Uncertainty Measure

Uncertainty quantification (UQ) in machine learning aims to estimate the reliability of model predictions, addressing the "black box" nature of many algorithms and improving decision-making in high-stakes applications. Current research focuses on developing and comparing various UQ measures, including entropy-based methods, variance-based approaches, and distance-based metrics, often within the context of specific model architectures like deep neural networks and ensembles. These advancements are crucial for enhancing the trustworthiness and safety of machine learning systems across diverse fields, from medical diagnosis to autonomous driving, by providing a quantitative assessment of prediction uncertainty.