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>).
| 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 |
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