通过使用PCA支持的梯度提升和随机森林算法预测水牛 (Bubalus bubalis) 在墨西哥东南部养的体重
Armando Gomez-Vazquez1, Cem Tırınk2, Alvar Alonzo Cruz-Tamayo3
1División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86280, Tabasco, Mexico.
Animals : an open access journal from MDPI
|January 23, 2024
概括
这项研究使用机器学习和主要组件分析 (PCA) 来估计水牛的体重. 渐变增强优于随机森林,为可持续的畜牧业提供了更可靠的方法.
科学领域:
- 动物科学动物科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确估计体重 (BW) 对于有效的畜牧业和管理至关重要.
- 传统的BW估计方法可能是劳动密集型和不那么精确.
- 先进的计算技术为更高效和可靠的BW预测提供了潜力.
研究的目的:
- 用机器学习技术估计水牛的体重 (BW).
- 应用主要组件分析 (PCA) 来进行尺寸缩小和特征选择.
- 为了比较梯度提升和随机森林算法的预测性能,用于BW估计.
主要方法:
- 从水牛身上收集了身体尺寸.
- 使用主要组件分析 (PCA) 来减少数据的维度,并确定关键变量.
- 使用PCA衍生组件开发了两种机器学习模型,即渐变增强和随机森林.
- 模型性能使用R2,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 度量来评估.
主要成果:
- 主要组件分析 (PCA) 有效地降低了数据的复杂性,促进了更快的模型训练.
- 与随机森林模型相比,渐变增强模型表现出更高的性能.
- 在BW预测中,渐变增强实现了更高的R2值和更低的错误率 (RMSE,MAE).
- 该研究证实了PCA支持的机器学习对BW估计的有效性.
结论:
- 主要组件分析 (PCA) 提高了机器学习模型的可靠性,用于估计水牛的体重.
- 在这个特定的预测任务中,渐变增强算法比随机森林更有效.
- 机器学习方法,特别是PCA的渐变增强,显示出通过改进决策来支持可持续畜牧业的巨大潜力.
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