RECLAIM: Reverse Engineering Classification Metrics
Flavio Giobergia, Elena Baralis · 2022
Being able to compare machine learning models in terms of performance is a fundamental part of improving the state of the art in a field. However, there is a risk of getting locked into only using a few - possibly not ideal - performance metrics, only for comparability with earlier works. In this work, we explore the possibility of reconstructing new classification metrics starting from what little information may be available in existing works. We propose three approaches to reconstruct confusion matrices and, as a consequence, other classification metrics. We empirically verify the quality of the reconstructions, drawing conclusions on the usefulness that various classification metrics have for the reconstruction task.