It is important to know the relation between the individual task hardness components, e.g., to understand whether they capture similar or distinct information. Figure After establishing that the individual task hardness components cover different information, we investigated whether their combination yields a stronger correlation with the performance of the prototypical network. In Figure , we visualize the relationship between each hardness concept and the improvement in the performance of the prototypical network (considering a support set size of 128 training samples for each test task; k=10). For EXT_CHEM and the performance of the prototypical network (measured as ΔAUPRC), Pearson's r was 0.63 (Figure ). For EXT_PROT and ΔAUPRC, the correlation was weaker (Pearson's r=0.34;