2021/07/17 by Dirk Tasche, Tasche, Dirk · 1 citation
Computer Science · Mathematics · #62F10 #68U99 #FOS: Computer and information sciences #FOS: Mathematics #Face and Expression Recognition #G.3 #I.5.2 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Statistical Methods and Inference #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2107.08209
openalex publication_date 2021/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For the binary prevalence quantification problem under prior probability shift, we determine the asymptotic variance of the maximum likelihood estimator. We find that it is a function of the Brier score for the regression of the class label on the features under the test data set distribution. This observation suggests that optimising the accuracy of a base classifier, as measured by the Brier score, on the training data set helps to reduce the variance of the related quantifier on the test data set. Therefore, we also point out training criteria for the base classifier that imply optimisation of both of the Brier scores on the training and the test data sets.