Local Cascade Ensemble package

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  • License: Apache Software License (Apache-2.0)
  • Maintainer: Kevin Fauvel
  • Requires: Python >=3.8
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Local Cascade Ensemble (LCE) is a high-performing, scalable and user-friendly machine learning method for the general tasks of Classification and Regression.
In particular, LCE:

  • Enhances the prediction performance of Random Forest and XGBoost by combining their strengths and adopting a complementary diversification approach

  • Supports parallel processing to ensure scalability

  • Handles missing data by design

  • Adopts scikit-learn API for the ease of use

  • Adheres to scikit-learn conventions to allow interaction with scikit-learn pipelines and model selection tools

  • Is released in open source and commercially usable - Apache 2.0 license

An article introducing LCE and illustrative code examples has been published in Towards Data Science.

Installation

LCE package can be installed using pip:

pip install lcensemble

Documentation

LCE documentation, including API documentation and general examples, can be found here.

Reference

The full information about LCE can be found in the associated journal paper:

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