Selective Prediction

2 articles

Artificial Intelligence 11 Sep 2026 11 min read

Let Classifiers Abstain with Selective Prediction

Let Classifiers Abstain with Selective Prediction A classifier does not have to answer every case. In many systems, forcing a prediction on an ambiguous input is worse than sending that input to a human, requesting more information, or falling back to a safer workflow. Selective prediction gives a model that option. The classifier produces its usual prediction, but the system accepts it only when a selection rule considers the case reliable enough. Otherwise, the system abstains.

Artificial Intelligence Machine Learning

Artificial Intelligence 04 Sep 2026 10 min read

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.

Artificial Intelligence Machine Learning