imbalanced-learn

imbalanced-learn is a Python library that provides a comprehensive suite of resampling techniques to address imbalanced datasets in machine learning, where one class significantly outnumbers another. It offers methods for over-sampling (e.g., SMOTE, ADASYN), under-sampling (e.g., NearMiss, EditedNearestNeighbours), and combined approaches, along with ensemble methods tailored for imbalanced data. It is compatible with scikit-learn and is part of scikit-learn-contrib projects. The current stable version is 0.14.1, with regular maintenance releases to ensure compatibility with scikit-learn and Python versions.