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 <http://proceedings.mlr.press/v130/benard21a>, for regression. This R package is a fork from the project ranger (<https://github.com/imbs-hl/ranger>).

Getting started

Package details

AuthorClement Benard [aut, cre], Marvin N. Wright [ctb, cph]
MaintainerClement Benard <clement.benard5@gmail.com>
LicenseGPL-3
Version0.3.3
URL https://gitlab.com/drti/sirus
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("sirus")

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sirus documentation built on June 13, 2022, 5:07 p.m.