Single Number Metric

Single-number metrics aim to summarize complex datasets into a single, easily interpretable value for evaluating model performance or assessing phenomena like image quality or bias in word embeddings. Current research focuses on addressing limitations of existing metrics, such as their insensitivity to context or reliance on potentially misleading averaging techniques, by developing new metrics and algorithms, including Bayesian approaches and pairwise comparison frameworks. These efforts aim to improve the accuracy and interpretability of evaluations, leading to more reliable insights in diverse fields ranging from machine learning to psychometrics and beyond.