LarsCV

Cross-validated Least Angle Regression model.

See glossary entry for cross-validation estimator.

Constructors

constructor()

Signature

new LarsCV(opts?: object): LarsCV;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.copy_X?`	`boolean`	If `true`, X will be copied; else, it may be overwritten. `Default Value` `true`
`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). `Default Value` `true`
`opts.max_iter?`	`number`	Maximum number of iterations to perform. `Default Value` `500`
`opts.max_n_alphas?`	`number`	The maximum number of points on the path used to compute the residuals in the cross-validation. `Default Value` `1000`
`opts.n_jobs?`	`number`	Number of CPUs to use during the cross validation. `undefined` means 1 unless in a `joblib.parallel\_backend` (opens in a new tab) context. `\-1` means using all processors. See Glossary for more details.
`opts.normalize?`	`boolean`	This parameter is ignored when `fit\_intercept` is set to `false`. If `true`, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use `StandardScaler` before calling `fit` on an estimator with `normalize=False`. `Default Value` `false`
`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. `Default Value` `'auto'`
`opts.verbose?`	`number` \| `boolean`	Sets the verbosity amount. `Default Value` `false`

Returns

LarsCV

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

Properties

_isDisposed

boolean = false

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

_isInitialized

boolean = false

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

_py

PythonBridge

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

id

string

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

opts

any

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

Accessors

active_

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

Signature

active_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/linear_model/LarsCV.ts:291 (opens in a new tab)

alpha_

the estimated regularization parameter alpha

Signature

alpha_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/LarsCV.ts:381 (opens in a new tab)

alphas_

the different values of alpha along the path

Signature

alphas_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LarsCV.ts:403 (opens in a new tab)

coef_

parameter vector (w in the formulation formula)

Signature

coef_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LarsCV.ts:313 (opens in a new tab)

coef_path_

the varying values of the coefficients along the path

Signature

coef_path_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/LarsCV.ts:358 (opens in a new tab)

cv_alphas_

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

Signature

cv_alphas_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/linear_model/LarsCV.ts:425 (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/LarsCV.ts:516 (opens in a new tab)

intercept_

independent term in decision function

Signature

intercept_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/linear_model/LarsCV.ts:335 (opens in a new tab)

mse_path_

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

Signature

mse_path_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/linear_model/LarsCV.ts:448 (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/LarsCV.ts:493 (opens in a new tab)

n_iter_

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

Signature

n_iter_(): Promise<number | ArrayLike>;

Returns

Promise<number | ArrayLike>

Defined in: generated/linear_model/LarsCV.ts:471 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/linear_model/LarsCV.ts:94 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/linear_model/LarsCV.ts:98 (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/LarsCV.ts:154 (opens in a new tab)

fit()

Fit the model using X, y as training data.

Signature

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

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training data.
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/linear_model/LarsCV.ts:171 (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

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/linear_model/LarsCV.ts:107 (opens in a new tab)

predict()

Predict using the linear model.

Signature

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

Parameters

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

Returns

Promise<any>

Defined in: generated/linear_model/LarsCV.ts:211 (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

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

Returns

Promise<number>

Defined in: generated/linear_model/LarsCV.ts:244 (opens in a new tab)

Lars Lasso