An interpretable machine learning methodology to generate interaction effect hypotheses from complex datasets

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NETL
NSTL
Wiley

外文摘要：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.

外文关键词：

interpretable machine learning (IML)explainable AI (XAI)interaction effects in MLhypothesis generation in ML

作者：

Murtaza Nasir、Nichalin S. Summerfield、Serhat Simsek、Asil Oztekin

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作者单位：

Finance,Real Estate,&Decision Science Department,Barton School of Business,Wichita State University,Wichita,Kansas,USA

Operations&Information Systems Department,Manning School of Business,University of Massachusetts Lowell,Lowell,Massachusetts,USA

Feliciano School of Business,Montclair State University,Montclair,New Jersey,USA

出版年：

2024

DOI：

10.1111/deci.12642

Decision sciences: The journal for the American Institute for Decision Sciences

ISSN：0011-7315

年,卷(期)：2024.55(6)

参考文献量47