Local Cascade Ensemble package
Project description
Local Cascade Ensemble (LCE) is a machine learning method that further enhances the prediction performance of the state-of-the-art Random Forest and XGBoost. LCE combines their strengths and adopts a complementary diversification approach to obtain a better generalizing predictor. Specifically, LCE is a hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. LCE has been evaluated on a public benchmark and published in the journal Data Mining and Knowledge Discovery.
LCE package is compatible with scikit-learn; it passes the check_estimator. Therefore, it can interact with scikit-learn pipelines and model selection tools.
Installation
LCE package can be installed using pip:
pip install lcensemble
or conda:
conda install -c conda-forge lcensemble
Documentation
LCE documentation can be found here.
Reference
The full information about LCE can be found in the associated journal paper.
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