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

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      Abstract

      At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the 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 that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.

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          Mining high-speed data streams

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            Author and article information

            Journal
            2016-11-17
            1611.05817

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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            Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems
            stat.ML cs.AI cs.LG

            Machine learning, Artificial intelligence

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