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New PINE method boosts tree ensemble compression by 30%

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PINE method boosts tree ensemble compression by 30%

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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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  1. arXiv cs.LG TIER_1 English(EN) · Haruki Yajima, Yusuke Matsui ·

    PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence

    arXiv:2605.28068v1 Announce Type: new Abstract: Tree ensembles are machine learning models with strong predictive performance and interpretability, and remain widely used for tabular data. Standard pruning methods for tree ensembles typically optimize an accuracy-compression trad…

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