For spectroscopy and similar high-dimensional data, combining PCA with SHAP explanations lets you understand model decisions in terms of the original measurements—critical for clinical adoption where trust and interpretability matter.
SHAPCA combines dimensionality reduction and explainability techniques to make machine learning predictions on spectroscopy data interpretable and trustworthy. It maps explanations back to the original spectral bands rather than abstract features, helping clinicians and researchers understand why models make specific predictions on high-dimensional, correlated data.