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English(EN) Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions

Quadrature-TreeSHAP 提供更快、更稳定的AI模型解释

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Quadrature-TreeSHAP，一种用于解释树集成预测的新颖方法，该方法独立于深度，并且比现有方法更具数值稳定性。这项新技术自然地扩展到高阶Shapley交互值，并利用基于求积的重构来实现高效计算。实证评估表明，Quadrature-TreeSHAP在Shapley值和交互计算的速度方面均显著优于TreeSHAP和GPUTreeSHAP。 AI

影响 引入了一种更高效、更稳定的解释基于树的模型的方法，有望提高机器学习应用的可解释性。

排序理由 这是一篇介绍机器学习模型预测新方法的论文。[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 →

Quadrature-TreeSHAP 提供更快、更稳定的AI模型解释

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ron Wettenstein, Rory Mitchell, Peng Yu ·

    Quadrature-TreeSHAP：与深度无关的TreeSHAP和Shapley交互

    arXiv:2605.04497v1 Announce Type: new Abstract: Shapley values are a standard tool for explaining predictions of tree ensembles, with Path-Dependent SHAP being the most widely used variant. Despite substantial progress, existing methods still exhibit trade-offs between depth-depe…

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