randomMachines
0.1.1An Ensemble Modeling using Random Machines
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-05-1213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- WARNING2026-05-1112 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 50%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.02023-12-14
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-12-14
- Total releases
- 2 / 3 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 178 KB / 3 files
- Download size
- 196 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("randomMachines")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
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