Estimating Discrete Choice Models with Random Forests

Ningyuan Chen, Guillermo Gallego and Zhuodong Tang ()
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Ningyuan Chen: University of Toronto Mississauga
Guillermo Gallego: Hong Kong University of Science and Technology
Zhuodong Tang: Hong Kong University of Science and Technology

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 184-196 from Springer

Abstract: Abstract We show the equivalence of discrete choice models and a forest of binary decision trees. This suggests that standard machine learning techniques based on random forests can serve to estimate discrete choice models with an interpretable output: the underlying trees can be viewed as the internal choice process of customers. Our data-driven theoretical results show that random forests can predict the choice probability of any discrete choice model consistently. Our numerical results show that using random forests to estimate customer choices can outperform the best parametric models in two real datasets in a shorter running time.

Keywords: Discrete choice model; Random forest; Machine learning; Online retailing (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-90275-9_16

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DOI: 10.1007/978-3-030-90275-9_16

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