Class: LarsCV

Cross-validated Least Angle Regression model.

See glossary entry for cross-validation estimator.

Constructors

new LarsCV()

new LarsCV(opts?): LarsCV

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.copy_X`?	`boolean`	If `true`, X will be copied; else, it may be overwritten.
`opts.cv`?	`number`	Determines the cross-validation splitting strategy. Possible inputs for cv are:
`opts.eps`?	`number`	The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the `tol` parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.
`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.max_iter`?	`number`	Maximum number of iterations to perform.
`opts.max_n_alphas`?	`number`	The maximum number of points on the path used to compute the residuals in the cross-validation.
`opts.n_jobs`?	`number`	Number of CPUs to use during the cross validation. `undefined` means 1 unless in a `joblib.parallel_backend` context. `\-1` means using all processors. See Glossary for more details.
`opts.precompute`?	`boolean` \| `ArrayLike` \| `"auto"`	Whether to use a precomputed Gram matrix to speed up calculations. If set to `'auto'` let us decide. The Gram matrix cannot be passed as argument since we will use only subsets of X.
`opts.verbose`?	`number` \| `boolean`	Sets the verbosity amount.

Returns LarsCV

Defined in generated/linear_model/LarsCV.ts:25

Properties

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

Accessors

active_

Get Signature

get active_(): Promise<any>

Indices of active variables at the end of the path. If this is a list of lists, the outer list length is n_targets.

Returns Promise<any>

Defined in generated/linear_model/LarsCV.ts:378

alpha_

Get Signature

get alpha_(): Promise<number>

the estimated regularization parameter alpha

Returns Promise<number>

Defined in generated/linear_model/LarsCV.ts:468

alphas_

Get Signature

get alphas_(): Promise<ArrayLike>

the different values of alpha along the path

Returns Promise<ArrayLike>

Defined in generated/linear_model/LarsCV.ts:490

coef_

Get Signature

get coef_(): Promise<ArrayLike>

parameter vector (w in the formulation formula)

Returns Promise<ArrayLike>

Defined in generated/linear_model/LarsCV.ts:400

coef_path_

Get Signature

get coef_path_(): Promise<ArrayLike[]>

the varying values of the coefficients along the path

Returns Promise<ArrayLike[]>

Defined in generated/linear_model/LarsCV.ts:445

cv_alphas_

Get Signature

get cv_alphas_(): Promise<ArrayLike>

all the values of alpha along the path for the different folds

Returns Promise<ArrayLike>

Defined in generated/linear_model/LarsCV.ts:512

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/LarsCV.ts:603

intercept_

Get Signature

get intercept_(): Promise<number>

independent term in decision function

Returns Promise<number>

Defined in generated/linear_model/LarsCV.ts:422

mse_path_

Get Signature

get mse_path_(): Promise<ArrayLike[]>

the mean square error on left-out for each fold along the path (alpha values given by cv_alphas)

Returns Promise<ArrayLike[]>

Defined in generated/linear_model/LarsCV.ts:535

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/LarsCV.ts:580

n_iter_

Get Signature

get n_iter_(): Promise<number | ArrayLike>

the number of iterations run by Lars with the optimal alpha.

Returns Promise<number | ArrayLike>

Defined in generated/linear_model/LarsCV.ts:558

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/LarsCV.ts:87

Methods

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/LarsCV.ts:138

fit()

fit(opts): Promise<any>

Fit the model using X, y as training data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.params`?	`any`	Parameters to be passed to the CV splitter.
`opts.X`?	`ArrayLike`[]	Training data.
`opts.y`?	`ArrayLike`	Target values.

Returns Promise<any>

Defined in generated/linear_model/LarsCV.ts:155

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/LarsCV.ts:199

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/LarsCV.ts:100

predict()

predict(opts): Promise<any>

Predict using the linear model.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`any`	Samples.

Returns Promise<any>

Defined in generated/linear_model/LarsCV.ts:231

score()

score(opts): Promise<number>

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.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`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.y`?	`ArrayLike`	True values for `X`.

Returns Promise<number>

Defined in generated/linear_model/LarsCV.ts:264

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.Xy`?	`string` \| `boolean`	Metadata routing for `Xy` parameter in `fit`.

Returns Promise<any>

Defined in generated/linear_model/LarsCV.ts:310

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/LarsCV.ts:346

Lars Lasso