Scoring Rule

Scoring rules are evaluation metrics that incentivize accurate probabilistic predictions, finding applications in diverse fields from machine learning to voting systems. Current research focuses on developing and refining scoring rules for specific tasks, such as multivariate regression, survival analysis, and probabilistic classification, often addressing limitations of existing methods like the Brier score or logarithmic loss, particularly concerning handling of uncertainty and extreme events. These advancements improve model selection, uncertainty quantification, and the design of robust prediction systems across various domains, leading to more reliable and informative predictions.