{"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/hyperopt-a-python-library-for-optimizing-the","title":"Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms","arxiv_id":null,"date":"2013-12-12","proceeding":"SCIPY 2013 2013 12","authors":["James Bergstra","Dan Yamins","David D. Cox"],"abstract":"Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. The paper closes with some discussion of ongoing and future work.","url_abs":"https://www.researchgate.net/publication/285464863_Hyperopt_A_Python_library_for_optimizing_the_hyperparameters_of_machine_learning_algorithms","url_pdf":"https://pdfs.semanticscholar.org/d4f4/9717c9adb46137f49606ebbdf17e3598b5a5.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":"hyperopt-a-python-library-for-optimizing-the","repo_url":"https://github.com/MindSpore-scientific-2/code-5/tree/main/hyperparameters-part1","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"hyperopt-a-python-library-for-optimizing-the","repo_url":"https://github.com/hyperopt/hyperopt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"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":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[{"method_slug":"tree-structured-parzen-estimator-approach-tpe","method_name":"Tree-structured Parzen Estimator Approach (TPE)"}],"datasets_introduced":[],"methods_introduced":[{"slug":"tree-structured-parzen-estimator-approach-tpe","name":"Tree-structured Parzen Estimator Approach (TPE)","full_name":"Tree-structured Parzen Estimator Approach (TPE)"}],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}