imbalanced-learn
Toolbox for imbalanced dataset in machine learning
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
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 · maintenance · safety
permissive license (permissive) — MIT license (permissive) allows use in commercial and private projects with minimal restrictions.
last release 2026-06-07 (68 days) · last repo commit 2026-06-29 · 7,117 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 16,822,922 downloads/mo, #1,138 on PyPI
Alternatives
Verify before relying
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- 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
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 on this page. Verify before relying on it.
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
imbalanced-learn on PyPI
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 pipelineVerify 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 | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesnumpyscipyscikit-learnsklearn-compatjoblibthreadpoolctl |
| Maintenance | Actively maintained 68 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 16,822,922 / month, #1,138 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: imbalanced_learn-0.14.2-py3-none-any.whl
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See also imbalance-xgboost · imblearn · scikit-learn-extra · scikit-surprise · scikit-learn · bootstrapped · sklearn-compat · forestci · spark-sklearn · river