New PINE method boosts tree ensemble compression by 30%

Researchers have developed PINE, a novel pruning method for tree ensembles designed to improve compression ratios while maintaining prediction consistency within an in-distribution region. Unlike existing faithful pruning methods that preserve equivalence across the entire input space, PINE focuses on a calibrated in-distribution area, allowing for greater compression. Experiments on 12 datasets demonstrated that PINE can achieve up to a 30% improvement in compression ratio while keeping predictions comparable to current faithful methods. AI

RANK_REASON This is a research paper detailing a new method for pruning machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

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