Documentation
Classes
Ridge

Ridge

Linear least squares with l2 regularization.

Minimizes the objective function:

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new Ridge(opts?: object): Ridge;

Parameters

NameTypeDescription
opts?object-
opts.alpha?numberConstant that multiplies the L2 term, controlling regularization strength. alpha must be a non-negative float i.e. in \[0, inf). When alpha \= 0, the objective is equivalent to ordinary least squares, solved by the LinearRegression object. For numerical reasons, using alpha \= 0 with the Ridge object is not advised. Instead, you should use the LinearRegression object. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. Default Value 1
opts.copy_X?booleanIf true, X will be copied; else, it may be overwritten. Default Value true
opts.fit_intercept?booleanWhether to fit the intercept for this model. If set to false, no intercept will be used in calculations (i.e. X and y are expected to be centered). Default Value true
opts.max_iter?numberMaximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000. For ‘lbfgs’ solver, the default value is 15000.
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

Ridge

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

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

Defined in: generated/linear_model/Ridge.ts:16 (opens in a new tab)

opts

any

Defined in: generated/linear_model/Ridge.ts:17 (opens in a new tab)

Accessors

coef_

Weight vector(s).

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/Ridge.ts:296 (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/Ridge.ts:386 (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/Ridge.ts:318 (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/Ridge.ts:363 (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/Ridge.ts:341 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/Ridge.ts:92 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/linear_model/Ridge.ts:96 (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/Ridge.ts:152 (opens in a new tab)

fit()

Fit Ridge regression 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/Ridge.ts:169 (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/Ridge.ts:105 (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/Ridge.ts:216 (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/Ridge.ts:249 (opens in a new tab)