Hoeffding Tree

Hoeffding Trees are a type of decision tree particularly suited for online learning from data streams, leveraging Hoeffding's inequality to efficiently build models with limited memory and computational resources. Current research focuses on improving their accuracy and efficiency, including developing variations like Green Accelerated Hoeffding Trees for energy-constrained environments and adapting them for continual learning scenarios with change point detection. These advancements are significant for applications requiring real-time decision-making on resource-limited devices, such as embedded systems and data stream processing in areas like natural gas consumption forecasting.