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Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms

In: Artificial Intelligence, Big Data and Data Science in Statistics

Author

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  • Sonja Kuhnt

    (University of Applied Sciences and Arts)

  • Arkadius Kalka

    (University of Applied Sciences and Arts)

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.

Suggested Citation

  • Sonja Kuhnt & Arkadius Kalka, 2022. "Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms," Springer Books, in: Ansgar Steland & Kwok-Leung Tsui (ed.), Artificial Intelligence, Big Data and Data Science in Statistics, pages 155-169, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-07155-3_6
    DOI: 10.1007/978-3-031-07155-3_6
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