Accuracy Estimation

Accuracy estimation focuses on predicting a model's performance on unseen data without requiring ground truth labels, a crucial challenge in deploying machine learning models, especially in domains with limited labeled data or significant distribution shifts. Current research emphasizes developing label-free methods leveraging model outputs (confidence scores, prediction disagreements), gradients, and sample distances from the training distribution to estimate accuracy, often incorporating techniques like temperature scaling or conformal prediction. These advancements are vital for reliable model evaluation and deployment, particularly in high-stakes applications like medical diagnosis and autonomous systems, enabling more informed decisions about model suitability and trustworthiness.