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>).
Package details |
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Author | Clement Benard [aut, cre], Marvin N. Wright [ctb, cph] |
Maintainer | Clement Benard <clement.benard5@gmail.com> |
License | GPL-3 |
Version | 0.3.3 |
URL | https://gitlab.com/drti/sirus |
Package repository | View on CRAN |
Installation |
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