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English(EN) Performance metrics in machine learning: a scenario-based guide from the confusion matrix to calibration, regression and uncertainty

arXiv论文指导机器学习性能指标

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发布的arXiv教程论文提供了机器学习性能指标的全面指南。它详细介绍了准确率、精确率和ROC-AUC等指标如何根据类别流行度、决策阈值和聚合规则等因素得出不同结论。该论文将22个混淆矩阵指标组织成一个流行度编码图，并使用15个场景来说明分类、回归和不确定性估计中的概念，所有验证检查均通过。 AI

影响 提供了一个理解和选择适当的机器学习评估指标的框架，这对于准确的模型评估至关重要。

排序理由 该集群包含一篇详细介绍机器学习方法的学术论文。[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 →

arXiv论文指导机器学习性能指标

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该集群包含一篇详细介绍机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mehmet Baygin, Sengul Dogan, Turker Tuncer ·

    机器学习中的性能指标：从混淆矩阵到校准、回归和不确定性的场景化指南

    arXiv:2611.08048v1 Announce Type: new Abstract: Performance metrics can yield different conclusions from the same predictions. This methodological tutorial explains the effects of class prevalence, decision thresholds, aggregation rules and software conventions through controlled…

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