The mean di↵erence of BEDROC(20) metrics for each possible combination of models is shown in 10. Di↵erences in terms of AUROC follow the same behavior but of a smaller magnitude (see Figure of Supplementary Information). The most remarkable di↵erences can be seen on random models versus database-based and intermediate models. There are also relevant di↵erences on performance between clustering-based cross-validation and the rest of strategies. families, while controlling for the data redundancy issue as the number of sca↵olds shared between train and test decreases from 2,676 (random) to 275. It also leads to less optimistic performance estimates than the random strategy, whereas the models still retain predictive power. Therefore, the clustering strategy is chosen as our reference, in line with previous studies.