SMX explains spectral ML classifiers by summarizing expert zones with PCA, testing quantile predicates via perturbation, aggregating via directed graph centrality, and reconstructing thresholds back onto original spectra.
Notions of explain- ability and evaluation approaches for explainable ar- tificial intelligence.Information Fusion, 76:89–106,
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Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models
SMX explains spectral ML classifiers by summarizing expert zones with PCA, testing quantile predicates via perturbation, aggregating via directed graph centrality, and reconstructing thresholds back onto original spectra.