Decision Forest

Decision forests, ensembles of decision trees, are a powerful machine learning technique used for classification and regression tasks, with recent research focusing on improving their accuracy, interpretability, and efficiency. Current efforts involve developing novel architectures like deep forests and model-based rule forests to enhance performance and address the "black box" nature of some models, alongside optimizations for faster inference and data reduction techniques tailored to specific applications like real-time data selection in autonomous driving. These advancements are impacting diverse fields, from healthcare (subgroup analysis for personalized treatments) to legal systems (predicting court delays) and signal processing (EMG classification), demonstrating the broad applicability and ongoing refinement of decision forest methods.