eXtreme RuleFit

An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) . 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.


Reference manual

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0.2.0 by Karl Holub, a year ago


Report a bug at https://github.com/holub008/xrf/issues

Browse source code at https://github.com/cran/xrf

Authors: Karl Holub [aut, cre]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Matrix, glmnet, xgboost, dplyr, fuzzyjoin, rlang, methods

Suggests testthat, covr

Suggested by rules.

See at CRAN