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>).
Version: 0.3.3 Depends: R (≥ 3.6) Imports: Rcpp (≥ 0.11.2), Matrix, ROCR, ggplot2, glmnet LinkingTo: Rcpp, RcppEigen Suggests: survival, testthat, ranger Published: 2022-06-13 DOI: 10.32614/CRAN.package.sirus Author: Clement Benard [aut, cre], Marvin N. Wright [ctb, cph] Maintainer: Clement Benard <clement.benard5 at gmail.com> BugReports: https://gitlab.com/drti/sirus/-/issues License: GPL-3 URL: https://gitlab.com/drti/sirus NeedsCompilation: yes Materials: README CRAN checks: sirus results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sirus to link to this page.
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