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

TL;DR

Imbalanced-learn is a Python toolbox offering diverse methods like under-sampling, over-sampling, and ensemble techniques to address the challenges of imbalanced datasets in machine learning, fully compatible with scikit-learn.

Contribution

It introduces a comprehensive, easy-to-use Python toolbox with state-of-the-art methods for handling imbalanced datasets, integrated with scikit-learn.

Findings

Provides a wide range of imbalanced data handling methods

Ensures compatibility with scikit-learn ecosystem

Open-source with extensive documentation and testing

Abstract

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-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 fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn.