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This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE).
Verification of forecasts expressed in terms of probability
Glen W. Brier · 1950
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Philip Dawid · 1982
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The comparison and evaluation of forecasters
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M Elter, R Schulz-Wendtland, and T Wittenberg · 2007
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Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E. Raftery · 2007
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Isotone optimization in r: Pool-adjacent-violators algorithm (pava) and active set methods
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Obtaining well calibrated probabilities using bayesian binning
M. P. Naeini, G. F. Cooper, and M. Hauskrecht · 2015
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Measuring calibration in deep learning
Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
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High-performance medicine: the convergence of human and artificial intelligence
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Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, and Thomas Bo Schön · 2019
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Calibration tests in multi-class classification: A unifying framework
David Widmann, Fredrik Lindsten, and Dave Zachariah · 2019
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A survey of deep learning techniques for autonomous driving
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, and Gigel Macesanu · 2020
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Stable reliability diagrams for probabilistic classifiers
Timo Dimitriadis, Tilmann Gneiting, and Alexander I. Jordan · 2021
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