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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.
Getting started
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Package details |
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| Author | Brandon M. Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>) |
| Maintainer | Brandon M. Greenwell <greenwell.brandon@gmail.com> |
| License | MIT + file LICENSE |
| Version | 0.1.0 |
| URL | https://github.com/bgreenwell/ebm https://bgreenwell.github.io/ebm/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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