How to Explain Individual Classification Decisions

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. [abs] [ pdf ][ bib ] &copy JMLR 2010. ( edit, beta )

Authors

• David Baehrens
• Timon Schroeter
• Stefan Harmeling
• Motoaki Kawanabe
• Katja Hansen
• Klaus-Robert Müller

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