The Use of Binary Choice Forests to Model and Estimate Discrete Choices

TL;DR

This paper introduces a novel interpretable machine learning approach using binary choice forests to model and predict customer discrete choices, addressing limitations of traditional models in capturing complex behaviors.

Contribution

It develops a forest-based discrete choice model that is both interpretable and capable of accurately predicting choices, outperforming existing methods and handling complex, real-world data.

Findings

The model predicts choice probabilities consistently across different data.

It can recover customer preference rankings using splitting criteria.

Outperforms existing methods in synthetic and real data experiments.

Abstract

Problem definition. In retailing, discrete choice models (DCMs) are commonly used to capture the choice behavior of customers when offered an assortment of products. When estimating DCMs using transaction data, flexible models (such as machine learning models or nonparametric models) are typically not interpretable and hard to estimate, while tractable models (such as the multinomial logit model) tend to misspecify the complex behavior represeted in the data. Methodology/results. In this study, we use a forest of binary decision trees to represent DCMs. This approach is based on random forests, a popular machine learning algorithm. The resulting model is interpretable: the decision trees can explain the decision-making process of customers during the purchase. We show that our approach can predict the choice probability of any DCM consistently and thus never suffers from misspecification. Moreover, our algorithm predicts assortments unseen in the training data. The mechanism and errors can be theoretically analyzed. We also prove that the random forest can recover preference rankings of customers thanks to the splitting criterion such as the Gini index and information gain ratio. Managerial implications. The framework has unique practical advantages. It can capture customers' behavioral patterns such as irrationality or sequential searches when purchasing a product. It handles nonstandard formats of training data that result from aggregation. It can measure product importance based on how frequently a random customer would make decisions depending on the presence of the product. It can also incorporate price information and customer features. Our numerical experiments using synthetic and real data show that using random forests to estimate customer choices can outperform existing methods.