On the other hand, semi_resampling outperformed no_resampling in four out of five metrics (Tukey's method, p < 0.05, Figure of the Supporting Information), which supported data augmentation usefulness even if the data balance in the test set differed from that of the training set. This was consistent with the observation that the main influence on the predicted actives ratio in the test set were their actual ratios in the test set instead of the original ratios in the training set. Combined with the less skewed distributions of predicted active ratios of semi_resampling against no_resampling (Figure ), we recommend semi_resampling for future studies.