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

arXiv: https://arxiv.org/abs/1609.06570 · HTML: https://mlsanity.com/paper/1609.06570/

## Key facts

- Cited by 195 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2016.

## Built on by (most cited first, 30 of 195)

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- [What Should Be Balanced in a "Balanced" Face Recognition Dataset?](https://mlsanity.com/paper/2304.09818/) (2023)
- [On the Usefulness of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2306.15636/) (2023)
- [Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models](https://mlsanity.com/paper/2404.08254/) (2024)
- [Foundations of data imbalance and solutions for a data democracy](https://mlsanity.com/paper/2108.00071/) (2021)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey](https://mlsanity.com/paper/2209.09239/) (2022)
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- [CLIMB: Class-imbalanced Learning Benchmark on Tabular Data](https://mlsanity.com/paper/2505.17451/) (2025)
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- [IMBENS: Ensemble Class-imbalanced Learning in Python](https://mlsanity.com/paper/2111.12776/) (2021)
- [Restoring balance: principled under/oversampling of data for optimal classification](https://mlsanity.com/paper/2405.09535/) (2024)
- [Bias-Corrected Data Synthesis for Imbalanced Learning](https://mlsanity.com/paper/2510.26046/) (2025)
- [Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning](https://mlsanity.com/paper/2111.12791/) (2021)
- [Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?](https://mlsanity.com/paper/2112.13210/) (2021)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
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- [TCE: A Test-Based Approach to Measuring Calibration Error](https://mlsanity.com/paper/2306.14343/) (2023)

## Abstract (arXiv)

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.