Chapter 5 starts with an analysis of the classification metrics presented in Chapter 4, outlining their strengths and weaknesses. It then presents more advanced metrics such as Cohen’s kappa, Youden’s index, and likelihood ratios. This is followed by a discussion about data and classifier complexities such as the class imbalance problem and classifier uncertainty that require particular scrutiny to ensure that the results are trustworthy. The chapter concludes with a detailed discussion of ROC analysis to complement its introduction in Chapter 4, and a presentation of other visualization metrics.