Tree Based

Tree-based methods are a cornerstone of machine learning, offering strong predictive performance and, in some cases, interpretability, with primary objectives focused on improving accuracy, explainability, and efficiency. Current research emphasizes enhancing existing algorithms like random forests and gradient boosting machines, exploring variations such as enriched functional trees and explainable ensemble trees (E2Tree), and developing novel architectures for specific applications (e.g., uplift modeling). These advancements are significant for various fields, improving prediction accuracy in diverse domains (e.g., healthcare, finance, and manufacturing) and providing more transparent and trustworthy models for decision-making.