To evaluate the performance of all 1,312 models and 96 average models, both F1-score and ΔF1-score are calculated from Tables S1, S2, which are displayed in Figure 7. F1-score is the key criterion used to select the best model during the modeling training with cross-validation, while ΔF1-score used to inspect the possible overfitting or underfitting of model is obtained with Equation (7). The scatter plot of ΔF1-score vs. F1-score (test set) demonstrates that all the average models and most of individual models have the ΔF1-score lower than 0.04, indicating that the performance on the test set and in the cross-validation is quite similar. Hence all these average models and most of individual models are probably robust without the apparent overfitting or underfitting from this perspective.