ebm-package: ebm: Explainable Boosting Machines

In ebm: Explainable Boosting Machines

ebm: Explainable Boosting Machines

Description

An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) \Sexpr[results=rd]{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.

Author(s)

Maintainer: Brandon M. Greenwell [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/bgreenwell/ebm

• https://bgreenwell.github.io/ebm/

ebm documentation built on April 3, 2025, 7:16 p.m.