English(EN) Vectorized Dynamic Histograms for Sparse Oblique Forests

Google的Yggdrasil决策树通过新的直方图技术得到提升

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了矢量化动态直方图，以显著加快稀疏斜（SPO）决策树的训练速度。这些新方法已集成到Google的Yggdrasil Decision Forests库中，将SPO-RF的训练效率提高了2倍，SPO-GBT提高了1.6倍，使得SPO-RF的训练时间与轴对齐随机森林相当。该优化在海量数据集上进行了评估，并且该实现已开源。 AI

影响加速了稀疏斜决策树的训练，可能在机器学习应用中实现更复杂的模型和更快的迭代。

排序理由关于决策树训练新颖优化技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [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 · 2026-10-02 04:00

用于稀疏斜向森林的向量化动态直方图

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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报道来源 [1]

用于稀疏斜向森林的向量化动态直方图

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