Class: RidgeClassifier

Classifier using Ridge regression.

This classifier first converts the target values into {-1, 1} and then treats the problem as a regression task (multi-output regression in the multiclass case).

Constructors

new RidgeClassifier()

new RidgeClassifier(opts?): RidgeClassifier

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.alpha`?	`number`	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`.
`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.copy_X`?	`boolean`	If `true`, X will be copied; else, it may be overwritten.
`opts.fit_intercept`?	`boolean`	Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
`opts.max_iter`?	`number`	Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
`opts.positive`?	`boolean`	When set to `true`, forces the coefficients to be positive. Only ‘lbfgs’ solver is supported in this case.
`opts.random_state`?	`number`	Used when `solver` == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.
`opts.solver`?	`"auto"` \| `"svd"` \| `"lsqr"` \| `"lbfgs"` \| `"sag"` \| `"saga"` \| `"cholesky"` \| `"sparse_cg"`	Solver to use in the computational routines:
`opts.tol`?	`number`	The precision of the solution (`coef_`) is determined by `tol` which specifies a different convergence criterion for each solver:

Returns RidgeClassifier

Defined in generated/linear_model/RidgeClassifier.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/linear_model/RidgeClassifier.ts:23
`_isInitialized`	`boolean`	`false`	generated/linear_model/RidgeClassifier.ts:22
`_py`	`PythonBridge`	`undefined`	generated/linear_model/RidgeClassifier.ts:21
`id`	`string`	`undefined`	generated/linear_model/RidgeClassifier.ts:18
`opts`	`any`	`undefined`	generated/linear_model/RidgeClassifier.ts:19

Accessors

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/RidgeClassifier.ts:434

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/RidgeClassifier.ts:532

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/RidgeClassifier.ts:457

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/RidgeClassifier.ts:507

n_iter_

Get Signature

get n_iter_(): Promise<ArrayLike>

Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return undefined.

Returns Promise<ArrayLike>

Defined in generated/linear_model/RidgeClassifier.ts:482

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/RidgeClassifier.ts:97

solver_

Get Signature

get solver_(): Promise<string>

The solver that was used at fit time by the computational routines.

Returns Promise<string>

Defined in generated/linear_model/RidgeClassifier.ts:557

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/RidgeClassifier.ts:168

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/RidgeClassifier.ts:149

fit()

fit(opts): Promise<any>

Fit Ridge classifier model.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`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.
`opts.y`?	`ArrayLike`	Target values.

Returns Promise<any>

Defined in generated/linear_model/RidgeClassifier.ts:202

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 `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/linear_model/RidgeClassifier.ts:246

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/RidgeClassifier.ts:110

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/RidgeClassifier.ts:280

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/RidgeClassifier.ts:314

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/RidgeClassifier.ts:360

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/RidgeClassifier.ts:398

Ridge RidgeClassifierCV