{"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/dive-into-decision-trees-and-forests-a","title":"Dive into Decision Trees and Forests: A Theoretical Demonstration","arxiv_id":"2101.08656","date":"2021-01-20","proceeding":null,"authors":["Jinxiong Zhang"],"abstract":"Based on decision trees, many fields have arguably made tremendous progress in recent years. In simple words, decision trees use the strategy of \"divide-and-conquer\" to divide the complex problem on the dependency between input features and labels into smaller ones. While decision trees have a long history, recent advances have greatly improved their performance in computational advertising, recommender system, information retrieval, etc. We introduce common tree-based models (e.g., Bayesian CART, Bayesian regression splines) and training techniques (e.g., mixed integer programming, alternating optimization, gradient descent). Along the way, we highlight probabilistic characteristics of tree-based models and explain their practical and theoretical benefits. Except machine learning and data mining, we try to show theoretical advances on tree-based models from other fields such as statistics and operation research. We list the reproducible resource at the end of each method.","url_abs":"https://arxiv.org/abs/2101.08656v1","url_pdf":"https://arxiv.org/pdf/2101.08656v1.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":"dive-into-decision-trees-and-forests-a","repo_url":"https://github.com/alvinwan/neural-backed-decision-trees","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}