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The practice of uncertainty quantification (UQ) validation, notably inmachine learning for the physico-chemical sciences, rests on several graphicalmethods (scattering plots, calibration curves, reliability diagrams andconfidence curves) which explore complementary aspects of calibration, withoutcovering all the desirable ones. For instance, none of these methods deals withthe reliability of UQ metrics across the range of input features (adaptivity).Based on the complementary concepts of consistency and adaptivity, the toolboxof common validation methods for variance- and intervals- based UQ metrics isrevisited with the aim to provide a better grasp on their capabilities. Thisstudy is conceived as an introduction to UQ validation, and all methods arederived from a few basic rules. The methods are illustrated and tested onsynthetic datasets and representative examples extracted from the recentphysico-chemical machine learning UQ literature.

Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity concepts