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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 ORCID iD [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

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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