{"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/imbalanced-learn-a-python-toolbox-to-tackle","title":"Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning","arxiv_id":"1609.06570","date":"2016-09-21","proceeding":null,"authors":["Guillaume Lemaitre","Fernando Nogueira","Christos K. Aridas"],"abstract":"Imbalanced-learn is an open-source python toolbox aiming at providing a wide\nrange of methods to cope with the problem of imbalanced dataset frequently\nencountered in machine learning and pattern recognition. The implemented\nstate-of-the-art methods can be categorized into 4 groups: (i) under-sampling,\n(ii) over-sampling, (iii) combination of over- and under-sampling, and (iv)\nensemble learning methods. The proposed toolbox only depends on numpy, scipy,\nand scikit-learn and is distributed under MIT license. Furthermore, it is fully\ncompatible with scikit-learn and is part of the scikit-learn-contrib supported\nproject. Documentation, unit tests as well as integration tests are provided to\nease usage and contribution. The toolbox is publicly available in GitHub:\nhttps://github.com/scikit-learn-contrib/imbalanced-learn.","url_abs":"http://arxiv.org/abs/1609.06570v1","url_pdf":"http://arxiv.org/pdf/1609.06570v1.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":"imbalanced-learn-a-python-toolbox-to-tackle","repo_url":"https://github.com/scikit-learn-contrib/imbalanced-learn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"imbalanced-learn-a-python-toolbox-to-tackle","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":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.06570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.06570"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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