L2 penalized ordinary least squares linear regression (OLS) solved using the closed-form equation. The addition of regularization, controlled by the alpha parameter, makes Ridge less prone to overfitting than non-regularized linear regression.

Interfaces: Estimator, Learner, Persistable

Data Type Compatibility: Continuous


# Param Default Type Description
1 alpha 1.0 float The L2 regularization penalty amount to be added to the weight coefficients.

Additional Methods#

Return the weights of the model:

public weights() : array|null

Return the bias parameter:

public bias() : float|null


use Rubix\ML\Regressors\Ridge;

$estimator = new Ridge(2.0);