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>.">

randomMachines: An Ensemble Modeling using Random Machines (original) (raw)

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 ORCID iD [aut, cre], Anderson Ara ORCID iD [cte], Gabriel Ribeiro [cte]
Maintainer: Mateus Maia <mateus.maiamarques.2021 at mumail.ie>
License: MIT + file
NeedsCompilation: no
Materials: README
CRAN checks: randomMachines results

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