imbalanced-ensemble
A Python library for class-imbalanced ensemble learning with 30+ algorithms, built on scikit-learn.
What is imbalanced-ensemble?
IMBENS is a Python library designed for class-imbalanced ensemble learning, providing over 30 algorithms to tackle datasets with unequal class distributions. It solves the problem of biased model performance in imbalanced scenarios, such as fraud detection or rare disease prediction, by integrating techniques like under-sampling, over-sampling, and cost-sensitive learning into ensemble frameworks.
Data scientists and machine learning engineers working on classification problems with imbalanced datasets, particularly in research or applied settings where benchmarking and deploying advanced ensemble methods are required.
Developers choose IMBENS for its comprehensive, scikit-learn-compatible toolkit that combines ease of use with extensive functionality, including multi-class support, visualization, and parallelized training, making it a go-to solution for rigorous imbalanced learning workflows.
Overview
[NeurIPS'25]🛠️Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库
Use Cases
Best For
- Benchmarking multiple class-imbalanced learning algorithms on tabular data
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