Gradient boosting is a powerful machine learning technique used for regression and classification problems. It builds models in a stage-wise fashion and generalizes them by allowing the optimization of an arbitrary differentiable loss function. In epidemiology, it can be particularly useful for predicting health outcomes and understanding the factors driving these outcomes.
