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Ming-Xing Zhang

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Progressive and Interpretable Explanations for Machine Learning Models on Spectroscopy Data

This study proposes an explainable machine learning pipeline that integrates feature extraction techniques, namely Sparse PCA, PLS, and MCR-ALS, with Shapley Additive exPlanations (SHAP) for post hoc interpretation, while preserving the ability to project explanations back into the original input space so that practiti...

Ming-Xing Zhang · 0 citations