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 [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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