{"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/mlrmbo-a-modular-framework-for-model-based","title":"mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions","arxiv_id":"1703.03373","date":"2017-03-09","proceeding":null,"authors":["Bernd Bischl","Jakob Richter","Jakob Bossek","Daniel Horn","Janek Thomas","Michel Lang"],"abstract":"We present mlrMBO, a flexible and comprehensive R toolbox for model-based\noptimization (MBO), also known as Bayesian optimization, which addresses the\nproblem of expensive black-box optimization by approximating the given\nobjective function through a surrogate regression model. It is designed for\nboth single- and multi-objective optimization with mixed continuous,\ncategorical and conditional parameters. Additional features include multi-point\nbatch proposal, parallelization, visualization, logging and error-handling.\nmlrMBO is implemented in a modular fashion, such that single components can be\neasily replaced or adapted by the user for specific use cases, e.g., any\nregression learner from the mlr toolbox for machine learning can be used, and\ninfill criteria and infill optimizers are easily exchangeable. We empirically\ndemonstrate that mlrMBO provides state-of-the-art performance by comparing it\non different benchmark scenarios against a wide range of other optimizers,\nincluding DiceOptim, rBayesianOptimization, SPOT, SMAC, Spearmint, and\nHyperopt.","url_abs":"http://arxiv.org/abs/1703.03373v3","url_pdf":"http://arxiv.org/pdf/1703.03373v3.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":"mlrmbo-a-modular-framework-for-model-based","repo_url":"https://github.com/mlr-org/mlrMBO","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mlrmbo-a-modular-framework-for-model-based","repo_url":"https://github.com/berndbischl/mlrMBO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mlrmbo-a-modular-framework-for-model-based","repo_url":"https://github.com/cran/mlrMBO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mlrmbo-a-modular-framework-for-model-based","repo_url":"https://github.com/mlr-org/mlr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"smac","task_name":"SMAC"},{"task_slug":"smac-1","task_name":"SMAC+"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}