{"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/a-scikit-based-python-environment-for","title":"A scikit-based Python environment for performing multi-label classification","arxiv_id":"1702.01460","date":"2017-02-05","proceeding":null,"authors":["Piotr Szymański","Tomasz Kajdanowicz"],"abstract":"scikit-multilearn is a Python library for performing multi-label\nclassification. The library is compatible with the scikit/scipy ecosystem and\nuses sparse matrices for all internal operations. It provides native Python\nimplementations of popular multi-label classification methods alongside a novel\nframework for label space partitioning and division. It includes modern\nalgorithm adaptation methods, network-based label space division approaches,\nwhich extracts label dependency information and multi-label embedding\nclassifiers. It provides python wrapped access to the extensive multi-label\nmethod stack from Java libraries and makes it possible to extend deep learning\nsingle-label methods for multi-label tasks. The library allows multi-label\nstratification and data set management. The implementation is more efficient in\nproblem transformation than other established libraries, has good test coverage\nand follows PEP8. Source code and documentation can be downloaded from\nhttp://scikit.ml and also via pip. The library follows BSD licensing scheme.","url_abs":"http://arxiv.org/abs/1702.01460v5","url_pdf":"http://arxiv.org/pdf/1702.01460v5.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":"a-scikit-based-python-environment-for","repo_url":"https://github.com/scikit-multilearn/scikit-multilearn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-scikit-based-python-environment-for","repo_url":"https://github.com/jlgarridol/scikit-multilearn-ubumlaas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"management","task_name":"Management"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.01460","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}