imbalanced-learn
Imbalanced-learn is an open-source library that enhances scikit-learn’s capabilities by providing specialized tools for handling classification tasks with imbalanced datasets. Version 0.13.0, released on December 20, 2024, offers user-friendly guides, extensive API documentation, and practical examples to facilitate effective implementation and contributions.
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Top imbalanced-learn Features
- Open source MIT-licensed
- Integration with scikit-learn
- Tools for imbalanced classification
- User-friendly installation guide
- In-depth user guide
- Comprehensive reference guide
- Example gallery for visualization
- Support for various sampling techniques
- Customizable method parameters
- Active community support
- Detailed contribution guidelines
- Compatibility with Python libraries
- Advanced preprocessing techniques
- Multi-class imbalance handling
- Evaluation metrics for imbalance
- Cross-validation strategies included
- Documentation for beginners
- Performance optimization tools
- Frequent updates and maintenance.
