Welcome to imbalanced-ensemble documentation!
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Date: Nov 25, 2021 Version: 0.1.6
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.
Getting Started
API
Examples