使用皮尔森相关系数作为衡量定量特征预测准确性的唯一指标:这是足够的吗?
Shouhui Pan1,2, Zhongqiang Liu1,2, Yanyun Han1,2
1Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Frontiers in plant science
|December 25, 2024
概括
评估定量特征预测准确度至关重要. 皮尔森公司 (Pearson) 的
科学领域:
- 植物育种和遗传学
- 生物信息学是一种生物信息学.
- 统计建模 统计建模
背景情况:
- 准确的定量特征预测对于选择最佳植物育种模型至关重要.
- 皮尔森相关系数 (PCC) 是常用的,但可能无法完全捕捉预测准确性,特别是复杂的模型.
- 过度依赖PCC可以导致在定量特征预测研究中模型评估不足.
研究的目的:
- 批判性地评估皮尔森相关系数 (PCC) 在评估定量特征预测准确性的局限性.
- 将PCC与使用各种预测方法的9个替代指标进行比较.
- 提高植物定量特征预测模型的可靠性和实际应用.
主要方法:
- 对皮尔森相关系数 (PCC) 和十个评估指标进行比较分析.
- 利用了四种传统和四种基于机器学习的定量特征预测方法.
- 通过典型的案例研究证明了PCC的局限性.
主要成果:
- 皮尔森相关系数 (PCC) 单独提供了一个不完整的预测准确性的评估,特别是对于非线性关系.
- 机器学习模型可以显示PCC和其他指标之间的差异.
- 该研究强调了使用PCC作为唯一评估指标的不足.
结论:
- 皮尔森相关系数 (PCC) 应与其他指标一起用于全面的定量特征预测模型评估.
- 补充指标的选择应根据特定的应用场景量身定制.
- 采用多度指标方法可以减轻植物育种中误导性结论的风险.
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