Selective Prediction

Design Model Abstention for Uncertain Predictions

A model does not have to make a decision on every input. In many applications, forcing a prediction is exactly what turns an uncertain case into an expensive mistake. Consider a classifier that routes support tickets to billing, account, or technical teams. Most tickets are straightforward, but some are vague or combine several problems. If the application automatically accepts every prediction, the model must act even when its evidence is weak. A better system can automate clear cases and send uncertain ones to a fallback such as human review.