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cs.LG2024
Improving Calibration by Relating Focal Loss, Temperature Scaling, and Properness
Viacheslav Komisarenko, Meelis Kull
Proper losses such as cross-entropy incentivize classifiers to produce class probabilities that are well-calibrated on the training data. Due to the generalization gap, these class…
cs.LG2024
Cautious Calibration in Binary Classification
Mari-Liis Allikivi, Joonas Järve, Meelis Kull
Being cautious is crucial for enhancing the trustworthiness of machine learning systems integrated into decision-making pipelines. Although calibrated probabilities help in optimal…