Gradient Boosting Machine

Gradient boosting machines (GBMs) are powerful machine learning algorithms that combine multiple weak learners, typically decision trees, to create a strong predictive model. Current research focuses on improving both the accuracy and reliability of GBMs, including developing novel algorithms like trust-region methods that balance computational efficiency with broad applicability to various loss functions and exploring Bayesian approaches to better quantify predictive uncertainty. These advancements are significant because they enhance the robustness and trustworthiness of GBMs, making them more suitable for high-stakes applications where reliable uncertainty estimates are crucial.