Learning Prediction Intervals for Model Performance

Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model typically evaluated using test sets or periodic manual quality assessments, both which require laborious labeling. Automated prediction techniques aim to mitigate this burden, but potential inaccuracy lack trust in their predictions has prevented widespread adoption. We address core problem uncertainty with method compute intervals for performance. Our methodology uses transfer learning train an estimate the predictions. evaluate our approach across wide range drift conditions show substantial improvement over competitive baselines. believe result makes intervals, general, significantly more practical real-world use.