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We introduced a novel ensemble-based explainable machine learning model using Model Confidence Set (MCS) and two stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The model combined the predictive capabilities of different machine-learning models and integrates the interpretability of explainability methods. To develop the proposed algorithm, a two-stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework was employed. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
| Version: | 0.1.1 |
| Imports: | stats, MCS, WeightedEnsemble, topsis |
| Published: | 2024-08-01 |
| DOI: | 10.32614/CRAN.package.EEML |
| Author: | Dr. Md Yeasin [aut], Dr. Ranjit Kumar Paul [aut, cre], Dr. Dipanwita Haldar [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | EEML results |
| Reference manual: | EEML.pdf |
| Package source: | EEML_0.1.1.tar.gz |
| Windows binaries: | r-devel: EEML_0.1.1.zip, r-release: EEML_0.1.1.zip, r-oldrel: EEML_0.1.1.zip |
| macOS binaries: | r-release (arm64): EEML_0.1.1.tgz, r-oldrel (arm64): EEML_0.1.1.tgz, r-release (x86_64): EEML_0.1.1.tgz, r-oldrel (x86_64): EEML_0.1.1.tgz |
| Old sources: | EEML archive |
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