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Forest Compression

/ˈfɒr.ɪst kəmˈprɛʃ.ən/noun
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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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