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At the core of interpretable machine learning is the question of whetherhumans are able to make accurate predictions about a model's behavior. Assumedin this question are three properties of the interpretable output: coverage,precision, and effort. Coverage refers to how often humans think they canpredict the model's behavior, precision to how accurate humans are in thosepredictions, and effort is either the up-front effort required in interpretingthe model, or the effort required to make predictions about a model's behavior.  In this work, we propose anchor-LIME (aLIME), a model-agnostic technique thatproduces high-precision rule-based explanations for which the coverageboundaries are very clear. We compare aLIME to linear LIME with simulatedexperiments, and demonstrate the flexibility of aLIME with qualitative examplesfrom a variety of domains and tasks.

Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance