skglm.GeneralizedLinearEstimatorCV

class skglm.GeneralizedLinearEstimatorCV(datafit, penalty, solver, alphas=None, l1_ratio=None, cv=4, n_jobs=1, random_state=None, eps=0.001, n_alphas=100)

Cross-validated wrapper for GeneralizedLinearEstimator.

This class performs cross-validated selection of the regularization parameter(s) for a generalized linear estimator, supporting both L1 and elastic-net penalties.

Parameters:

datafitobject

Datafit (loss) function instance (e.g., Logistic, Quadratic).

penaltyobject

Penalty instance with an ‘alpha’ parameter (and optionally ‘l1_ratio’).

solverobject

Solver instance to use for optimization.

alphasarray-like of shape (n_alphas,), optional

List of alpha values to try. If None, they are set automatically.

l1_ratiofloat or array-like, optional

The ElasticNet mixing parameter(s), with 0 <= l1_ratio <= 1. Only used if the penalty supports ‘l1_ratio’. If None, defaults to 1.0 (Lasso).

cvint, default=4

Number of cross-validation folds.

n_jobsint, default=1

Number of jobs to run in parallel for cross-validation.

random_stateint or None, default=None

Random seed for cross-validation splitting.

epsfloat, default=1e-3

Ratio of minimum to maximum alpha if alphas are set automatically.

n_alphasint, default=100

Number of alphas along the regularization path if alphas are set automatically.

Attributes:

alpha_float

Best alpha found by cross-validation.

l1_ratio_float or None

Best l1_ratio found by cross-validation (if applicable).

best_estimator_GeneralizedLinearEstimator

Estimator fitted on the full data with the best parameters.

coef_ndarray

Coefficients of the fitted model.

intercept_float or ndarray

Intercept of the fitted model.

alphas_ndarray

Array of alphas used in the search.

scores_path_ndarray

Cross-validation scores for each parameter combination.

n_iter_int or None

Number of iterations run by the solver (if available).

n_features_in_int or None

Number of features seen during fit.

feature_names_in_ndarray or None

Names of features seen during fit.

__init__(datafit, penalty, solver, alphas=None, l1_ratio=None, cv=4, n_jobs=1, random_state=None, eps=0.001, n_alphas=100)

Methods

__init__(datafit, penalty, solver[, alphas, ...])
fit(X, y)	Fit the model using cross-validation.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters of the estimators including the datafit's and penalty's.
predict(X)	Predict target values for samples in X.
predict_proba(X)
score(X, y)
set_params(**params)	Set the parameters of this estimator.