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  How to Explain Individual Classification Decisions

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.

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Baehrens, D, Autor
Schroeter T, Harmeling, S1, Autor           
Kawanabe M, Hansen, K, Autor
Müller, K-R1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Datum: 2010-06
 Publikationsstatus: Erschienen
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 Identifikatoren: URI: http://jmlr.csail.mit.edu/papers/volume11/baehrens10a/baehrens10a.pdf
BibTex Citekey: 6670
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Titel: Journal of Machine Learning Research
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 11 Artikelnummer: - Start- / Endseite: 1803 - 1831 Identifikator: -