Ridge#
L2 regularized linear regression solved using a closed-form solution. The addition of regularization, controlled by the alpha hyper-parameter, makes Ridge less likely to overfit the training data than ordinary least squares (OLS).
Interfaces: Estimator, Learner, Ranks Features, Persistable
Data Type Compatibility: Continuous
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | alpha | 1.0 | float | The strength of the L2 regularization penalty. |
Example#
use Rubix\ML\Regressors\Ridge;
$estimator = new Ridge(2.0);
Additional Methods#
Return the weights of features in the decision function.
public coefficients() : array|null
Return the bias added to the decision function.
public bias() : float|null
Last update:
2021-01-23