More informative than the performance averages is a look at how the two methodologically distinct approaches perform on the individual tasks. For runs with a support set size of 16 samples, the Pearson's correlation coefficient r for the ROC-AUC values was 0.495. With larger support set sizes, the Pearson's r increased slightly, up to r=0.555 for . These 𝑛 𝑡𝑟𝑎𝑖𝑛 = 128 results indicate a moderate correlation between the performance of the single-task and meta-learning approaches. As apparent from the scatter plots presented in Figure , a subset of tasks poses a similar hardness level to the prototypical network and the random forest (i.e., those dots placed in proximity of the diagonal), whereas other tasks are perceived as more challenging by either method. Interestingly, large spreads in ROC-AUC values are observed for the prototypical network. For example, for tasks for which the random forest reaches ROC-AUC values of around 0.60, the values for the prototypical network range from 0.57 to 0.86 (indicated in Figure by the red dots and arrows). This means many tasks pose a comparable hardness to the random forest but distinct hardness to the prototypical network. An explanation for the disparity and gaps could be differences in how well the source tasks represent the test tasks. It is intuitive to hypothesize that the prototypical network performs particularly well on test tasks sufficiently closely related to the source tasks so that knowledge can be transferred.