Tree Based Model

Tree-based models are a class of machine learning algorithms used for classification and regression tasks, prized for their interpretability and often superior performance on tabular data compared to deep learning methods. Current research focuses on enhancing their explainability through techniques like Shapley values and algorithmic recourse, improving their efficiency and scalability for large datasets and federated learning settings, and extending their applicability to time series forecasting and other complex data structures. These advancements are significant for various fields, improving the accuracy and transparency of predictive models in applications ranging from healthcare and finance to environmental science and high-energy physics.