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English(EN) Enhancing Automated Machine Learning via Homogeneous Train-Test Splitting Methods

新方法改进AutoML数据集划分,以实现更好的模型评估

一篇新的研究论文探讨了在自动化机器学习(AutoML)中划分数据集的方法,以确保更准确的模型评估。该研究将五种现有策略,包括随机划分和分层抽样,与Kennard-Stone、Duplex和SPXY等几何方法进行了比较。研究人员发现,几何方法可能由于接近零的相似度得分而导致性能估计不稳定。他们提出了一种新的“Optimised-Distribution”方法,该方法优先考虑统计相似性,达到了89.0%的平均MMD相似度得分,高于其他评估策略。 AI

影响 这项研究可能导致AutoML中更可靠的模型性能估计,尤其是在处理具有复杂分布的数据集时。

排序理由 arXiv上发表的研究论文,详细介绍了AutoML中数据集划分的新方法。

在 arXiv cs.LG 阅读 →

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新方法改进AutoML数据集划分,以实现更好的模型评估

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arXiv上发表的研究论文,详细介绍了AutoML中数据集划分的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yearn Tan Yin Tze, Charles Grellois ·

    通过同质训练-测试划分方法增强自动化机器学习

    arXiv:2607.26625v1 Announce Type: new Abstract: Accurate model evaluation in machine learning depends critically on how datasets are split into training and testing subsets. Standard random splitting assumes that both partitions share the same underlying distribution, an assumpti…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    通过同质训练-测试划分方法增强自动化机器学习

    Accurate model evaluation in machine learning depends critically on how datasets are split into training and testing subsets. Standard random splitting assumes that both partitions share the same underlying distribution, an assumption often violated in datasets with class imbalan…