sirus: 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. (2021), Electron. J. Statist., 15:427-505 <DOI:10.1214/20-EJS1792> for classification, and Benard C., Biau G., da Veiga S., Scornet E. (2021), AISTATS, PMLR 130:937-945 <>, for regression. This R package is a fork from the project ranger (<>).

Getting started

Package details

AuthorClement Benard [aut, cre], Marvin N. Wright [ctb, cph]
MaintainerClement Benard <>
Package repositoryView on CRAN
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sirus documentation built on June 13, 2022, 5:07 p.m.