Model-Agnostic Interpretability of Machine Learning
arXiv: Machine Learning, Volume abs/1606.05386, 2016.
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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