In Polynesian phonology and morphology, passives have traditionally been analyzed in two steps: (1) by assigning a single default pattern, which has been characterized as fully predictable and productive; and (2) by lexicalizing the remaining patterns, which have been assumed to be unpredictable. We overturn this assumption of unpredictability here for the Hawaiian passive by finding a computational model capable of predicting more than just the single default. This is achieved by learning statistical regularities over active verbs which predict their passive forms. Whether Hawaiian speakers are able to make use of these model predictions in a productive way can now be tested behaviorally.