Confidence-based Estimators for Predictive Performance in Model Monitoring

TL;DR

This paper investigates confidence-based estimators for monitoring model performance when ground truth labels are delayed or unavailable, revealing that the naive Average Confidence method is theoretically unbiased and often competitive.

Contribution

The paper provides a theoretical analysis of the Average Confidence estimator, demonstrating its unbiasedness and consistency, and empirically compares it with more complex estimators.

Findings

AC estimator is unbiased under certain conditions

AC often outperforms complex estimators in practice

Estimator performance is case-dependent

Abstract

After a machine learning model has been deployed into production, its predictive performance needs to be monitored. Ideally, such monitoring can be carried out by comparing the model's predictions against ground truth labels. For this to be possible, the ground truth labels must be available relatively soon after inference. However, there are many use cases where ground truth labels are available only after a significant delay, or in the worst case, not at all. In such cases, directly monitoring the model's predictive performance is impossible. Recently, novel methods for estimating the predictive performance of a model when ground truth is unavailable have been developed. Many of these methods leverage model confidence or other uncertainty estimates and are experimentally compared against a naive baseline method, namely Average Confidence (AC), which estimates model accuracy as the average of confidence scores for a given set of predictions. However, until now the theoretical properties of the AC method have not been properly explored. In this paper, we try to fill this gap by reviewing the AC method and show that under certain general assumptions, it is an unbiased and consistent estimator of model accuracy with many desirable properties. We also compare this baseline estimator against some more complex estimators empirically and show that in many cases the AC method is able to beat the others, although the comparative quality of the different estimators is heavily case-dependent.