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>.

Getting started

Package details

AuthorDr. Md Yeasin [aut], Dr. Ranjit Kumar Paul [aut, cre], Dr. Dipanwita Haldar [aut]
MaintainerDr. Ranjit Kumar Paul <ranjitstat@gmail.com>
LicenseGPL-3
Version0.1.1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("EEML")

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EEML documentation built on Sept. 11, 2024, 6:54 p.m.