Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets.

ABSTRACT: With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet to be established and different studies on uncertainties generally uses different metrics to evaluate them. We compare three of the most popular validation metrics (Spearman's rank correlation coefficient, the negative log likelihood (NLL) and the miscalibration area) to the error-based calibration introduced by Levi et al. (Sensors 2022, 22, 5540). Importantly, metrics such as the negative log likelihood (NLL) and Spearman's rank correlation coefficient bear little informatio

SUBMITTER: Rasmussen MH 

PROVIDER: S-EPMC10729461 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature