machine-learning-metrics

Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts

This research addresses the bias in performance estimation for imbalanced classification, particularly regarding minority subconcepts within classes. It introduces a new practical utility-weighted evaluation metric, predicted-weighted balanced accuracy (pBA), which uses predicted posterior probabilities to correct this bias and provide a more accurate assessment.