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An interpretable machine learning methodology to generate interaction effect hypotheses from complex datasets
Nasir, Murtaza Summerfield, Nichalin S. Simsek, Serhat Oztekin, Asil
Nasir, Murtaza
Summerfield, Nichalin S.
Simsek, Serhat
Oztekin, Asil
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2024
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Article
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Explainable AI (XAI),Hypothesis generation in ML,Interaction effects in ML,Interpretable machine learning (IML)
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Citation
Nasir, M., Summerfield, N.S., Simsek, S., Oztekin, A. An interpretable machine learning methodology to generate interaction effect hypotheses from complex datasets. (2024). Decision Sciences. DOI: 10.1111/deci.12642
Abstract
Machine learning (ML) models are increasingly being used in decision-making, but they can be difficult to understand because most ML models are black boxes, meaning that their inner workings are not transparent. This can make interpreting the results of ML models and understanding the underlying data-generation process (DGP) challenging. In this article, we propose a novel methodology called Simple Interaction Finding Technique (SIFT) that can help make ML models more interpretable. SIFT is a data- and model-agnostic approach that can be used to identify interaction effects between variables in a dataset. This can help improve our understanding of the DGP and make ML models more transparent and explainable to a wider audience. We test the proposed methodology against various factors (such as ML model complexity, dataset noise, spurious variables, and variable distributions) to assess its effectiveness and weaknesses. We show that the methodology is robust against many potential problems in the underlying dataset as well as ML algorithms. © 2024 The Author(s). Decision Sciences published by Wiley Periodicals LLC on behalf of Decision Sciences Institute.
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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2024 The Author(s). Decision Sciences published by Wiley Periodicals LLC on behalf of Decision Sciences Institute.
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John Wiley and Sons Inc
Journal
Decision Sciences
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0011-7315