I am using HPSPLIT and working with very highly imbalanced database (3% had "event"). In this case, events are considered extremely costly so we are willing to trade off specificity (false positives) for sensitivity (false negatives). I have tried balancing the data (undersample non-events), but we are still missing too many events.
Is there a more direct way of modifying the model to reflect the "high cost" of missing events? Priors, cost weights, etc?
Thank you.