Label-Descriptive Patterns and Their Application to Characterizing Classification Errors. (arXiv:2110.09599v3 [cs.LG] UPDATED)

State-of-the-art deep learning methods achieve human-like performance on many
tasks, but make errors nevertheless. Characterizing these errors in easily
interpretable terms gives insight into whether a classifier is prone to making
systematic errors, but also gives a way to act and improve the classifier. We
propose to discover those feature-value combinations (i.e., patterns) that
strongly correlate with correct resp. erroneous predictions to obtain a global
and interpretable description for arbitrary classifiers. We show this is an
instance of the more …

application arxiv classification errors lg patterns