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.
@article{arxiv.2308.07250,
title = {LCE: An Augmented Combination of Bagging and Boosting in Python},
author = {Kevin Fauvel and Élisa Fromont and Véronique Masson and Philippe Faverdin and Alexandre Termier},
journal= {arXiv preprint arXiv:2308.07250},
year = {2023}
}