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

Imbalanced-learn is an open-source python toolbox aiming at providing a wide\nrange of methods to cope with the problem of imbalanced dataset frequently\nencountered in machine learning and pattern recognition. The implemented\nstate-of-the-art methods can be categorized into 4 groups: (i) under-sampling,\n(ii) over-sampling, (iii) combination of over- and under-sampling, and (iv)\nensemble learning methods. The proposed toolbox only depends on numpy, scipy,\nand scikit-learn and is distributed under MIT license. Furthermore, it is fully\ncompatible with scikit-learn and is part of the scikit-learn-contrib supported\nproject. Documentation, unit tests as well as integration tests are provided to\nease usage…