{"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/introducing-sgboost-a-practical-guide-and","title":"Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation","arxiv_id":"2405.21037","date":"2024-05-31","proceeding":null,"authors":["Fabian Obster","Christian Heumann"],"abstract":"This paper introduces a novel framework for reducing variable selection bias by balancing selection frequencies of base-learners in boosting and introduces the sgboost package in R, which implements this framework combined with sparse-group boosting. The group bias reduction algorithm employs a simulation-based approach to iteratively adjust the degrees of freedom for both individual and group base-learners, ensuring balanced selection probabilities and mitigating the tendency to over-select more complex groups. The efficacy of the group balancing algorithm is demonstrated through simulations. Sparse-group boosting offers a flexible approach for both group and individual variable selection, reducing overfitting and enhancing model interpretability for modeling high-dimensional data with natural groupings in covariates. The package uses regularization techniques based on the degrees of freedom of individual and group base-learners. Through comparisons with existing methods and demonstration of its unique functionalities, this paper provides a practical guide on utilizing sparse-group boosting in R, accompanied by code examples to facilitate its application in various research domains.","url_abs":"https://arxiv.org/abs/2405.21037v2","url_pdf":"https://arxiv.org/pdf/2405.21037v2.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":"introducing-sgboost-a-practical-guide-and","repo_url":"https://github.com/FabianObster/sgboost-introduction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"selection-bias","task_name":"Selection bias"},{"task_slug":"variable-selection","task_name":"Variable Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}