Package: ebm 0.1.0

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. 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.

Authors:Brandon M. Greenwell [aut, cre]

ebm_0.1.0.tar.gz
ebm_0.1.0.zip(r-4.7)ebm_0.1.0.zip(r-4.6)ebm_0.1.0.zip(r-4.5)
ebm_0.1.0.tgz(r-4.6-any)ebm_0.1.0.tgz(r-4.5-any)
ebm_0.1.0.tar.gz(r-4.7-any)ebm_0.1.0.tar.gz(r-4.6-any)
ebm_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
ebm/json (API)

# Install 'ebm' in R:
install.packages('ebm', repos = c('https://bgreenwell.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bgreenwell/ebm/issues

Pkgdown/docs site:https://bgreenwell.github.io

On CRAN:

Conda:

aiblackboxexplainable-aiexplainable-machine-learningexplainable-mlglassboximlinterpretabilityinterpretability-and-explainabilityinterpretableinterpretable-aiinterpretable-machine-learninginterpretable-mlinterpretable-modelsmachine-learningxai

4.90 score 5 stars 16 scripts 134 downloads 5 exports 27 dependencies

Last updated from:cfcfd3541f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK158
source / vignettesOK191
linux-release-x86_64OK151
macos-release-arm64OK126
macos-oldrel-arm64OK92
windows-develOK110
windows-releaseOK111
windows-oldrelOK101
wasm-releaseOK110

Exports:as.ebmebmgeom_stepribbonGeomStepribboninstall_interpret

Dependencies:clicpp11farverggplot2gluegtablehereisobandjsonlitelabelinglatticelifecycleMatrixpngR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootS7scalesvctrsviridisLitewithr

ebm-introduction

Last update: 2025-02-27
Started: 2025-02-27

Introduction to ebm
Getting started

Last update: 2025-02-24
Started: 2025-02-14