Ensemble Explainable Machine Learning Models
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 3
- Examples that run
- 33%1 run in a plain check · 2 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Installing EEML adds 4 packages (0 with compiled code, 69 KB of source tarballs). Heaviest: metaheuristicOpt 52 KB, MCS 13 KB, WeightedEnsemble 2.3 KB.
Code & Tests
People & History
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 0.1.1Latest
- 0.1.02024-07-14
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-07-14
- Total releases
- 2 / 2 yrs
- License
- GPL-3 OSI
- Download size
- 3.0 KB
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