{"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/multilabel-classification-with-r-package-mlr","title":"Multilabel Classification with R Package mlr","arxiv_id":"1703.08991","date":"2017-03-27","proceeding":null,"authors":["Philipp Probst","Quay Au","Giuseppe Casalicchio","Clemens Stachl","Bernd Bischl"],"abstract":"We implemented several multilabel classification algorithms in the machine\nlearning package mlr. The implemented methods are binary relevance, classifier\nchains, nested stacking, dependent binary relevance and stacking, which can be\nused with any base learner that is accessible in mlr. Moreover, there is access\nto the multilabel classification versions of randomForestSRC and rFerns. All\nthese methods can be easily compared by different implemented multilabel\nperformance measures and resampling methods in the standardized mlr framework.\nIn a benchmark experiment with several multilabel datasets, the performance of\nthe different methods is evaluated.","url_abs":"http://arxiv.org/abs/1703.08991v2","url_pdf":"http://arxiv.org/pdf/1703.08991v2.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":"multilabel-classification-with-r-package-mlr","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":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}