使用机器学习预测肯尼亚的食品价格:使用XGBoost和梯度增强的混合模型方法
Benard O Ogol1, Evans Omondi2,3, John Olukuru1
1@ilabAfrica, Strathmore University, Nairobi, Kenya.
Frontiers in artificial intelligence
|November 10, 2025
概括
机器学习使用混合XGBoost和梯度提升模型准确预测肯尼亚的食品价格. 该系统帮助政策制定者规划和预防粮食不安全.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 粮食价格波动是肯尼亚的一个主要经济挑战.
- 预测粮食价格对于经济稳定和粮食安全至关重要.
研究的目的:
- 开发和评估一种混合机器学习模型,用于预测肯尼亚的食品价格.
- 确定影响食品价格波动的关键因素.
主要方法:
- 使用了混合方法,结合了XGBoost和梯度增强模型.
- 训练并比较多个机器学习算法,包括LightGBM,决策树,随机森林和线性回归.
- 采用超参数随机搜索和5倍交叉验证用于模型调整和验证.
主要成果:
- 混合型号实现了高精度,R平方为0.9940,优于独立型号的性能.
- 确定的主要预测因素包括单位数量,价格类型,商品和货币.
- 为了特征重要性分析,使用了夏普利添加式扩张 (SHAP).
结论:
- 开发的模型为预测肯尼亚的食品价格提供了可靠的工具.
- 这种数据驱动的系统可以支持决策者和利益相关者减轻粮食不安全.
- 该模型及其功能被保存到Web应用程序中部署.
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