{"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/reinbo-machine-learning-pipeline-search-and","title":"ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning","arxiv_id":"1904.05381","date":"2019-04-10","proceeding":null,"authors":["Xudong Sun","Jiali Lin","Bernd Bischl"],"abstract":"Machine learning pipeline potentially consists of several stages of\noperations like data preprocessing, feature engineering and machine learning\nmodel training. Each operation has a set of hyper-parameters, which can become\nirrelevant for the pipeline when the operation is not selected. This gives rise\nto a hierarchical conditional hyper-parameter space. To optimize this mixed\ncontinuous and discrete conditional hierarchical hyper-parameter space, we\npropose an efficient pipeline search and configuration algorithm which combines\nthe power of Reinforcement Learning and Bayesian Optimization. Empirical\nresults show that our method performs favorably compared to state of the art\nmethods like Auto-sklearn , TPOT, Tree Parzen Window, and Random Search.","url_abs":"http://arxiv.org/abs/1904.05381v1","url_pdf":"http://arxiv.org/pdf/1904.05381v1.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":"reinbo-machine-learning-pipeline-search-and","repo_url":"https://github.com/smilesun/reinbo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"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}