Chapter 4 provides a detailed coverage of methods for the evaluation of predictive models: the methods applicable to regression models implementing estimation biomarkers, as well as methods evaluating binary and multiclass classification models. Discussion of resampling techniques is accompanied by accentuating the danger of information leakage and by emphasizing the paramount importance of avoiding internal validation. Discussion of metrics for the evaluation of classification biomarkers includes the issue of proper and improper interpretation of sensitivity and specificity, illustrated by an example of a screening biomarker targeting a population with low prevalence of the tested disease. For such biomarkers, positive predictive value may be unacceptably low even when the biomarker has a very high specificity and sensitivity. Discussed in this chapter are also misclassification costs and incorporating them into cost-sensitive classification.