3 citations · 3 across the 5 of their papers we have counts for
3 papers · 1 filter
Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
Eduardo Aguilar-Bejarano, Daniel Lea, Karthikeyan Sivakumar +8
Helix is an open-source, extensible, Python-based software framework to facilitate reproducible and interpretable machine learning workflows for tabular data. It addresses the grow…
EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python
Aayush Kumar, Jimiama Mafeni Mase, Divish Rengasamy +4
This paper presents an open-source Python toolbox called Ensemble Feature Importance (EFI) to provide machine learning (ML) researchers, domain experts, and decision makers with ro…
Mechanistic Interpretation of Machine Learning Inference: A Fuzzy Feature Importance Fusion Approach
Divish Rengasamy, Jimiama M. Mase, Mercedes Torres Torres +3
With the widespread use of machine learning to support decision-making, it is increasingly important to verify and understand the reasons why a particular output is produced. Altho…