Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features

) of 0.981 and a low mean absolute percentage error (MAPE) of 14.49%. Importantly, interpretability analysis using Shapley Additive Explanations (SHAP) provides physically interpretable statistical insights, revealing that high BCR suppresses specific capacitance through an insulating barrier effect, whereas lattice distortion in the filler material promotes ion transport. This study offers a robust, data-driven framework for optimizing composite electrode performance, demonstrating the potential of interpretable ML models for the rational design of advanced energy-storage materials.