{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/choice-learn-large-scale-choice-modeling-for","title":"Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning","arxiv_id":null,"date":"2024-09-10","proceeding":"Journal of Open-Source Software 2024 9","authors":["Vincent Auriau","Ali Aouad","Antoine Désir","Emmanuel Malherbe"],"abstract":"Discrete choice models aim at predicting choice decisions made by individuals from a menu\r\nof alternatives, called an assortment. Well-known use cases include predicting a commuter’s\r\nchoice of transportation mode or a customer’s purchases. Choice models are able to handle\r\nassortment variations, when some alternatives become unavailable or when their features\r\nchange in different contexts. This adaptability to different scenarios allows these models to be\r\nused as inputs for optimization problems, including assortment planning or pricing.\r\nChoice-Learn is a Python package that provides a modular suite of choice modeling tools for practitioners and academic\r\nresearchers to process choice data, and then formulate, estimate and operationalize choice\r\nmodels. The library is structured into two levels of usage, as illustrated in Figure 1. The\r\nhigher-level is designed for fast and easy implementation and the lower-level enables more\r\nadvanced parameterizations.","url_abs":"https://joss.theoj.org/papers/10.21105/joss.06899","url_pdf":"https://joss.theoj.org/papers/10.21105/joss.06899","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"choice-learn-large-scale-choice-modeling-for","repo_url":"https://github.com/artefactory/choice-learn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"discrete-choice-models","task_name":"Discrete Choice Models"}],"methods":[{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}