Web: http://arxiv.org/abs/2110.09599

June 20, 2022, 1:12 a.m. | Michael Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken

cs.CL updates on arXiv.org arxiv.org

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

More from arxiv.org / cs.CL updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY