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Updated: Jul 6, 2025

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增强特征选择的基于树的分类器的性能和可解释性,用于COVID-19检测.

Jesús Rufino1, Juan Marcos Ramírez1, Jose Aguilar1,2,3

  • 1IMDEA Networks Institute, 28918, Madrid, Spain.

Heliyon
|January 3, 2024
PubMed
概括

本研究引入了一种使用特征选择的机器学习方法,从自我报告的数据中准确预测COVID-19病例,增强公共卫生监测. 该方法考虑了症状之外的各种因素,以改善检测.

关键词:
发现COVID-19的检测方法可解释性分析的分析.梯度提升分类器的分类器.随机的森林随机的森林消除递归特征的消除.沙普利的价值是什么意思

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