Learning Prediction Intervals for Model Performance

TL;DR

This paper introduces a transfer learning-based method to compute prediction intervals for model performance, addressing uncertainty and improving practical reliability in performance prediction on unlabeled data.

Contribution

It presents a novel approach to quantify uncertainty in performance predictions using transfer learning, enhancing trust and accuracy over existing methods.

Findings

Substantial improvement over baseline methods

Effective across various drift conditions

Enhances practical deployment of performance prediction

Abstract

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