Fair Bagging Boosting Models [SWR-24-38]
Juliette Ugirumurera, Joseph Severino, Benson, Erik · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2024
Fair Bagging Boosting Models is a software implementation of a framework for building, measuring bias and correcting bias in 3 popular forest machine learning models: gradient boosted trees (GBT), random forest (RF), and XGBoost models, using the XGBoost library. The framework takes advantage of the flexibility in XGBoost library to represent gradient boosted tree and random forest models, as well as the ability to use custom loss function.