# LCE: An Augmented Combination of Bagging and Boosting in Python

arXiv: https://arxiv.org/abs/2308.07250 · HTML: https://mlsanity.com/paper/2308.07250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification](https://mlsanity.com/paper/2005.03645/) (2020)

## Abstract (arXiv)

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.