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

MCML Authors

Christian Alexander Scholbeck

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Giuseppe Casalicchio

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

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.

inproceedings

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ECML-PKDD 2019

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Wuerzburg, Germany, Sep 16-20, 2019.

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Authors

C. A. Scholbeck • C. Molnar • C. Heumann • B. Bischl • G. Casalicchio

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability