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Imbalanced-learn is an open-source python toolbox aiming at providing a widerange of methods to cope with the problem of imbalanced dataset frequentlyencountered in machine learning and pattern recognition. The implementedstate-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 only depends on numpy, scipy,and scikit-learn and is distributed under MIT license. Furthermore, it is fullycompatible with scikit-learn and is part of the scikit-learn-contrib supportedproject. Documentation, unit tests as well as integration tests are provided toease usage and contribution. The toolbox is publicly available in GitHub:https://github.com/scikit-learn-contrib/imbalanced-learn.

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