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

, , , , , and . Mach. Learn., 112 (9): 3211-3260 (September 2023)

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Rate-Oriented Point-Wise Confidence Bounds for ROC Curves., , and . ECML/PKDD (2), volume 8725 of Lecture Notes in Computer Science, page 404-421. Springer, (2014)Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy., and . ECML/PKDD (2), volume 8725 of Lecture Notes in Computer Science, page 18-33. Springer, (2014)Shift Happens: Adjusting Classifiers., , and . ECML/PKDD (2), volume 11907 of Lecture Notes in Computer Science, page 55-70. Springer, (2019)Fast approximate hierarchical clustering using similarity heuristics., and . BioData Min., (2008)Classifier Calibration: How to assess and improve predicted class probabilities: a survey., , , , , and . CoRR, (2021)Precision-Recall-Gain Curves: PR Analysis Done Right., and . NIPS, page 838-846. (2015)Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration., , , , , and . NeurIPS, page 12295-12305. (2019)The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data., , , , , , , , , and 1 other author(s). CoRR, (2016)LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion., , and . ICMLA, page 74-79. IEEE, (2014)Subgroup Discovery with Proper Scoring Rules., , , and . ECML/PKDD (2), volume 9852 of Lecture Notes in Computer Science, page 492-510. Springer, (2016)