{"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/a-comparison-of-machine-learning-methods-for","title":"A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables","arxiv_id":"2307.02071","date":"2023-07-05","proceeding":null,"authors":["Fabio Sigrist"],"abstract":"High-cardinality categorical variables are variables for which the number of different levels is large relative to the sample size of a data set, or in other words, there are few data points per level. Machine learning methods can have difficulties with high-cardinality variables. In this article, we empirically compare several versions of two of the most successful machine learning methods, tree-boosting and deep neural networks, and linear mixed effects models using multiple tabular data sets with high-cardinality categorical variables. We find that, first, machine learning models with random effects have higher prediction accuracy than their classical counterparts without random effects, and, second, tree-boosting with random effects outperforms deep neural networks with random effects.","url_abs":"https://arxiv.org/abs/2307.02071v1","url_pdf":"https://arxiv.org/pdf/2307.02071v1.pdf","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":"a-comparison-of-machine-learning-methods-for","repo_url":"https://github.com/fabsig/compare_ml_highcardinality_categorical_variables","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2307.02071","atlas_url":"https://app.syntology.ai/?focus=2307.02071","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}