Tree Ensemble

Tree ensembles are powerful machine learning models that combine multiple decision trees to improve predictive accuracy and robustness. Current research focuses on enhancing interpretability through techniques like rule extraction, feature importance quantification (e.g., using Shapley values), and visualization methods, while simultaneously addressing challenges such as computational efficiency for large ensembles and the development of robust and verifiable models. These advancements are significant because they improve the trustworthiness and applicability of tree ensembles across diverse fields, from improving the efficiency of existing models to enabling their use in safety-critical applications requiring explainability and verifiability.