Forest Compression
Forest compression refers to a technique in computer science and machine learning for reducing the size and complexity of ensemble models like random forests, which are collections of decision trees used for predictive analytics. This process involves pruning or simplifying the trees to minimize storage needs and improve processing speed, making it essential for deploying AI on devices with limited resources such as smartphones or embedded systems.
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In a 2019 study by researchers at Stanford University, applying forest compression to a random forest model reduced its file size by up to 95% while maintaining 98% accuracy, enabling real-time fraud detection on mobile devices that previously required powerful servers.
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