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Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms

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Artificial Intelligence, Big Data and Data Science in Statistics

Abstract

Global sensitivity analysis aims to quantify the importance of model input variables for a model response. We highlight the role sensitivity analysis can play in interpretable machine learning and provide a short survey on sensitivity analysis with a focus on global variance-based sensitivity measures like Sobol’ indices and Shapley values. We discuss the Monte Carlo estimation of various Sobol’ indices as well as their graphical presentation in the so-called FANOVA graphs. Global sensitivity analysis is applied to an analytical example, a Kriging model of a piston simulator and a neural net model of the resistance of yacht hulls.

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Acknowledgements

The financial support of the Deutsche Forschungsgemeinschaft (SFB 823, project B1) is gratefully acknowledged.

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Authors and Affiliations

  1. University of Applied Sciences and Arts, Dortmund, Germany

    Sonja Kuhnt & Arkadius Kalka

Authors
  1. Sonja Kuhnt
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  2. Arkadius Kalka
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Correspondence to Sonja Kuhnt .

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Editors and Affiliations

  1. Institute of Statistics and AI Center, RWTH Aachen University, Aachen, Germany

    Ansgar Steland

  2. Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    Kwok-Leung Tsui

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Kuhnt, S., Kalka, A. (2022). Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms. In: Steland, A., Tsui, KL. (eds) Artificial Intelligence, Big Data and Data Science in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-07155-3_6

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