imbalanced-learn and machine-learning-imbalanced-data
The first is a mature, production-ready resampling and algorithmic library for handling class imbalance, while the second is an educational repository teaching imbalance techniques using that library as a dependency—making them complementary rather than competitive.
About imbalanced-learn
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.
About machine-learning-imbalanced-data
solegalli/machine-learning-imbalanced-data
Code repository for the online course Machine Learning with Imbalanced Data
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