Measuring the Generalization Ability of a Trained Machine Learning Model With Respect to Given Measurement Data

Abstract
A method for measuring the ability of a trained machine learning model for the processing of measurement data to generalize, with respect to a given task, to a target domain and/or distribution to which one or more input records of measurement data belong. The method includes: determining), from the input records of measurement data, a target style that characterizes the target domain and/or distribution; obtaining, based at least in part on the target style, validation examples in the target domain and/or distribution, and also corresponding ground truth labels; processing, by the trained machine learning model, the validation examples into outputs; and determining, based on a comparison between the outputs and the respective ground truth labels, the accuracy of the trained machine learning model as the sought ability of the trained machine learning model to generalize to the target domain.