Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
Spectral-based machine learning models have been increasingly deployed in chemometrics and spectroscopy, where predictive accuracy is as important as explainability. Current employed eXplainable Artificial Intelligence (XAI) methods are largely adapted from tabular or generic multivariate domains, assigning relevance to isolated spectral variables rather than to the chemically meaningful spectral zones. Widely adopted tools such as SHapley Additive exPlanations (SHAP), Permutation Feature Importance (PFI), and Variable Importance in Projection scores (VIP) were not designed for the physical continuity and high collinearity of spectral data, and their variable-level outputs require post-ho...
| Authors | Jose Vinicius Ribeiro, Rafael Figueira Goncalves, Fabio Luiz Melquiades et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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