Tree Based Ensemble

Tree-based ensemble methods, such as Random Forests, Gradient Boosting Machines, and XGBoost, are powerful machine learning techniques used for classification and regression tasks, primarily aiming to improve predictive accuracy and robustness compared to single decision trees. Current research focuses on enhancing interpretability through novel visualization techniques and graph-based representations of the ensemble's decision-making process, as well as improving efficiency through quantum-enhanced retraining algorithms and optimized inference methods. These advancements are significant for various applications, including healthcare, finance, and industrial IoT security, where accurate, efficient, and interpretable models are crucial for decision-making and risk assessment.