ebm: Explainable Boosting Machines

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. Greenwell [aut, cre]
Maintainer:	Brandon M. Greenwell <greenwell.brandon at gmail.com>
License:	MIT + file LICENSE
URL:	https://github.com/bgreenwell/ebm, https://bgreenwell.github.io/ebm/
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	ebm results

Documentation:

Reference manual:	ebm.pdf
Vignettes:	Introduction to ebm (source, R code) ebm-introduction (source)

Downloads:

Package source:	ebm_0.1.0.tar.gz
Windows binaries:	r-devel: ebm_0.1.0.zip, r-release: ebm_0.1.0.zip, r-oldrel: ebm_0.1.0.zip
macOS binaries:	r-devel (arm64): ebm_0.1.0.tgz, r-release (arm64): ebm_0.1.0.tgz, r-oldrel (arm64): ebm_0.1.0.tgz, r-devel (x86_64): ebm_0.1.0.tgz, r-release (x86_64): ebm_0.1.0.tgz, r-oldrel (x86_64): ebm_0.1.0.tgz

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