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Classes
RidgeClassifier

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).

Read more in the User Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new RidgeClassifier(opts?: object): RidgeClassifier;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberRegularization 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. Default Value 1
opts.class_weight?anyWeights 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?booleanIf true, X will be copied; else, it may be overwritten. Default Value true
opts.fit_intercept?booleanWhether 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). Default Value true
opts.max_iter?numberMaximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
opts.positive?booleanWhen set to true, forces the coefficients to be positive. Only ‘lbfgs’ solver is supported in this case. Default Value false
opts.random_state?numberUsed 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: Default Value 'auto'
opts.tol?numberPrecision of the solution. Note that tol has no effect for solvers ‘svd’ and ‘cholesky’. Default Value 0.0001

Returns

RidgeClassifier

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

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

Defined in: generated/linear_model/RidgeClassifier.ts:22 (opens in a new tab)

_py

PythonBridge

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

id

string

Defined in: generated/linear_model/RidgeClassifier.ts:18 (opens in a new tab)

opts

any

Defined in: generated/linear_model/RidgeClassifier.ts:19 (opens in a new tab)

Accessors

coef_

Coefficient of the features in the decision function.

coef\_ is of shape (1, n_features) when the given problem is binary.

Signature

coef_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/RidgeClassifier.ts:343 (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/RidgeClassifier.ts:441 (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/RidgeClassifier.ts:366 (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/RidgeClassifier.ts:416 (opens in a new tab)

n_iter_

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

Signature

n_iter_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/RidgeClassifier.ts:391 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/RidgeClassifier.ts:97 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/RidgeClassifier.ts:101 (opens in a new tab)

Methods

decision_function()

Predict confidence scores for samples.

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

Signature

decision_function(opts: object): Promise<ArrayLike>;

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeThe data matrix for which we want to get the confidence scores.

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/RidgeClassifier.ts:177 (opens in a new tab)

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/RidgeClassifier.ts:158 (opens in a new tab)

fit()

Fit Ridge classifier model.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining data.
opts.sample_weight?number | ArrayLikeIndividual weights for each sample. If given a float, every sample will have the same weight.
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/linear_model/RidgeClassifier.ts:212 (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/RidgeClassifier.ts:110 (opens in a new tab)

predict()

Predict class labels for samples in X.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]The data matrix for which we want to predict the targets.

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/RidgeClassifier.ts:259 (opens in a new tab)

score()

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.

Signature

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

Parameters

NameTypeDescription
optsobject-
opts.X?ArrayLike[]Test samples.
opts.sample_weight?ArrayLikeSample weights.
opts.y?ArrayLikeTrue labels for X.

Returns

Promise<number>

Defined in: generated/linear_model/RidgeClassifier.ts:294 (opens in a new tab)