Package: xrf 0.3.1

xrf: eXtreme RuleFit

An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.

Authors:Karl Holub [aut, cre]

xrf_0.3.1.tar.gz
xrf_0.3.1.zip(r-4.7)xrf_0.3.1.zip(r-4.6)xrf_0.3.1.zip(r-4.5)
xrf_0.3.1.tgz(r-4.6-any)xrf_0.3.1.tgz(r-4.5-any)
xrf_0.3.1.tar.gz(r-4.7-any)xrf_0.3.1.tar.gz(r-4.6-any)
xrf_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
xrf/json (API)

# Install 'xrf' in R:
install.packages('xrf', repos = c('https://holub008.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/holub008/xrf/issues

On CRAN:

Conda:

5.72 score 44 stars 22 scripts 1.2k downloads 1 exports 28 dependencies

Last updated from:eb2449db22. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK141
source / vignettesOK178
linux-release-x86_64OK202
macos-release-arm64OK189
macos-oldrel-arm64OK193
windows-develOK132
windows-releaseOK104
windows-oldrelOK96
wasm-releaseOK106

Exports:xrf

Dependencies:clicodetoolsdata.tabledplyrforeachgenericsglmnetglueiteratorsjsonlitelatticelifecyclemagrittrMatrixpillarpkgconfigR6RcppRcppEigenrlangshapesurvivaltibbletidyselectutf8vctrswithrxgboost