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模拟因果关系是否提高了预测哺乳期牛奶产量的准确性?
Xiao-Lin Wu1,2, Asha M Miles3, Curtis P Van Tassell3
1Council on Dairy Cattle Breeding, Bowie, MD 20716.
JDS communications
|September 20, 2023
概括
选择正确的统计模型是准确估计牛奶产量的关键. 当乳腺炎存在时,经常性神经网络的表现最好,这凸显了模型选择对动物健康和生产的重要性.
科学领域:
- 动物科学动物科学
- 统计建模 统计建模
- 机器学习 机器学习
背景情况:
- 准确估计哺乳期牛奶产量对于乳牛群管理和健康监测至关重要.
- 存在各种统计模型,它们的假设不同,它们的假设是关于诸如牛奶产量和健康状况等变量之间的关系.
研究的目的:
- 为了比较相关性和因果模型的准确性,以估计哺乳期牛奶产量.
- 在不同的条件下评估模型性能,包括乳腺炎的存在.
- 评估模型复杂性和生物假设对预测准确性的影响.
主要方法:
- 模拟的哺乳数据使用木制哺乳曲线作为基准.
- 我们比较了三个相关模型:最佳预测,线性回归和前神经网络.
- 我们比较了两个因果模型:递归结构方程模型和递归神经网络.
- 评估基于预测性能的模型准确性,特别是在有或没有乳腺炎的场景中.
主要成果:
- 个别木质哺乳曲线提供了优秀的哺乳动态的参数解释.
- 最好的预测模型在没有乳腺炎的情况下表现出色,但在乳腺炎存在且未被考虑的情况下表现不佳.
- 经常性神经网络在乳腺炎存在时表现出最高的准确性.
- 因果模型虽然有助于推断因果关系,但在准确捕捉复杂的生物关系方面面临挑战,错误规范导致准确性丧失.
结论:
- 建模因果关系本身并不能保证改善牛奶产量的预测准确性.
- 在实践中,一个节的模型,平衡复杂性和准确性,通常是首选的.
- 选择统计模型和准确计算影响牛奶产量的共变量对于可靠的估计至关重要.
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