2023/03/13 by Pascal Pernot, Pernot, Pascal · 1 citation
Decision Sciences · Engineering · Materials Science · #Chemical Physics (physics.chem-ph) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Fault Detection and Control Systems #Machine Learning (stat.ML) #Machine Learning in Materials Science #Scientific Measurement and Uncertainty Evaluation #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2303.07170
openalex publication_date 2023/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The practice of uncertainty quantification (UQ) validation, notably in machine learning for the physico-chemical sciences, rests on several graphical methods (scattering plots, calibration curves, reliability diagrams and confidence curves) which explore complementary aspects of calibration, without covering all the desirable ones. For instance, none of these methods deals with the reliability of UQ metrics across the range of input features (adaptivity). Based on the complementary concepts of consistency and adaptivity, the toolbox of common validation methods for variance- and intervals- based UQ metrics is revisited with the aim to provide a better grasp on their capabilities. This study is conceived as an introduction to UQ validation, and all methods are derived from a few basic rules. The methods are illustrated and tested on synthetic datasets and representative examples extracted from the recent physico-chemical machine learning UQ literature.