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-4.7)EEML_0.1.1.zip(r-4.6)EEML_0.1.1.zip(r-4.5)
EEML_0.1.1.tgz(r-4.6-any)EEML_0.1.1.tgz(r-4.5-any)
EEML_0.1.1.tar.gz(r-4.7-any)EEML_0.1.1.tar.gz(r-4.6-any)
EEML_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
EEML/json (API)

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

On CRAN:

Conda:

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

1.00 score 174 downloads 3 exports 4 dependencies

Last updated from:c8adedecd7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK103
source / vignettesOK178
linux-release-x86_64OK89
macos-release-arm64OK65
macos-oldrel-arm64OK101
windows-develOK81
windows-releaseOK59
windows-oldrelOK67
wasm-releaseOK90

Exports:EEMLModelSelWeight

Dependencies:MCSmetaheuristicOpttopsisWeightedEnsemble