`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. .. raw:: html .. toctree:: :maxdepth: 2 :hidden: :caption: Getting Started quick_start .. toctree:: :maxdepth: 2 :hidden: :caption: Documentation user_guide contributing api .. toctree:: :maxdepth: 2 :hidden: :caption: Tutorial - Examples auto_examples/index `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. `Examples `_ -------------------------------------- A set of examples. It complements the `User Guide `_.