Transcription of Anchors: High Precision Model-Agnostic Explanations
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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.
conditions in the rule are met, and if they apply the precision is high (by design). We demonstrate the usefulness of anchors by applying them to a variety of machine learning tasks (classification, structured prediction, text generation) on a diverse set of domains (tabular, text, and images). We also run a user study,
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