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Google's Yggdrasil Decision Forests boosted by new histogram techniques

By PulseAugur Editorial · [1 sources] ·

Researchers have developed vectorized dynamic histograms to significantly speed up the training of sparse oblique (SPO) decision forests. These new methods, integrated into Google's Yggdrasil Decision Forests library, improve training efficiency by up to 2x for SPO-RF and 1.6x for SPO-GBT, making SPO-RF training time comparable to axis-aligned random forests. The optimizations were evaluated on extensive datasets and the implementation has been open-sourced. AI

IMPACT Accelerates training for sparse oblique decision forests, potentially enabling more complex models and faster iteration in machine learning applications.

RANK_REASON Academic paper detailing novel optimization techniques for decision forest training. [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 →

Google's Yggdrasil Decision Forests boosted by new histogram techniques

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Academic paper detailing novel optimization techniques for decision forest training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ariel Lubonja, Jungsang Yoon, Haoyin Xu, Yue Wan, Yilin Xu, Richard Stotz, Mathieu Guillame-Bert, Joshua T. Vogelstein, Randal Burns ·

    Vectorized Dynamic Histograms for Sparse Oblique Forests

    arXiv:2603.00326v2 Announce Type: replace Abstract: Sparse oblique (SPO), part of the top-ranked configuration of Google's Yggdrasil Decision Forests (YDF), improve the accuracy while maintaining interpretability of Random Forests (RF) and Gradient Boosted Trees (GBT) by scanning…

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