imbalanced-learn

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.