Mondrian Forest

Mondrian Forests are ensemble methods designed for efficient and memory-conscious classification, particularly valuable for handling large or streaming datasets. Current research focuses on optimizing Mondrian Forest performance under memory constraints, exploring strategies like dynamic ensemble size adjustment and out-of-memory data handling techniques to maintain accuracy while minimizing resource usage. These advancements are significant for deploying machine learning models on resource-limited devices like those found in the Internet of Things, enabling real-time classification in applications with limited computational power.