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-any)EEML_0.1.1.zip(r-4.6-any)EEML_0.1.1.zip(r-4.5-any)
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✨
DESCRIPTION
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 272 downloads 3 exports 4 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK93
source / vignettesOK127
linux-releaseOK83
macos-releaseOK113
macos-oldrelOK80
windows-develOK59
windows-releaseOK60
windows-oldrelOK56
wasm-releaseOK73

Exports:EEMLModelSelWeight

Dependencies:MCSmetaheuristicOpttopsisWeightedEnsemble