Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, what is also important is understanding how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the generalization power of the rules it learned. We present here an approach that learns rules to explain globally the behavior of black box machine learning models. Collectively these rules represent the logic learned by the model and are hence useful for gaining insight into its behavior. We demonstrate the power of the approach on three publicly available data sets. MAGIX: Model Agnostic Globally Interpretable Explanations

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