Welcome to imbalanced-ensemble documentation!ΒΆ


Documentation Status

[Github] [Gallery] [PyPI] [Changelog] [Source] [Download]

Date: Jun 14, 2021 Version: 0.1.4

imbalanced-ensemble (IMBENS, imported as imbalanced_ensemble) is a Python toolbox for quick implementing and deploying ensemble learning algorithms on class-imbalanced data. It was built on the basis of scikit-learn and imbalanced-learn. IMBENS includes more than 15 ensemble imbalanced learning (EIL) algorithms, from the classical SMOTEBoost (2003) and RUSBoost (2010) to recent SPE (2020), from resampling-based methods to cost-sensitive ensemble learning.

IMBENS is featured for:

  • Unified, easy-to-use APIs, detailed documentation and examples.

  • Capable for multi-class imbalanced learning out-of-box.

  • Optimized performance with parallelization when possible using joblib.

  • Powerful, customizable, interactive training logging and visualizer.

  • Full compatibility with other popular packages like scikit-learn and imbalanced-learn.