Enhances the prediction performance of Random Forest and XGBoost by combining their strengths and adopting a complementary diversification approach
Supports parallel processing to ensure scalability
Handles missing data by design
Adopts scikit-learn API for the ease of use
Adheres to scikit-learn conventions to allow interaction with scikit-learn pipelines and model selection tools
Is released in open source and commercially usable - Apache 2.0 license
An article introducing LCE and illustrative code examples has been published in Towards Data Science.
Installation
LCE package can be installed using pip:
pip install lcensemble
Documentation
LCE documentation, including API documentation and general examples, can be found here.
Reference
The full information about LCE can be found in the associated journal paper:
1 maintainer
Metadata
1 maintainer
Release files for lcensemble 0.3.4
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