Package: EEML 0.1.1

EEML: Ensemble Explainable Machine Learning Models

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>.

Authors:Dr. Md Yeasin [aut], Dr. Ranjit Kumar Paul [aut, cre], Dr. Dipanwita Haldar [aut]

EEML_0.1.1.tar.gz
EEML_0.1.1.zip(r-4.5)EEML_0.1.1.zip(r-4.4)EEML_0.1.1.zip(r-4.3)
EEML_0.1.1.tgz(r-4.4-any)EEML_0.1.1.tgz(r-4.3-any)
EEML_0.1.1.tar.gz(r-4.5-noble)EEML_0.1.1.tar.gz(r-4.4-noble)
EEML_0.1.1.tgz(r-4.4-emscripten)EEML_0.1.1.tgz(r-4.3-emscripten)
EEML.pdf |EEML.html
EEML/json (API)

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

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 249 downloads 3 exports 4 dependencies

Last updated 4 months agofrom:c8adedecd7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-winOKNov 19 2024
R-4.5-linuxOKNov 19 2024
R-4.4-winOKNov 19 2024
R-4.4-macOKNov 19 2024
R-4.3-winOKNov 19 2024
R-4.3-macOKNov 19 2024

Exports:EEMLModelSelWeight

Dependencies:MCSmetaheuristicOpttopsisWeightedEnsemble