CML models are typically more interpretable than deep learning models owing to their transparent architecture and ability to identify dominant features. Feature importance analysis in CML models elucidates the structure-property relationships, thereby enhancing their interpretability. Here, we employed the SHapley Additive exPlanations (SHAP) analysis , a method that can be used to explain the correlation between input features and output predictions of CML models. First, we evaluated the important features for E f prediction based on the mean absolute SHAP values, where higher values indicate greater feature importance for the specific prediction. As shown in Figure -S19, the most important features identified across different CML models are consistent for any given dataset. For instance, in the MP dataset, the group number of atom X, the covalent radius of atom A, and the electronegativity of atom B are consistently ranked as the top three most important features by all four CML models in predicting E f (see Figure ). This consistency highlights the reliability and robustness of CML models in deciphering the structure-property relationships within a specific dataset, demonstrating that the choice of model does not affect the identification of important features when the same dataset is used. Similar to E f , different CML models underscore the identical ranking of feature importance for predicting E g in a specific dataset (see Figure -S22). For example, the electron affinity of atom X and the valence electrons of atom B were selected by all four CML models as the top two features for E g prediction in the MP dataset.