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收缩和基于树的回归方法用于使用形态特征预测阿卡拉曼羊的活体重

Hulya Ozen¹, Dogukan Ozen², Afsin Kocakaya³

¹Gulhane Faculty of Medicine, Department of Medical Informatics, University of Health Sciences, Ankara, 06018, Türkiye.

Tropical animal health and production

|October 15, 2024

概括

随机森林 (RF) 准确地使用形态特征预测阿卡拉曼羊的活体重. 这种方法提供了一种数据驱动的方法,用于改进动物育种决策.

科学领域:

动物科学动物科学
农业工程农业工程
机器学习在农业中的应用

背景情况:

准确的活体重 (LW) 预测对于畜牧管理至关重要,包括繁殖和生长监测.
为了预测动物特征,可以使用各种回归方法,包括收缩和基于树的方法.

研究的目的:

预测阿卡拉曼羊在6个月大时的活体重 (LW).
为了比较不同回归模型 (Ridge,Lasso,弹性网,CART,RF) 对于LW预测的性能.
确定影响阿卡拉曼羊LW预测的关键形态特征.

主要方法:

使用了收缩方法 (Ridge,Lasso,弹性网) 和基于树的回归方法 (CART,RF).
数据集包括100只阿卡拉曼羊 (44只雄羊,56只母羊),测量了性别,出生体重 (BW) 和形态特征 (WH,CD,BL,CW,RH,CC).
在80%的培训组中进行了10倍的交叉验证,并在20%的测试组中评估了绩效.

主要成果:

活体重 (LW) 与所有形态特征和出生体重 (BW) 有显著的相关性.
随机森林 (RF) 以0.865的R2表现出卓越的表现,超过了Ridge (0.761),Lasso (0.755),弹性网 (0.750) 和CART (0.654).
胸部高度 (WH) 和胸部深度 (CD) 是RF模型中最有影响力的预测因素,而性别和BW贡献最小.

结论:

随机森林 (RF) 被推为预测阿卡拉曼羊的活体重的最佳模型.
该研究提供了数据驱动的基础,以加强动物育种计划的决策过程.
形态特征是羊活体重估计的非常有价值的预测指标.

关键词:

阿卡拉曼羊是阿卡拉曼的羊.活体重量活体重量机器学习机器学习形态特征形态特征收缩回归方法的收缩回归方法基于树的回归方法.

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