Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms

Sonja Kuhnt () and Arkadius Kalka ()
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Sonja Kuhnt: University of Applied Sciences and Arts
Arkadius Kalka: University of Applied Sciences and Arts

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 155-169 from Springer

Abstract: 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.

Keywords: Interpretable machine learning; Global sensitivity analysis; Sobol’ indices; Shapley values; FANOVA graph; Kriging (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_6

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DOI: 10.1007/978-3-031-07155-3_6

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