An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) tools:::Rd_expr_doi("10.48550/arXiv.1909.09223") for details. EBMs are a modern type of generalized additive model that use tree-based, cyclic gradient boosting with automatic interaction detection. They are often as accurate as state-of-the-art blackbox models while remaining completely interpretable.
Maintainer: Brandon M. Greenwell greenwell.brandon@gmail.com (ORCID)