Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration

NeurIPS 2019 Meelis KullMiquel Perello NietoMarkus KängseppTelmo Silva FilhoHao SongPeter Flach

Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperature scaling, a method to learn a single corrective multiplicative factor for inputs to the last softmax layer... (read more)

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