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In the realm of machine learning and statistical modeling, practitionersoften work under the assumption of accessible, static, labeled data forevaluation and training. However, this assumption often deviates from realitywhere data may be private, encrypted, difficult- to-measure, or unlabeled. Inthis paper, we bridge this gap by adapting the Hui-Walter paradigm, a methodtraditionally applied in epidemiology and medicine, to the field of machinelearning. This approach enables us to estimate key performance metrics such asfalse positive rate, false negative rate, and priors in scenarios where noground truth is available. We further extend this paradigm for handling onlinedata, opening up new possibilities for dynamic data environments. Ourmethodology involves partitioning data into latent classes to simulate multipledata populations (if natural populations are unavailable) and independentlytraining models to replicate multiple tests. By cross-tabulating binaryoutcomes across ensemble categorizers and multiple populations, we are able toestimate unknown parameters through Gibbs sampling, eliminating the need forground-truth or labeled data. This paper showcases the potential of ourmethodology to transform machine learning practices by allowing for accuratemodel assessment under dynamic and uncertain data conditions.

Unlocking Unlabeled Data: Ensemble Learning with the Hui- Walter Paradigm for Performance Estimation in Online and Static Settings