scikit-learn-contrib/imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Provides over-sampling, under-sampling, and hybrid re-sampling algorithms (SMOTE, ADASYN, tomek links) that integrate directly with scikit-learn's pipeline API for seamless preprocessing. Implements statistical and distance-based techniques to generate synthetic minority samples or remove noisy majority instances while maintaining data integrity. Supports TensorFlow and Keras models alongside traditional scikit-learn estimators for end-to-end imbalanced data workflows.
7,090 stars. Used by 23 other packages. Available on PyPI.
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7,090
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1,328
Language
Python
License
MIT
Category
Last pushed
Feb 02, 2026
Commits (30d)
0
Dependencies
6
Reverse dependents
23
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