Gradient Boosted Tree

Gradient boosted trees (GBTs) are ensemble learning methods that combine multiple decision trees to create powerful predictive models, primarily for tabular data. Current research emphasizes extending GBTs beyond traditional supervised learning, exploring applications in generative modeling, reinforcement learning, and probabilistic forecasting, often incorporating techniques like conditional flow matching and diffusion models. This versatility, coupled with GBTs' inherent interpretability and efficiency, makes them a valuable tool across diverse fields, from retail forecasting and fraud detection to high-energy physics and personalized medicine. Furthermore, ongoing work focuses on improving the efficiency and scalability of GBTs, as well as enhancing their explainability through methods like feature attribution and rule extraction.