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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.3.1 |
rolling linux/jammy R-4.5 | xrf_0.3.1.tar.gz |
119.8 KiB |
0.3.1 |
rolling linux/noble R-4.5 | xrf_0.3.1.tar.gz |
119.8 KiB |
0.3.1 |
rolling source/ R- | xrf_0.3.1.tar.gz |
75.5 KiB |
0.3.1 |
latest linux/jammy R-4.5 | xrf_0.3.1.tar.gz |
119.8 KiB |
0.3.1 |
latest linux/noble R-4.5 | xrf_0.3.1.tar.gz |
119.8 KiB |
0.3.1 |
latest source/ R- | xrf_0.3.1.tar.gz |
75.5 KiB |
0.3.1 |
2026-04-26 source/ R- | xrf_0.3.1.tar.gz |
75.5 KiB |
0.3.1 |
2026-04-23 source/ R- | xrf_0.3.1.tar.gz |
75.5 KiB |
0.3.1 |
2026-04-09 windows/windows R-4.5 | xrf_0.3.1.zip |
122.3 KiB |
0.2.2 |
2025-04-20 source/ R- | xrf_0.2.2.tar.gz |
20.5 KiB |