The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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.

Version: 0.2.2
Depends: R (≥ 3.1.0)
Imports: Matrix, glmnet (≥ 3.0), xgboost (≥ 0.71.2), dplyr, fuzzyjoin, rlang, methods
Suggests: testthat, covr
Published: 2022-10-04
DOI: 10.32614/CRAN.package.xrf
Author: Karl Holub [aut, cre]
Maintainer: Karl Holub <karljholub at gmail.com>
BugReports: https://github.com/holub008/xrf/issues
License: MIT + file LICENSE
URL: https://github.com/holub008/xrf
NeedsCompilation: no
Materials: README
CRAN checks: xrf results

Documentation:

Reference manual: xrf.pdf

Downloads:

Package source: xrf_0.2.2.tar.gz
Windows binaries: r-devel: xrf_0.2.2.zip, r-release: xrf_0.2.2.zip, r-oldrel: xrf_0.2.2.zip
macOS binaries: r-release (arm64): xrf_0.2.2.tgz, r-oldrel (arm64): xrf_0.2.2.tgz, r-release (x86_64): xrf_0.2.2.tgz, r-oldrel (x86_64): xrf_0.2.2.tgz
Old sources: xrf archive

Reverse dependencies:

Reverse suggests: butcher, rules

Linking:

Please use the canonical form https://CRAN.R-project.org/package=xrf to link to this page.

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.