Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

Abstract : 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 https://github.com/scikit-learn-contrib/imbalanced-learn.
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Journal of Machine Learning Research, Journal of Machine Learning Research, 2017, 18, pp.1 - 5
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  • HAL Id : hal-01516244, version 1

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Guillaume Lemaitre, Fernando Nogueira, Christos Aridas. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. Journal of Machine Learning Research, Journal of Machine Learning Research, 2017, 18, pp.1 - 5. 〈hal-01516244〉

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