Tree Model

Tree models, representing hierarchical or branching structures, are central to diverse fields, from phylogenetic analysis to machine learning. Current research focuses on improving the efficiency and accuracy of tree fitting algorithms, particularly for high-dimensional data and non-tree-like structures, exploring methods like $\ell_1$-hyperbolic distance minimization and adaptive split balancing in random forests. These advancements enhance the interpretability and performance of tree-based models, impacting applications ranging from ETA prediction and graph analysis to sustainable AI through energy-efficient hardware implementations.