Tree Ensemble Model

Tree ensemble models, which combine multiple decision trees to improve predictive accuracy, are a powerful machine learning tool currently experiencing significant research interest. Active research focuses on enhancing their interpretability, a crucial aspect for building trust and understanding model behavior, through techniques like graph-based visualizations and the creation of simpler, surrogate models. These advancements are impacting diverse fields, from improving intrusion detection in industrial IoT systems to enhancing epidemiological forecasting by combining multiple mechanistic models and optimizing their ensemble performance. The overall goal is to leverage the high accuracy of ensemble methods while simultaneously addressing their inherent "black box" nature.