{"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-sklearn-automatic-hyperparameter","title":"Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn","arxiv_id":null,"date":"2014-01-01","proceeding":"SCIPY 2014 2014 1","authors":["Brent Komer","James Bergstra","Chris Eliasmith"],"abstract":"Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-Newsgroups, Convex Shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and Convex Shapes.","url_abs":"https://www.semanticscholar.org/paper/Hyperopt-Sklearn%3A-Automatic-Hyperparameter-for-Komer-Bergstra/7b2ce6eb25ab840936ae6bf2cd49bc7f20785bc4","url_pdf":"http://conference.scipy.org/proceedings/scipy2014/pdfs/komer.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-sklearn-automatic-hyperparameter","repo_url":"https://github.com/hyperopt/hyperopt-sklearn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"hyperopt-sklearn-automatic-hyperparameter","repo_url":"https://github.com/jim-schwoebel/allie","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"}],"methods":[{"method_slug":"pca","method_name":"PCA"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}