使用多类后勤回归和机器学习方法确定水牛的奶产
Demet Çanga Boğa1, Mustafa Boğa2, Orhan Ermetin3
1Faculty of Economics and Administrative Sciences, Department of Business Administration, Nigde Omer Halisdemir University, Nigde, TR51700, Türkiye. demetcangaboga@ohu.edu.tr.
梯度增强机 (GBM) 在预测水牛牛奶产量方面表现最好,准确度达到64.63%. 这项研究强调了机器学习模型在优化乳制品生产方面的潜力.
科学领域:
- 动物科学动物科学
- 机器学习应用 机器学习应用
- 乳制品生产 乳制品生产
背景情况:
- 准确预测水牛的牛奶产量对于有效的乳业管理至关重要.
- 机器学习模型为分析复杂的生物数据和提高预测准确性提供了有希望的工具.
研究的目的:
- 为了比较评估随机森林,梯度提升机 (GBM),支向量机 (SVM) 和多类后勤回归 (MCLR) 对水牛牛奶产量的预测性能.
- 根据关键性能指标,确定最成功的机器学习模型来预测牛奶产量.
主要方法:
- 该研究使用了RF,GBM和SVM模型的分层8倍交叉验证和超参数调整.
- 数据包括哺乳期,哺乳期牛奶产量,第一次怀孕的年龄和料类型,使用Python库 (Pandas,NumPy,Scikit-learn) 处理.
- 删除了多线性AGE变量以提高模型的稳定性.
主要成果:
- 梯度提升机 (GBM) 显示出卓越的性能,平均准确率为64.63%.
- 在评估的模型中,GBM还产生了最高的加权精度 (0.6578),回忆 (0.6463),F1得分 (0.6311),和ROC AUC (0.6625).
- 虽然预测性表现适度,但GBM显示出显著的潜力.
结论:
- 梯度增强机 (GBM) 模型是测试过的算法中最有效的,用于预测水牛的牛奶产量.
- 这些发现强调了先进的机器学习技术在提高乳制品动物生产预测的精度方面的实用性.
- 进一步的研究和模型改进可以提高水牛养殖的准确性和实际应用.
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