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.html , 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-release (arm64): ebm_0.1.0.tgz, r-oldrel (arm64): 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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