Figure shows a fold-averaged picture of the metrics by protein and by model type (DL or input-naïve baseline). Visual inspection suggested that the F1-score, accuracy, and possibly balanced accuracy were affected by the baseline data imbalance. To quantify this finding, the model in Equation was fitted to the baseline performance metrics. According to Table of the Supporting Information, the strategy term was significant (type 3 ANOVA, p < 10 -16 , p < 10 -16 and 5.61 • 10 -11 ) for those three metrics, and non-significant in AUROC and MCC (p = 0.91 and 0.82). Based on this, metrics were divided in two types: (1) imbalancesensitive, if the baseline was different between strategies, and (2) imbalance-insensitive, if the baseline was constant.