Estimating Performance for Safe Deployment of Machine Learning Models

arXiv:2610.29262v1 Announce Type: new Abstract: The main challenge in applying deep neural networks to real-world scenarios lies in distributional discrepancies between training and test datasets, often caused by variations in data acquisition conditions. Assessing the performance of pre-trained models without labels is crucial for safety, but this is complicated by deep models' overly optimistic probabilistic outputs, often exacerbated by class imbalances, leading to unreliable predictions. To address these issues, we introduce MOVAL, a novel MOdel eVALuation tool that estimates performance using unlabeled test data. MOVAL incorporates class-wise confidence calibration to adjust model outputs, allowing for the estimation of metrics beyond accuracy and mitigating the detrimental effects of class imbalance on confidence calibration. Additionally, we propose an ensemble approach that combines multiple calibrated confidence scores for a more robust solution. We validate MOVAL on natural and medical image datasets with various acquisition shifts, covering tasks from classification to segmentation. It achieves state-of-the-art results across evaluation metrics such as accuracy, F1-Score, AUC, and more. Furthermore, we demonstrate that MOVAL provides accurate performance estimations at the class, instance, and pixel levels, ensuring the transparent and safe deployment of machine learning models. We provide MOVAL as a deployment tool for monitoring and estimating performance under a range of domain shifts.

Abstract:The main challenge in applying deep neural networks to real-world scenarios lies in distributional discrepancies between training and test datasets, often caused by variations in data acquisition conditions. Assessing the performance of pre-trained models without labels is crucial for safety, but this is complicated by deep models' overly optimistic probabilistic outputs, often exacerbated by class imbalances, leading to unreliable predictions. To address these issues, we introduce MOVAL, a novel MOdel eVALuation tool that estimates performance using unlabeled test data. MOVAL incorporates class-wise confidence calibration to adjust model outputs, allowing for the estimation of metrics beyond accuracy and mitigating the detrimental effects of class imbalance on confidence calibration. Additionally, we propose an ensemble approach that combines multiple calibrated confidence scores for a more robust solution. We validate MOVAL on natural and medical image datasets with various acquisition shifts, covering tasks from classification to segmentation. It achieves state-of-the-art results across evaluation metrics such as accuracy, F1-Score, AUC, and more. Furthermore, we demonstrate that MOVAL provides accurate performance estimations at the class, instance, and pixel levels, ensuring the transparent and safe deployment of machine learning models. We provide MOVAL as a deployment tool for monitoring and estimating performance under a range of domain shifts.

Machine Learning (cs.LG)

arXiv:2610.29262 [cs.LG]

(or arXiv:2610.29262v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.29262 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

aiqbench analysis

Estimating Performance for Safe Deployment of Machine Learning Models

Related coverage