Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings

Angéline Pouget, Mohammad Yaghini, Stephan Rabanser, Nicolas Papernot

International Conference on Machine Learning 2025 · Oral

In the rapidly evolving landscape of machine learning, deploying models into production environments presents a unique set of challenges, particularly when the operational data distribution differs from the training or evaluation data. Mohammad Yaghini, alongside collaborators Angéline Pouget, Stephan Rabanser, and Nicolas Papernot, presented a compelling solution to this pervasive problem at ICML 2025 with their work on the **Suitability Filter**. This talk addresses a fundamental question for any company looking to utilize a pre-trained model: "Is this model suitable for my specific user data?" The core issue arises because model providers typically offer performance guarantees based on their own evaluation datasets, which may not translate directly to a user's potentially different data distribution, especially when that user data is unlabeled.

AI review

A competent and honest application of established statistical machinery — non-inferiority testing, calibration theory, uncertainty estimation — to the practically important problem of evaluating pre-trained classifiers on unlabeled target data. The framework is sensible and the empirical validation is unusually broad. But the theoretical contribution is thinner than the presentation suggests: the core ideas are recombinations of known tools, the delta-calibration condition essentially assumes away the hardest part of the problem, and the 100% unsuitability detection claim at a 3-4% threshold…