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预测罗马诺夫羔羊的断奶体重从生物识别测量断奶年龄之前使用机器学习算法

Mehmet Eroğlu¹, Ali Osman Turgut¹, Mürsel Küçük¹

¹Department of Animal Science, Faculty of Veterinary Medicine, Siirt University, Siirt, Türkiye.

Veterinary medicine and science

|May 28, 2025

概括

机器学习,特别是梯度提升和XGBoost,使用生物识别数据准确预测羊羔断奶体重. 这有助于畜牧管理和育种决策.

科学领域:

农业科学农业科学
数据科学数据科学数据科学
动物科学动物科学

背景情况:

机器学习 (ML) 模型使用历史数据预测未来的结果.
在畜牧业的ML应用包括预测生长率,牛奶生产和繁殖成功.
动物健康,营养和环境数据对于ML预测至关重要.

研究的目的:

调查ML算法的性能,以预测羔羊的断奶重量.
使用罗曼诺夫羊羔30天龄的生物识别测量进行预测.
比较各种ML算法的预测准确度.

主要方法:

生物识别特征 (身体长度,胸围等) 和性被使用.
采用了随机森林 (RF),CART,梯度提升 (GB),XGBoost和CatBoost算法.
数据标准化,80/20列车测试分割,超参数优化 (GridSearchCV) 和使用RMSE,MAE,R2,MAPE,RAE,MAD和SD比率的性能评估.

主要成果:

梯度提升和XGBoost模型表现出卓越的性能.
这些模型在测试数据上实现了最高的R2值和最低的误差指标 (RMSE,MAE,MAPE).
随机森林和CatBoost模型的预测准确性较低,错误率更高.

结论:

梯度提升和XGBoost显示出预测羊羔断奶体重的巨大潜力.
机器学习的洞察力可以改善动物育种和选择策略.
这些发现支持通过数据驱动的决策来加强畜牧管理实践.

关键词:

在XGBoost中使用.梯度增强可以提高梯度.机器学习是机器学习.预测预测预测预测断奶时的体重断奶时的体重

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