himalaya.ridge.RidgeCV

class himalaya.ridge.RidgeCV(alphas=[0.1, 1], fit_intercept=False, solver='svd', solver_params=None, cv=5, Y_in_cpu=False, force_cpu=False)

Ridge regression with efficient cross-validation over alpha.

Solve the ridge regression:

b* = argmin_b ||X @ b - Y||^2 + alpha ||b||^2,

with a grid-search over cross-validation to find the best alpha.

Parameters

alphasarray of shape (n_alphas, )

List of L2 regularization parameter to try.

fit_interceptboolean

Whether to fit an intercept. If False, X and Y must be zero-mean over samples.

solverstr

Algorithm used during the fit, “svd” only for now.

solver_paramsdict or None

Additional parameters for the solver. See more details in the docstring of the function: RidgeCV.ALL_SOLVERS[solver]

cvint or scikit-learn splitter

Cross-validation splitter. If an int, KFold is used.

Y_in_cpubool

If True, keep the target values y in CPU memory (slower).

force_cpubool

If True, computations will be performed on CPU, ignoring the current backend. If False, use the current backend.

Examples

>>> from himalaya.ridge import RidgeCV
>>> import numpy as np
>>> n_samples, n_features, n_targets = 10, 5, 3
>>> X = np.random.randn(n_samples, n_features)
>>> Y = np.random.randn(n_samples, n_targets)
>>> clf = RidgeCV()
>>> clf.fit(X, Y)
RidgeCV()

Attributes

coef_array of shape (n_features) or (n_features, n_targets)

Ridge coefficients.

intercept_float or array of shape (n_targets, )

Intercept. Only returned when fit_intercept is True.

best_alphas_array of shape (n_targets, )

Selected best hyperparameter alphas.

cv_scores_array of shape (n_targets, )

Cross-validation scores averaged over splits, for the best alpha.

n_features_in_int

Number of features used during the fit.

Methods

fit(X[, y])	Fit ridge regression model
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict using the model.
score(X, y)	Return the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.