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Model-Free Local Recalibration of Neural Networks
March 12, 2024, 4:42 a.m. | R. TorresUniversity of Bras\'ilia, D. J. NottNational University of Singapore, S. A. SissonUniversity of New South Wales, Sydney, T. RodriguesUniversi
cs.LG updates on arXiv.org arxiv.org
Abstract: Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been much recent work on "recalibration" of predictive distributions for ANNs, so that forecast probabilities for events of interest are consistent with certain frequency evaluations of them. Uncalibrated probabilistic forecasts are of limited use for many important decision-making tasks. To address this issue, we propose a localized recalibration of ANN predictive distributions using …
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