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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | EEML_0.1.1.tar.gz |
18.8 KiB |
0.1.1 |
rolling linux/noble R-4.5 | EEML_0.1.1.tar.gz |
18.6 KiB |
0.1.1 |
rolling source/ R- | EEML_0.1.1.tar.gz |
3.0 KiB |
0.1.1 |
latest linux/jammy R-4.5 | EEML_0.1.1.tar.gz |
18.8 KiB |
0.1.1 |
latest linux/noble R-4.5 | EEML_0.1.1.tar.gz |
18.6 KiB |
0.1.1 |
latest source/ R- | EEML_0.1.1.tar.gz |
3.0 KiB |
0.1.1 |
2026-04-26 source/ R- | EEML_0.1.1.tar.gz |
3.0 KiB |
0.1.1 |
2026-04-23 source/ R- | EEML_0.1.1.tar.gz |
3.0 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | EEML_0.1.1.zip |
21.3 KiB |
0.1.1 |
2025-04-20 source/ R- | EEML_0.1.1.tar.gz |
3.0 KiB |