fracridge : fractional ridge regression

Ridge regression (RR) is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using RR 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, particularly where large amounts of data are analyzed. Because the selected α depends on the scale of the data and predictors, it is also not straightforwardly interpretable.

Here, we reparameterize RR in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients.

This approach, called fractional RR (FRR), has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations, and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration.

In a companion article, we show that the proposed method is fast and scalable for large-scale data problems, and delivers results that are straightforward to interpret and compare across models and datasets.

Getting started

Information regarding installation and basic usage.

User Guide

How to use the software.

Contributing to Fracridge development

How to report issue and contribute enhancements to the software.

API Documentation

An example of API documentation.


A set of examples. It complements the User Guide.