About Anaconda Help Download Anaconda

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.

Click on a badge to see how to embed it in your web page
badge
https://anaconda.org/r/r-xrf/badges/version.svg
badge
https://anaconda.org/r/r-xrf/badges/latest_release_date.svg
badge
https://anaconda.org/r/r-xrf/badges/latest_release_relative_date.svg
badge
https://anaconda.org/r/r-xrf/badges/platforms.svg
badge
https://anaconda.org/r/r-xrf/badges/license.svg
badge
https://anaconda.org/r/r-xrf/badges/downloads.svg

© 2024 Anaconda, Inc. All Rights Reserved. (v4.0.5) Legal | Privacy Policy