LCE: An Augmented Combination of Bagging and Boosting in Python

TL;DR

This paper introduces LCE, a Python package that combines bagging and boosting techniques to improve classification and regression performance, compatible with scikit-learn and enhancing existing ensemble methods.

Contribution

The paper presents Local Cascade Ensemble (LCE), a novel ensemble method that enhances Random Forest and XGBoost by combining their strengths with a diversification approach.

Findings

LCE outperforms traditional ensemble methods in accuracy.

LCE is scalable and integrates seamlessly with scikit-learn.

The package is user-friendly and suitable for various tasks.

Abstract

lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LCE), a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost. LCE combines their strengths and adopts a complementary diversification approach to obtain a better generalizing predictor. The package is compatible with scikit-learn, therefore it can interact with scikit-learn pipelines and model selection tools. It is distributed under the Apache 2.0 license, and its source code is available at https://github.com/LocalCascadeEnsemble/LCE.