Model-Agnostic Interpretability of Machine Learning

arXiv: Machine Learning, Volume abs/1606.05386, 2016.

Cited by: 189|Bibtex|Views116
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the a...More

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