A View on Model Misspecification in Uncertainty Quantification
Yuko Kato (TU Delft - Electrical Engineering, Mathematics and Computer Science)
David M.J. Tax (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Marco Loog (Radboud Universiteit Nijmegen, TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecification is reliable or not. In this paper, we argue that model misspecification should receive more attention, by providing thought experiments and contextualizing these with relevant literature.