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
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 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

Linking:

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