使用XGBoost和MARS算法来预测Kajli羊品种的体重
Asim Faraz1, Cem Tırınk2, Hasan Önder3
1Department of Livestock and Poultry Production, Bahauddin Zakariya University, Multan, Pakistan.
Tropical animal health and production
|July 27, 2023
概括
XGBoost和多变量自适应回归脊柱 (MARS) 算法使用身体测量准确预测动物的体重. XGBoost在重量预测方面表现出卓越的可靠性和准确性,在没有秤的情况下提供了有价值的工具.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 动物养殖 动物养殖
背景情况:
- 准确的体重估计对于动物管理和繁殖至关重要.
- 传统的称重方法在某些环境中可能不切实际.
- 机器学习算法为非侵入性体重预测提供了潜力.
研究的目的:
- 评估XGBoost和MARS算法用于从身体测量中预测动物体重.
- 确定最准确,最可靠的预测模型.
- 了解身体尺寸和体重之间的关系.
主要方法:
- 在152只动物的数据集上使用了XGBoost和MARS算法.
- 采用各种适合性标准 (R2,r,RMSE,CV,SD_ratio,PI,MAPE,AIC) 来进行模型比较.
- 评估了算法模拟人体尺寸和体重之间的非线性关系的能力.
主要成果:
- XGBoost算法被证明是预测当前体重的最可靠和最准确的模型.
- 此外,MARS算法还证明了重量预测的可靠性.
- 这两种算法都显示出模拟身体尺寸和体重之间的复杂关系的潜力.
结论:
- XGBoost 是一个有效,高效和可靠的工具,用于估计动物的体重从身体测量.
- 这种预测能力在农村地区等资源有限的环境中尤为有价值.
- 这些发现可以帮助研究人员和育种者更好地控制体重,了解动物生理学.
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