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 of methods to cope with the problem of imbalanced dataset frequently in machine learning and pattern recognition. The implemented-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) learning methods. The proposed toolbox only depends on numpy, scipy, scikit-learn and is distributed under MIT license. Furthermore, it is fully with scikit-learn and is part of the scikit-learn-contrib supported. Documentation, unit tests as well as integration tests are provided to usage and contribution. The toolbox is publicly available in GitHub: : //github. com/scikit-learn-contrib/imbalanced-learn.