---
id: imbalanced-learn
version: "0.14.2"
license: unclear
license_treatment: permissive
maintenance: active
---

# imbalanced-learn — Toolbox for imbalanced dataset in machine learning

License: permissive · Maintenance: active · Downloads: 16.8M/mo

## What it is and what it does

imbalanced-learn is a scikit-learn-compatible Python toolbox for handling datasets where one class significantly outnumbers others—a common problem in real-world machine learning. It implements multiple re-sampling strategies (both oversampling and undersampling) to balance class distributions before training, helping classification algorithms learn more robust decision boundaries on skewed data.

The package integrates seamlessly into scikit-learn workflows via a standard transformer interface, making it straightforward to include resampling in preprocessing pipelines. It depends on numpy, scipy, scikit-learn, joblib, and threadpoolctl, and supports Python 3.10 through 3.14. The project is actively maintained as part of the scikit-learn-contrib ecosystem.

Use it for:

- Preprocess fraud detection datasets where fraudulent transactions are rare before training a classifier
- Balance medical diagnosis datasets with few positive cases to improve model sensitivity
- Prepare imbalanced text classification data for training with rare event prediction
- Create balanced training sets in credit risk or loan default prediction tasks
- Handle class imbalance in anomaly detection or rare failure prediction scenarios

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides re-sampling techniques to address class imbalance in machine learning datasets, integrating with scikit-learn for preprocessing imbalanced data before model training.

Yes. Active maintenance, low install friction, permissive MIT license, and no known vulnerabilities make this a safe choice. It solves a real, common problem in machine learning with a mature, well-integrated API. Install if you work with imbalanced classification datasets.

## Install

pip install imbalanced-learn
uv add imbalanced-learn
poetry add imbalanced-learn

## Installing imbalanced-learn

Before you install: Low friction installation with a pure-Python wheel. Active maintenance with recent releases; last commit 2026-06-29. Depends on well-established scientific Python stack (numpy, scipy, scikit-learn, joblib).

License in practice: MIT license (permissive) allows use in commercial and private projects with minimal restrictions.

Quickstart:

pip install imbalanced-learn

import imbalanced_learn
from sklearn.datasets import make_classification

X, y = make_classification()
# Use imbalanced-learn resampling techniques in preprocessing pipeline

Verify before relying:

- Whether optional dependencies (Pandas, TensorFlow, Keras) are required for core functionality or only for specific features
- Performance characteristics when handling very large datasets
- Specific resampling algorithms included and their names

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 16.8M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

class imbalance resampling, imbalanced dataset handling, oversampling undersampling, scikit-learn imbalance, minority class balancing, skewed dataset preprocessing, resampling techniques, class-imbalance, resampling, data-preprocessing

[View on SkillFed](https://skillfed.io/packages/imbalanced-learn) · [View on PyPI](https://pypi.org/project/imbalanced-learn/)