How to evaluate uncertainty estimates in machine learning for regression?

As neural networks become more popular, the need for accompanying uncertainty estimates increases. There are currently two main approaches to test the quality of these estimates. Most methods output a density. They can be compared by evaluating their loglikelihood on a test set. Other methods output...

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Veröffentlicht in:	Neural networks 2024-05, Vol.173, p.106203-106203, Article 106203
Hauptverfasser:	Sluijterman, Laurens, Cator, Eric, Heskes, Tom
Format:	Artikel
Sprache:	eng
Schlagworte:	Bootstrap Computer Simulation Dropout Machine Learning Neural networks Neural Networks, Computer Regression Uncertainty
Online-Zugang:	Volltext
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