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揭开ML驱动科学中的过度乐观和出版偏见
Pouria Saidi1, Gautam Dasarathy1, Visar Berisha1,2
1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA.
Patterns (New York, N.Y.)
|April 23, 2025
概括
发表的机器学习 (ML) 模型性能往往过于乐观,因为过度拟合和出版偏见. 本研究引入了一个模型来纠正这些偏见,估计真实的学习曲线和ML应用的现实预测极限.
科学领域:
- 计算科学是一种计算科学.
- 生物医学信息学是生物医学信息学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 机器学习 (ML) 模型在各个学科中显示出令人印象深刻的结果,但报告的表现往往过于乐观.
- 样本大小和ML模型中报告的准确性之间的反向关系与学习曲线理论相矛盾,引发了有效性问题.
研究的目的:
- 调查导致ML驱动科学过度乐观的因素,特别是过度拟合和出版偏见.
- 开发一个框架来估计潜在的学习曲线,并从已公布的ML结果提供现实的绩效评估.
主要方法:
- 引入了观察准确性的随机模型,整合了参数学习曲线与过拟合和出版偏差.
- 为观察到的数据构建了一个偏差校正估计器.
- 将模型应用于对神经疾病分类的元分析.
主要成果:
- 拟议的框架可以从已公布的ML结果准确地估计潜在的学习曲线.
- 该模型提供了现实的绩效评估,纠正过度乐观.
- 在神经疾病分类领域的ML驱动预测的估计内在极限.
结论:
- 发表的ML性能过度乐观是一个重大问题,由过度拟合和出版偏见驱动.
- 开发的随机模型和估计器提供了一种可靠的方法来评估真正的ML模型性能.
- 这种方法对于了解ML在科学应用中的真正预测能力和局限性至关重要,特别是在医疗保健中.
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