randomMachines: An Ensemble Modeling using Random Machines
A novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) <doi:10.6339/21-JDS1014>, and regression (Ara A., et. al, 2021) <doi:10.1016/j.eswa.2022.117107> problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) <doi:10.6339/21-JDS1025>.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | kernlab, methods, stats |
| Published: | 2023-12-14 |
| DOI: | 10.32614/CRAN.package.randomMachines |
| Author: | Mateus Maia  [aut,
cre],
Anderson Ara
[cte],
Gabriel Ribeiro [cte] |
| Maintainer: | Mateus Maia <mateus.maiamarques.2021 at mumail.ie> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | randomMachines results |
Documentation:
| Reference manual: | randomMachines.pdf |
Downloads:
| Package source: | randomMachines_0.1.0.tar.gz |
| Windows binaries: | r-devel: randomMachines_0.1.0.zip, r-release: randomMachines_0.1.0.zip, r-oldrel: randomMachines_0.1.0.zip |
| macOS binaries: | r-release (arm64): randomMachines_0.1.0.tgz, r-oldrel (arm64): randomMachines_0.1.0.tgz, r-release (x86_64): randomMachines_0.1.0.tgz, r-oldrel (x86_64): randomMachines_0.1.0.tgz |
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