Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

MCML Authors

Giuseppe Casalicchio

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Abstract

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity.

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. Molnar • G. Casalicchio • B. Bischl

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability