#8888th of 105 in ML frameworks and libraries
imbalanced-learn
Re-sampling methods for imbalanced classification datasets, scikit-learn compatible
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- Stars
- 7.1k
- License
- MIT
- Last commit
- Jun 2026
- Last release
- Jun 2026
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
imbalanced-learn is a Python package of re-sampling techniques for classification datasets with strong between-class imbalance. It follows the scikit-learn API and is part of the scikit-learn-contrib projects. Optional support exists for pandas dataframes and TensorFlow/Keras models.
Who it is for: Data scientists training classifiers on skewed tabular data with scikit-learn
Strengths
- Follows the scikit-learn API, so it fits existing estimators
- Installable from PyPI and conda-forge
- Documentation with API reference and examples; JMLR paper to cite
- MIT license; last release 2026-06-07
Weaknesses
- Aimed at tabular classification; not for LLM or text workflows
- Requires Python 3.10+, scikit-learn 1.4.2+ and NumPy 1.25.2+
- README lists Pytest as a hard dependency
- README does not list the algorithms; docs must be consulted
What it needs
- no GPU
- Needs Python >= 3.10, NumPy >= 1.25.2, SciPy >= 1.11.4, scikit-learn >= 1.4.2, pandas >= 2.0.3 (optional), TensorFlow >= 2.16.1 (optional), Keras >= 3.3.3 (optional)
- Models: scikit-learn estimators, Keras, TensorFlow
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