Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation.

ABSTRACT: It is insightful to report an estimator that describes how certain a model is in a prediction, additionally to the prediction alone. For regression tasks, most approaches implement a variation of the ensemble method, apart from few exceptions. Instead of a single estimator, a group of estimators yields several predictions for an input. The uncertainty can then be quantified by measuring the disagreement between the predictions, for example by the standard deviation. In theory, ensembles should not only provide uncertainties, they also boost the predictive performance by reducing errors arising from variance. Despite the development of novel methods, they are still considered the "golden-standard" to quantify the uncertainty of regression models. Subsampling-based methods to obtain ensemble

SUBMITTER: Dutschmann TM 

PROVIDER: S-EPMC10142532 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature