imbalanced-learn and imbalanced-ensemble
Imbalanced-learn provides foundational resampling and individual algorithm techniques (SMOTE, random undersampling, etc.), while imbalanced-ensemble builds specialized ensemble methods on top of these primitives to handle class imbalance at the ensemble level, making them complementary tools that can be used together in a machine learning pipeline.
About imbalanced-learn
scikit-learn-contrib/imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
This tool helps data scientists and machine learning engineers build more accurate predictive models when their datasets have unequal numbers of examples across different categories. It takes a raw, imbalanced dataset and processes it using various re-sampling techniques to create a more balanced dataset, which then leads to improved model performance, especially for the under-represented categories. This is particularly useful for tasks where correctly identifying rare events is critical.
About imbalanced-ensemble
ZhiningLiu1998/imbalanced-ensemble
🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库 [NeurIPS'25]
This tool helps data professionals build more accurate predictive models when their datasets have very skewed categories, like detecting a rare disease or fraudulent transactions. It takes your existing tabular data and applies advanced techniques to balance the categories, outputting a more robust and reliable classification model. Data scientists, machine learning engineers, and analysts working with real-world, messy datasets will find this valuable.
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