The predictions of mean and standard deviations for 𝑇 ! of dataset 2 can be obtained from the six different ML models. The metrics for mean and standard deviations are shown in Figure . The MAE, RMSE, and 𝑅 # are used for the evaluation of the accuracy of mean of 𝑇 ! in Figure (a), (b), and (c). The GPR has better performance with the least errors and its 𝑅 # is more than 0.8 with the highest accuracy. 𝑅 # of Ensemble, MVE, BNN and EDL are between 0.75 to 0.80, and MCD has the largest errors with 𝑅 # less than 0.70. Then the metrics for UQ are shown in Figure (d), (e), and (f). The BNN and MCD can predict the global relationship between actual errors and uncertainty, with Spearman's rank correlation coefficient more than 0.25, and GPR and EDL of that is close to zero, which means that the ranks of actual errors and standard deviations are not related to each other. For calibration, like the calibration of test data, due to GPR can capture the scale of standard deviations, the GPR can obtain the best metric for confidence. Other models are overconfident with high confidence as shown in Figure (e). Furthermore, in the process of sparsification, data points with significant uncertainties are systematically removed. Subsequently, the remaining points are utilized to compute the RMSE, as depicted in Figure (f). The RMSE of all models except GPR decrease individually when sparsification fraction is less than 0.8. The RMSE oscillates violently at very large sparsification fraction with a small amount of data, which is usually not considered for the evaluation.