Anchors: High Precision Model-Agnostic Explanations

Anchors: High- Precision Model-Agnostic ExplanationsMarco Tulio RibeiroUniversity of SinghUniversity of California, GuestrinUniversity of introduce a novel Model-Agnostic system that explains thebehavior of complex models with high- Precision rules calledanchors, representing local, sufficient conditions for predic-tions. We propose an algorithm to efficiently compute theseexplanations for any black-box model with high-probabilityguarantees. We demonstrate the flexibility of anchors by ex-plaining a myriad of different models for different domainsand tasks. In a user study, we show that anchors enable usersto predict how a model would behave on unseen instanceswith less effort and higher Precision , as compared to existinglinear Explanations or no machine learning models such as deep neuralnetworks have been shown to be highly accurate for manyapplications, even though their complexity virtually makesthem black-boxes.

(“question” is feminine, “problem” is masculine). Tabular Classification: Classification of data in tabular form (categorical and/or continuous features) is a popular appli-cation of machine learning. We use a validation dataset to define D, and sample from D(zjA) by fixing the predicates in Aand sampling the rest of the row as a whole.

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