Metrics for Measuring Error Extents of Machine Learning Classifiers
Hong Zhu, Ian Bayley, Mark Alan Green · 2022
Metrics play a crucial role in evaluating the performance of machine learning (ML) models. Metrics for quantifying the extent of errors, in particular, have been intensively studied and widely used but only so far for regression models. This paper focuses instead on classifier models. A new approach is proposed in which datamorphic exploratory testing is used to discover the boundary values between classes and the distance of misclassified instances from that boundary is used to quantify the errors that the model makes. Empirical experiments and case studies are reported that validate and evaluate the proposed metrics.