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How to Explain Individual Classification Decisions

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83954

Schroeter T, Harmeling,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84096

Müller,  K-R
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Baehrens, D., Schroeter T, Harmeling, S., Kawanabe M, Hansen, K., & Müller, K.-R. (2010). How to Explain Individual Classification Decisions. Journal of Machine Learning Research, 11, 1803-1831. Retrieved from http://jmlr.csail.mit.edu/papers/volume11/baehrens10a/baehrens10a.pdf.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BF74-6
Zusammenfassung
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted a particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.