LarsCV#

classsklearn.linear_model.LarsCV(*,fit_intercept=True,verbose=False,max_iter=500,precompute='auto',cv=None,max_n_alphas=1000,n_jobs=None,eps=np.float64(2.220446049250313e-16),copy_X=True)[source]#

Cross-validated Least Angle Regression model.

See glossary entry forcross-validation estimator.

Read more in theUser Guide.

Parameters:

fit_interceptbool, default=True

Whether to calculate the intercept for this model. If setto false, no intercept will be used in calculations(i.e. data is expected to be centered).

verbosebool or int, default=False

Sets the verbosity amount.

max_iterint, default=500

Maximum number of iterations to perform.

precomputebool, ‘auto’ or array-like , default=’auto’

Whether to use a precomputed Gram matrix to speed upcalculations. If set to'auto' let us decide. The Gram matrixcannot be passed as argument since we will use only subsets of X.

cvint, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy.Possible inputs for cv are:

• None, to use the default 5-fold cross-validation,

• integer, to specify the number of folds.

• CV splitter,

• An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs,KFold is used.

ReferUser Guide for the variouscross-validation strategies that can be used here.

Changed in version 0.22:cv default value if None changed from 3-fold to 5-fold.

max_n_alphasint, default=1000

The maximum number of points on the path used to compute theresiduals in the cross-validation.

n_jobsint or None, default=None

Number of CPUs to use during the cross validation.None means 1 unless in ajoblib.parallel_backend context.-1 means using all processors. SeeGlossaryfor more details.

epsfloat, default=np.finfo(float).eps

The machine-precision regularization in the computation of theCholesky diagonal factors. Increase this for very ill-conditionedsystems. Unlike thetol parameter in some iterativeoptimization-based algorithms, this parameter does not controlthe tolerance of the optimization.

copy_Xbool, default=True

IfTrue, X will be copied; else, it may be overwritten.

Attributes:

active_list of length n_alphas or list of such lists

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

coef_array-like of shape (n_features,)

parameter vector (w in the formulation formula)

intercept_float

independent term in decision function

coef_path_array-like of shape (n_features, n_alphas)

the varying values of the coefficients along the path

alpha_float

the estimated regularization parameter alpha

alphas_array-like of shape (n_alphas,)

the different values of alpha along the path

cv_alphas_array-like of shape (n_cv_alphas,)

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

mse_path_array-like of shape (n_folds, n_cv_alphas)

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

n_iter_array-like or int

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

n_features_in_int

Number of features seen duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added in version 1.0.

See also

lars_path

Compute Least Angle Regression or Lasso path using LARS algorithm.

lasso_path

Compute Lasso path with coordinate descent.

Lasso

Linear Model trained with L1 prior as regularizer (aka the Lasso).

LassoCV

Lasso linear model with iterative fitting along a regularization path.

LassoLars

Lasso model fit with Least Angle Regression a.k.a. Lars.

LassoLarsIC

Lasso model fit with Lars using BIC or AIC for model selection.

sklearn.decomposition.sparse_encode

Sparse coding.

Notes

Infit, once the best parameteralpha is found throughcross-validation, the model is fit again using the entire training set.

Examples

>>>fromsklearn.linear_modelimportLarsCV>>>fromsklearn.datasetsimportmake_regression>>>X,y=make_regression(n_samples=200,noise=4.0,random_state=0)>>>reg=LarsCV(cv=5).fit(X,y)>>>reg.score(X,y)0.9996>>>reg.alpha_np.float64(0.2961)>>>reg.predict(X[:1,])array([154.3996])

fit(X,y,**params)[source]#

Fit the model using X, y as training data.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training data.

yarray-like of shape (n_samples,)

Target values.

**paramsdict, default=None

Parameters to be passed to the CV splitter.

Added in version 1.4:Only available ifenable_metadata_routing=True,which can be set by usingsklearn.set_config(enable_metadata_routing=True).SeeMetadata Routing User Guide formore details.

Returns:

selfobject

Returns an instance of self.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Added in version 1.4.

Returns:

routingMetadataRouter

AMetadataRouter encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

predict(X)[source]#

Predict using the linear model.

Parameters:

Xarray-like or sparse matrix, shape (n_samples, n_features)

Samples.

Returns:

Carray, shape (n_samples,)

Returns predicted values.

score(X,y,sample_weight=None)[source]#

Returncoefficient of determination on test data.

The coefficient of determination,\(R^2\), is defined as\((1 - \frac{u}{v})\), where\(u\) is the residualsum 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 themodel can be arbitrarily worse). A constant model that always predictsthe expected value ofy, disregarding the input features, would geta\(R^2\) score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputedkernel matrix or a list of generic objects instead with shape(n_samples,n_samples_fitted), wheren_samples_fittedis the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values forX.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

\(R^2\) ofself.predict(X) w.r.t.y.

Notes

The\(R^2\) score used when callingscore on a regressor usesmultioutput='uniform_average' from version 0.23 to keep consistentwith default value ofr2_score.This influences thescore method of all the multioutputregressors (except forMultiOutputRegressor).

set_fit_request(*,Xy:bool|None|str='$UNCHANGED$')→LarsCV[source]#

Configure whether metadata should be requested to be passed to thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

• True: metadata is requested, and passed tofit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it tofit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

Xystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forXy parameter infit.

Returns:

selfobject

The updated object.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_score_request(*,sample_weight:bool|None|str='$UNCHANGED$')→LarsCV[source]#

Configure whether metadata should be requested to be passed to thescore method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

• True: metadata is requested, and passed toscore if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it toscore.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter inscore.

Returns:

selfobject

The updated object.