Evaluating the Performance of the Model Selection with Average ECE and Naive Calibration in Out-of-Domain Generalization Problems for Binary Classifiers

Out-of-domain (OOD) generalization refers to learning a model from one or more different but related domain(s) that can be used in an unknown test domain. It is challenging for existing machine learning models. Several methods have been proposed to solve this problem, and multi-domain calibration is one of these methods. Model selection with the...