Navigating Metric Elicitation in Machine Learning

This chapter explores the complexities of metric elicitation in machine learning, focusing on how traditional metrics often fall short in real-world applications. It discusses the evolution of performance evaluation methods, particularly the use of the confusion matrix and the importance of incorporating expert-defined trade-offs. Additionally, the chapter emphasizes innovative approaches like pairwise preference to gather feedback for optimizing complex metrics that are better aligned with specific applications.