LCE: An Augmented Combination of Bagging and Boosting in Python
This work provides a scalable and user-friendly Python package for machine learning practitioners, though it appears incremental as it builds on existing ensemble methods.
The authors tackled the problem of improving prediction performance for classification and regression by introducing LCE, a method that combines bagging and boosting, resulting in enhanced performance over state-of-the-art methods like Random Forest and XGBoost.
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.