The paper addresses the challenge of evaluating classifiers in the presence of missing labels, particularly in scenarios where data is Missing Not At Random (MNAR). It introduces a multiple imputation method to derive robust metrics such as precision, recall, and ROC-AUC, providing both point estimates and predictive distributions. The authors demonstrate the accuracy of these distributions and establish their Gaussian nature, along with finite-sample convergence bounds and a robustness proof under a realistic error model.