适应性抽样方法有助于确定可靠的数据集大小,用于基于证据的建模
Tim Breitenbach1, Thomas Dandekar1
1Lehrstuhl für Bioinformatik, Biozentrum, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
Frontiers in bioinformatics
|September 22, 2025
概括
确保模型可靠性需要足够的数据. 本研究介绍了一种方法,通过分析趋同速度来确定必要的数据大小,减少采样变化,以得出可靠的模型性能结论.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 数据科学数据科学数据科学
背景情况:
- 在未见的数据上评估模型可靠性对于可靠的预测至关重要.
- 确定适当的样本大小可以防止数据集之间存在重大结论差异.
- 大数定律表明,假设没有数据漂移,模型准确性与足够的数据趋同.
研究的目的:
- 系统地检查数据大小与模型准确度增长之间的关系.
- 开发一种启发式方法来研究与样本大小相对的收速度.
- 通过尽量减少采样偏差,确保对数据要求做出可靠的结论.
主要方法:
- 采用采样方法来估计数据样本大小的趋同速度.
- 实施了一种自动化方法来确定最佳的重复次数以稳定结果.
- 减少采样偏差低于预定义的值以确保结果的一致性.
主要成果:
- 建立了一种方法来分析随着数据大小的增加而趋于模型的速度.
- 演示了如何在多个采样运行中稳定收速度结果.
- 量化了采样偏差的减少,以确保可靠的数据要求结论.
结论:
- 呈现的启发式方法可靠地确定了足够的数据大小,以实现模型稳定性.
- 自动重复确定提高了关于数据需求的结论的可靠性.
- 这种方法确保预测仍然可靠的看不见的数据和模型结论是强大的.
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