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.">

xrf: eXtreme RuleFit (original) (raw)

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
BugReports: https://github.com/holub008/xrf/issues
License: MIT + file
URL: https://github.com/holub008/xrf
NeedsCompilation: no
Materials: README
CRAN checks: xrf results

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