Tree Based Machine Learning

Tree-based machine learning, encompassing algorithms like random forests and gradient boosting, excels at extracting information from structured data, outperforming deep learning in many tabular data applications. Current research focuses on improving model interpretability (e.g., through efficient feature attribution methods like Linear TreeShap), accelerating inference speed (via specialized hardware and algorithmic optimizations like MABSplit), and addressing issues of fairness and bias in model outputs. These advancements are impacting diverse fields, from insurance claims prediction and scientific simulation to real-time systems and material science, by enabling faster, more accurate, and more explainable predictions.