使用梯度增强回归技术和高斯过程增强回归与Shapley添加式解释解释人工智能,MLflow及其容器化,在规模上预测牛群的干物摄入量
K E ArunKumar1,2, Nathan E Blake1,2, Matthew Walker2,3,4
1School of Agriculture and Food Systems, Davis College of Agriculture and Natural Resources, West Virginia University, Morgantown, WV, USA.
Journal of animal science
|February 13, 2025
概括
现在可以使用机器学习 (ML) 准确预测肉牛干物质摄入量 (DMI). 高斯过程提振模型为畜牧管理和生态系统服务信贷提供了可靠的解决方案.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 动物科学动物科学
背景情况:
- 干物质摄入量 (DMI) 对于畜牧管理至关重要,但准确测量放牧牛是具有挑战性的.
- 现有的干地DMI量化的方法很昂贵,不适用于放牧系统.
- 机器学习 (ML) 为预测DMI提供了一个潜在的解决方案,提高效率并使参与生态系统服务计划成为可能.
研究的目的:
- 开发和评估机器学习模型,用于预测肉牛的个人干物质摄入量 (DMI).
- 为了比较各种梯度增强方法与基线模型的性能.
- 为DMI预测建立一个可扩展和可重复的ML工作流.
主要方法:
- 利用了西弗吉尼亚大学 (2019-2020) 178 头肉牛的 12,056 份每日记录的数据集.
- 探索了梯度增强回归 (GBR),LightGBM (LGB),XGBoost (XGB) 和高斯过程增强 (GPBoost) 模型.
- 使用MLflow和Docker开发了一个端到端的MLOps管道,以简化ML操作和部署.
主要成果:
- 与其他模型相比,高斯过程提振 (GPBoost) 模型表现出优异的性能,具有最佳偏差和差异.
- 优化的GPBoost模型在测试数据上实现了RMSE值,从1.18到1.54公斤不等.
- GPBoost模型实现了0.58 (训练) 和0.55 (测试) 的R平方值,MAE值为0.92公斤 (训练) 和0.90公斤 (测试),表明了良好的概括性.
结论:
- GPBoost模型提供了一种可靠和准确的方法来预测肉牛DMI,其性能优于传统和其他ML方法.
- 开发的MLOps管道确保了DMI预测模型的可复制性,可扩展性和无部署.
- 使用机器学习准确的DMI预测可以提高牲畜管理效率,并促进生产者参与生态系统服务信贷计划.
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