在假设下使用线性和灵活的差别分析预测弗里西亚奶牛的牛奶产量,违反了假设
Sherif A Moawed1, Esraa Mahrous2, Ahmed Elaswad3
1Department of Animal Wealth Development, Biostatistics Division, Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522, Egypt.
灵活区分分析 (FDA) 比线性区分分析 (LDA) 更好地预测弗里西亚牛的牛奶产量. 美国食品药品监督管理局 (FDA) 的准确率达到82%,在分类低,中,高奶产类别方面超过了LDA的71%的表现.
科学领域:
- 动物科学动物科学
- 数据科学数据科学数据科学
- 农业技术 农业技术
背景情况:
- 新技术有助于畜牧管理中的决策.
- 弗里西亚牛的牛奶生产分类对于最佳的农场管理至关重要.
- 将牛奶产量预测为低,中,高等级有助于战略规划.
研究的目的:
- 评估线性差异分析 (LDA) 和灵活差异分析 (FDA) 用于分类弗里西亚牛奶产量.
- 为了比较LDA和FDA模型的预测性能.
- 确定影响奶产类别的关键预测因素.
主要方法:
- 利用了来自弗里西亚奶牛的3793个哺乳记录 (2009-2020年).
- 分析的预测因素:第一次分娩的年龄,哺乳顺序,开放日,牛奶中的天,干期,分娩季节,305天的牛奶产量,分娩间隔和每次受孕总繁殖.
- 基于准确性,灵敏度,精度和F1分数进行了LDA和FDA的比较.
主要成果:
- 灵活区分分析 (FDA) 显示出优于线性区分分析 (LDA) 的性能.
- 美国FDA的分类准确率达到82%,而LDA的分类准确率为71%.
- 美国食品和药物管理局显示F1得分比LDA (0.667-0.79) 高 (0.81-0.83).
结论:
- 灵活差别分析 (FDA) 在违反假设时比线性差别分析 (LDA) 更强大.
- 无论是LDA还是FDA,都是解释和预测牲畜数据集的有效工具.
- 美国食品和药物管理局 (FDA) 提供了更高的准确性来对牛奶生产进行分类.
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