Fractional ridge regression: a fast, interpretable reparameterization of ridge regression.

ABSTRACT:

Background

Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable.

Results

The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ

SUBMITTER: Rokem A 

PROVIDER: S-EPMC7702219 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature