{"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/srlearn-a-python-library-for-gradient-boosted","title":"srlearn: A Python Library for Gradient-Boosted Statistical Relational Models","arxiv_id":"1912.08198","date":"2019-12-17","proceeding":null,"authors":["Alexander L. Hayes"],"abstract":"We present srlearn, a Python library for boosted statistical relational models. We adapt the scikit-learn interface to this setting and provide examples for how this can be used to express learning and inference problems.","url_abs":"https://arxiv.org/abs/1912.08198v1","url_pdf":"https://arxiv.org/pdf/1912.08198v1.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":"srlearn-a-python-library-for-gradient-boosted","repo_url":"https://github.com/hayesall/srlearn-StarAI-2020-workshop","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"srlearn-a-python-library-for-gradient-boosted","repo_url":"https://github.com/hayesall/srlearn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}