Calibration Set

Calibration sets are subsets of data used to refine machine learning models, ensuring accurate prediction intervals or reliable performance metrics, rather than just point predictions. Research currently focuses on optimizing the size and composition of these sets, exploring techniques like overlapping training and calibration data, and investigating their impact across diverse model architectures, including large language models and conformal predictors. Effective calibration is crucial for improving the reliability and trustworthiness of machine learning systems across various applications, from surgical augmented reality to agricultural robotics and high-throughput phenotyping. The goal is to develop robust calibration strategies that minimize the need for extensive labeled data and maintain accuracy even under distribution shifts.