ArrayLike>

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	The data matrix for which we want to get the confidence scores.

Returns Promise<ArrayLike>

Defined in generated/linear_model/RidgeClassifierCV.ts:146

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/RidgeClassifierCV.ts:127

fit()

fit(opts): Promise<any>

Fit Ridge classifier 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 vectors, where `n_samples` is the number of samples and `n_features` is the number of features. When 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/RidgeClassifierCV.ts:182

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/RidgeClassifierCV.ts:233

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/RidgeClassifierCV.ts:86

predict()

predict(opts): Promise<ArrayLike>

Predict class labels for samples in X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The data matrix for which we want to predict the targets.

Returns Promise<ArrayLike>

Defined in generated/linear_model/RidgeClassifierCV.ts:269

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

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`[]	Test samples.
`opts.y`?	`ArrayLike`	True labels for `X`.

Returns Promise<number>

Defined in generated/linear_model/RidgeClassifierCV.ts:305

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/RidgeClassifierCV.ts:353

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/RidgeClassifierCV.ts:393

RidgeClassifier RidgeCV