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
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:

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