Package: randomMachines 0.1.0

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>.

Authors:Mateus Maia [aut, cre], Anderson Ara [cte], Gabriel Ribeiro [cte]

randomMachines_0.1.0.tar.gz
randomMachines_0.1.0.zip(r-4.7-any)randomMachines_0.1.0.zip(r-4.6-any)randomMachines_0.1.0.zip(r-4.5-any)
randomMachines_0.1.0.tgz(r-4.6-any)randomMachines_0.1.0.tgz(r-4.5-any)
randomMachines_0.1.0.tar.gz(r-4.7-any)randomMachines_0.1.0.tar.gz(r-4.6-any)
randomMachines_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
randomMachines/json (API)

# Install 'randomMachines' in R:
install.packages('randomMachines', repos = c('https://mateusmaiads.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mateusmaiads/randommachines/issues

Datasets:

On CRAN:

Conda:

2.78 score 1 stars 12 scripts 222 downloads 9 exports 1 dependencies

Last updated from:5ba69d47f3. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK97
source / vignettesOK128
linux-releaseOK99
macos-releaseOK161
macos-oldrelOK172
windows-develOK81
windows-releaseOK69
windows-oldrelOK71
wasm-releaseOK70

Exports:brier_scorerandomMachinesRMSEsim_classsim_reg1sim_reg2sim_reg3sim_reg4sim_reg5

Dependencies:kernlab