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.">

ebm: Explainable Boosting Machines (original) (raw)

An interface to the 'Python' 'InterpretML' framework for fitting explainable boosting machines (EBMs); see Nori et al. (2019) <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.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: reticulate, ggplot2 (≥ 0.9.0), lattice
Suggests: htmltools, ISLR2, knitr, rmarkdown, rstudioapi
Published: 2025-03-05
DOI: 10.32614/CRAN.package.ebm
Author: Brandon M. GreenwellORCID iD [aut, cre]
Maintainer: Brandon M. Greenwell <greenwell.brandon at gmail.com>
License: MIT + file
URL: https://github.com/bgreenwell/ebm,https://bgreenwell.github.io/ebm/
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: ebm results

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