Kull, Meelis

Classifier calibration: a survey on how to assess and improve predicted class probabilities

Silva Filho, T., Song, H., Perello-Nieto, M., Santos-Rodriguez, R., Kull, M., & Flach, P. (2023) · Machine Learning

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 228

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

Kull, M., Perello-Nieto, M., Kängsepp, M., Filho, T. S., Song, H., & Flach, P. (2019) · arXiv

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 206

Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration

Kull, M., Silva Filho, T. M., & Flach, P. (2017) · Electronic Journal of Statistics

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 111

Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration

Kull, M., & Flach, P. (2015) · Lecture Notes in Computer Science

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 43