162 citations · 215 across the 9 of their papers we have counts for
3 papers · 1 filter
Classifier Calibration: A survey on how to assess and improve predicted class probabilities
Telmo Silva Filho, Hao Song, Miquel Perello-Nieto +3
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the…
Shift Happens: Adjusting Classifiers
Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull
Minimizing expected loss measured by a proper scoring rule, such as Brier score or log-loss (cross-entropy), is a common objective while training a probabilistic classifier. If the…
Instance-based Label Smoothing For Better Calibrated Classification Networks
Mohamed Maher, Meelis Kull
Label smoothing is widely used in deep neural networks for multi-class classification. While it enhances model generalization and reduces overconfidence by aiming to lower the prob…