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: | 0.1.2 |
| Depends: | R (≥ 3.1.0) |
| Imports: | Matrix, glmnet, xgboost (≥ 0.71.2), dplyr, fuzzyjoin, rlang, methods |
| Suggests: | testthat |
| Published: | 2019-04-28 |
| 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 |
| Reference manual: | xrf.pdf |
| Package source: | xrf_0.1.2.tar.gz |
| Windows binaries: | r-devel: xrf_0.1.2.zip, r-release: xrf_0.1.2.zip, r-oldrel: xrf_0.1.2.zip |
| OS X binaries: | r-release: xrf_0.1.2.tgz, r-oldrel: xrf_0.1.2.tgz |
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