Stable and Interpretable RUle Set

A regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with a predictivity close to random forests. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS aggregates the forest structure: the most frequent nodes of the forest are selected to form a stable rule ensemble model. The algorithm is fully described in the following articles: Benard C., Biau G., da Veiga S., Scornet E. (2019) for classification, and Benard C., Biau G., da Veiga S., Scornet E. (2020) for regression. This R package is a fork from the project ranger (< https://github.com/imbs-hl/ranger>).


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("sirus")

0.3.1 by Clement Benard, a month ago


https://gitlab.com/drti/sirus


Report a bug at https://gitlab.com/drti/sirus/-/issues


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


Authors: Clement Benard [aut, cre] , Marvin N. Wright [ctb, cph]


Documentation:   PDF Manual  


GPL-3 license


Imports Rcpp, Matrix, ROCR, ggplot2, glmnet

Suggests survival, testthat

Linking to Rcpp, RcppEigen


See at CRAN