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cs.LG2026
Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
Jose Vinicius Ribeiro, Rafael Figueira Goncalves, Fabio Luiz Melquiades +1
Spectral-based machine learning models have been increasingly deployed in chemometrics and spectroscopy, where predictive accuracy is as important as explainability. Current employ…
cs.LG2024★ 5 cited
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
Leonardo Arrighi, Luca Pennella, Gabriel Marques Tavares +1
Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop…