Boosted Tree

Boosted trees are ensemble machine learning methods that combine multiple decision trees to achieve high predictive accuracy, particularly on tabular data. Current research focuses on improving interpretability and efficiency through techniques like functionally-identical pruning, developing novel boosting algorithms such as diffusion boosted trees, and exploring variations like subagging and lassoed boosting to enhance performance and convergence rates. These advancements are significant because they address the inherent "black box" nature of boosted trees, enabling wider application in domains requiring explainable AI and faster inference times, while also pushing the boundaries of predictive power in various fields.