Estimating prevalence with precision and accuracy

Abstract:Unlike classification, whose goal is to estimate the class of each data point, quantification (or prevalence estimation) aims to estimate the distribution of classes in a dataset. An important task in prevalence estimation is to quantify the uncertainty in prevalence estimates. In this paper, we introduce Precise Quantifier (PQ), a Bayesian aggregative quantifier that achieves narrow prediction intervals with sufficient coverage (i.e., sufficient proportion of intervals containing the true prevalence). We find that PQ produces more precise prevalence estimates than existing methods as the discriminative power of the underlying classifier increases and as the validation-to-test size ratio increases. These empirical results suggest that PQ uses validation information more effectively to quantify uncertainty in prevalence estimates than existing approaches.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2507.06061 [stat.ML]

(or arXiv:2507.06061v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2507.06061 Focus to learn more arXiv-issued DOI via DataCite

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