ML Anthology

ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem

Wenglei Wu, Nicholas Kunz, Paula Branco
ECML-PKDD 2022 pp. 645-648
doi:10.1007/978-3-031-26422-1_48 /ecmlpkdd/2022/wu2022ecmlpkdd-imbalancedlearningregression/

Abstract

This package helps Python users address imbalanced regression problems. Popular Python packages exist for imbalanced classification. However, there is still little Python support for imbalanced regression. Imbalanced regression is a well-known problem that occurs across domains, where a continuous target variable is poorly represented on ranges that are important to the end-user. Here, a re-sampling strategy is applied to modify the distribution of the target variable, biasing it towards the end-user interests so that downstream learning algorithms can be trained on the most relevant cases. The package provides an easy-to-use and extensible implementation of eight state-of-the-art re-sampling methods for regression, including four under-sampling and four over-sampling techniques. Code related to this paper is available at: https://github.com/paobranco/ImbalancedLearningRegression .

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Cite

Text

Wu et al. "ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26422-1_48

Markdown

[Wu et al. "ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/wu2022ecmlpkdd-imbalancedlearningregression/) doi:10.1007/978-3-031-26422-1_48

BibTeX

@inproceedings{wu2022ecmlpkdd-imbalancedlearningregression,
  title     = {{ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem}},
  author    = {Wu, Wenglei and Kunz, Nicholas and Branco, Paula},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {645-648},
  doi       = {10.1007/978-3-031-26422-1_48},
  url       = {https://mlanthology.org/ecmlpkdd/2022/wu2022ecmlpkdd-imbalancedlearningregression/}
}