Linear Model Tree

Linear Model Trees (LMTs) combine the interpretability of decision trees with the ability of linear models to capture complex relationships within data, aiming to improve both predictive accuracy and model understanding. Current research focuses on developing efficient algorithms for LMT construction, such as greedy approaches with regularization and optimized feature selection, and exploring their applications in diverse fields including process monitoring, graph neural networks, and approximating complex models like deep reinforcement learning agents. The resulting models offer a balance between predictive power and explainability, making them valuable tools for applications where both accuracy and interpretability are crucial.