imbalanced-learn is an open source Python library designed to help users address classification problems with imbalanced datasets. Built on top of scikit-learn, it provides a suite of tools for resampling, evaluation, and other specific techniques related to imbalanced class distributions, making it useful for machine learning practitioners, data scientists, and researchers dealing with real-world, skewed data scenarios.
Visit imbalanced-learn's official website for product details and getting started.
In-depth user guide covering installation, usage examples, and best practices for handling imbalanced datasets.
Access the source code, report issues, and contribute to the development of imbalanced-learn on GitHub.