Average performance metrics across training, validation, and held-out test sets for all class-balancing ensembles and test set performance for top-10 ensembles, for all routes/modes.

The average performance is calculated as the mean over 50 iterations for the training, validation, and test sets, and over 10 iterations for the top-10 ensembles. Except for ROC-AUC and PR-AUC, all other metrics were computed at >0.5 probability threshold. The top-10 ensembles were selected by ranking each route/mode class-balancing ensembles (n = 50) based on the average of four metrics—AUC, PR-AUC, PPV/Precision, and adjusted Brier score (1—actual score)—computed on the test sets, and then selecting the best 20% ranked ensembles. Brier scores range from 0 (best performance) to 1 (worst performance), while MCC values range from +1 (best performance) to -1 (worst performance). ± values indicate standard deviation from the mean. Values in square brackets indicate the worst and best performing ensembles, respectively. S4 Dataset provides the average performance metrics (and their standard deviations) across the training, validation, and held-out test sets, as well as the percentage of positive class instances for each route/mode.