eXtreme RuleFit
Produce rules & coefficients for the RuleFit model
Generate the design matrix from an eXtreme RuleFit model
Draw predictions from a RuleFit xrf model
Print an eXtreme RuleFit model
Summarize an eXtreme RuleFit model
Fit an eXtreme RuleFit model
Fit an eXtreme RuleFit model
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.