Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations
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Non-linear machine learning models often trade off a great predictive performance for a lack of interpretability. However, model agnostic interpretation techniques now allow us to estimate the effect and importance of features for any predictive model. Different notations and terminology have complicated their understanding and how they are related. A unified view on these methods has been missing. We present the generalized SIPA (Sampling, Intervention, Prediction, Aggregation) framework of work stages for model agnostic interpretation techniques and demonstrate how several prominent methods for feature effects can be embedded into the proposed framework. We also formally introduce pre-existing marginal effects to describe feature effects for black box models. Furthermore, we extend the framework to feature importance computations by pointing out how variance-based and performance-based importance measures are based on the same work stages. The generalized framework may serve as a guideline to conduct model agnostic interpretations in machine learning.
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