Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox depends only on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. Source code, binaries, and documentation can be downloaded from

References in zbMATH (referenced in 9 articles )

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  1. Bej, Saptarshi; Davtyan, Narek; Wolfien, Markus; Nassar, Mariam; Wolkenhauer, Olaf: LoRAS: an oversampling approach for imbalanced datasets (2021)
  2. Koziarski, Michał; Bellinger, Colin; Woźniak, Michał: RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification (2021)
  3. Merdan, Selin; Barnett, Christine L.; Denton, Brian T.; Montie, James E.; Miller, David C.: OR practice-data analytics for optimal detection of metastatic prostate cancer (2021)
  4. Chaabane, Ikram; Guermazi, Radhouane; Hammami, Mohamed: Enhancing techniques for learning decision trees from imbalanced data (2020)
  5. Liang, Jing; Wei, Panpan; Qu, Boyang; Yu, Kunjie; Yue, Caitong; Hu, Yi; Ge, Shilei: Ensemble learning based on multimodal multiobjective optimization (2020)
  6. Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; Elwany, Alaa; Strong, Ray; Blomberg, Jeanette: Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (2020)
  7. Sage Hahn, Dekang Yuan, Wesley Thompson, Max M Owens, Nicholas Allgaier, Hugh Garavan: Brain Predictability toolbox: a Python library for neuroimaging based machine learning (2020) arXiv
  8. Bing Zhu; Zihan Gao; Junkai Zhao; Seppe K.L.M. van den Broucke: IRIC: An R library for binary imbalanced classification (2019) not zbMATH
  9. Koziarski, Michał; Wożniak, Michał: CCR: a combined cleaning and resampling algorithm for imbalanced data classification (2017)