LCE: An Augmented Combination of Bagging and Boosting in Python

• URL: http://arxiv.org/abs/2308.07250v2
• Date: Tue, 15 Aug 2023 19:08:36 GMT
• Title: LCE: An Augmented Combination of Bagging and Boosting in Python
• Authors: Kevin Fauvel, \'Elisa Fromont, V\'eronique Masson, Philippe Faverdin and Alexandre Termier
• Abstract summary: lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. Local Cascade Ensemble (LCE) is a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost.
• Score: 45.65284933207566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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