On Wednesday, February 26th from 3pm to 4pm ET, join Amanda Brucker, PhD, Biostatistician III for Duke BERD Methods Core at Duke University School of Medicine provides an overview of Extreme Gradient Boosting (XGBoost), a modern machine-learning algorithm, and will walk through an example R workflow that applies XGBoost to a supervised learning task. This seminar will demonstrate how to implement XGBoost along with several benchmark predictive modeling methods, and will compare the methods on predictive performance and interpretability.
Co-sponsored by the Duke Department of Biostatistics and Bioinformatics and the Duke Biostatistics, Epidemiology, and Research Design (BERD) Methods Core, this event is also being promoted by the NC BERD Consortium. The Consortium is a collaboration of the CTSA-funded BERD cores at UNC-Chapel Hill, Wake Forest University School of Medicine, and Duke University School of Medicine.