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arXiv · 0912.1128

How to Explain Individual Classification Decisions

David Baehrens·Timon Schroeter·Stefan Harmeling·Motoaki Kawanabe·Katja Hansen·Klaus-Robert Mueller
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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 the 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.

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David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Mueller. 2009-12-06. How to Explain Individual Classification Decisions. https://arxiv.org/abs/0912.1128

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