scoring rule

A scoring rule is a method for evaluating the accuracy of probabilistic predictions by quantifying the difference between predicted probabilities and actual outcomes. Its aim is to incentivize predictors to provide forecasts that are as close as possible to the true probabilities, thereby enhancing the reliability and accuracy of the predictions. In fields such as decision support systems and risk management, the application of scoring rules can effectively optimize model selection and parameter tuning, improving the overall performance of the system.