{"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/fseval-a-benchmarking-framework-for-feature","title":"fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms","arxiv_id":null,"date":"2022-11-23","proceeding":"Journal of Open Source Software 2022 11","authors":["Jeroen G. S. Overschie","Ahmad Alsahaf","George Azzopardi"],"abstract":"The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables users to run experiments in parallel and distributed over multiple machines, and export the results to an SQL database. The execution of an experiment can be fully determined by a configuration file, which means the experiment results can be reproduced easily, given only the configuration file. fseval has high test coverage, continuous integration, and rich documentation. The package is open source and can be installed through PyPI. The source code is available at: https://github.com/dunnkers/fseval.","url_abs":"https://joss.theoj.org/papers/10.21105/joss.04611","url_pdf":"https://www.theoj.org/joss-papers/joss.04611/10.21105.joss.04611.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":"fseval-a-benchmarking-framework-for-feature","repo_url":"https://github.com/dunnkers/fseval","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"automated-feature-engineering","task_name":"Automated Feature Engineering"},{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":null,"task_name":"Classification with Costly Features"},{"task_slug":"distributed-computing","task_name":"Distributed Computing"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"feature-importance","task_name":"Feature Importance"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}