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Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning
Vincent Auriau, Ali Aouad, Antoine Désir, Emmanuel Malherbe
Discrete choice models aim at predicting choice decisions made by individuals from a menu of alternatives, called an assortment. Well-known use cases include predicting a commuter’s choice of transportation mode or a customer’s purchases. Choice models are able to handle assortment variations, when some alternatives become unavailable or when their features change in different contexts. This adaptability to different scenarios allows these models to be used as inputs for optimization problems, including assortment planning or pricing. Choice-Learn is a Python package that provides a modular suite of choice modeling tools for practitioners and academic researchers to process choice data, and then formulate, estimate and operationalize choice models. The library is structured into two levels of usage, as illustrated in Figure 1. The higher-level is designed for fast and easy implementation and the lower-level enables more advanced parameterizations.
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