How to Explain Individual Classification Decisions

Baehrens, D; Schroeter, T; Harmeling, S; Kawanabe, M; Hansen, K; Müller, K-R

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How to Explain Individual Classification Decisions

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

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http://jmlr.csail.mit.edu/papers/volume11/baehrens10a/baehrens10a.pdf
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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BF74-6

Abstract

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.