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

Guillaume Lemaitre, Fernando Nogueira, Christos K. Aridas
9/21/2016
cs.LG

Official code: scikit-learn-contrib/imbalanced-learn (7,131 stars, MIT, last commit 2026-06-29).

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.

View on arXivView PDF

Code Implementations(1)

scikit-learn-contrib/imbalanced-learnOfficial100%
MIT

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

7,1311,381TeX, ShellAug 16, 20143 months agoMIT
data-analysisdata-sciencemachine-learningpythonstatistics

Similar Papers

Do Machine Learning Models Learn Statistical Rules Inferred from Data?

80% match1 repo52023

metric-learn: Metric Learning Algorithms in Python

80% match1 repo1.4k2019

DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning

79% match2024

Beyond Static Costs: Learning-Dynamics Aware Loss Functions for Long-Tailed Classification

79% match1 repo2026

Efficient Augmentation for Imbalanced Deep Learning

79% match1 repo42022

Multimodal Distillation-Driven Ensemble Learning for Long-Tailed Histopathology Whole Slide Images Analysis

78% match1 repo12025

Cite this paper

@article{lemaitre2016imbalancedlearn,
  title  = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
  author = {Guillaume Lemaitre and Fernando Nogueira and Christos K. Aridas},
  year   = {2016},
  eprint = {1609.06570},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1609.06570}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/1609.06570.svg)](https://codeofpaper.com/paper/1609.06570)

Discussion