Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations

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Authors Christoph Molnar, Giuseppe Casalicchio, Christian Heumann, Bernd Bischl, Christian A. Scholbeck
Journal/Conference Name Communications in Computer and Information Science
Paper Category
Paper Abstract Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see 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 interpretations and demonstrate how several prominent methods for feature effects can be embedded into the proposed framework. 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 SIPA framework reduces the diverse set of model-agnostic techniques to a single methodology and establishes a common terminology to discuss them in future work.
Date of publication 2019
Code Programming Language R
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