通过集成的多层集群和机器学习模型分析和预测全球幸福指数
1School of Economics and Management, Beijing Jiaotong University, Beijing, China.
PloS one
|April 30, 2025
概括
全球幸福预测得到了新的机器学习框架的增强. 社会支持和GDP是国家幸福度的关键驱动因素.
科学领域:
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
- 心理学 心理学 心理学
背景情况:
- 全球幸福预测是复杂的,需要先进的分析方法.
- 了解幸福驱动因素对于政策制定和社会福祉至关重要.
- 现有的模型可能无法完全捕捉到国民幸福感的细微模式.
研究的目的:
- 开发和验证一个新的全球幸福预测框架.
- 确定影响各国幸福分数的关键决定因素.
- 为了有针对性的分析,将国家分为不同的幸福群体.
主要方法:
- 无监督 (K-Means集群) 和监督 (随机森林,XGBoost) 机器学习技术的整合.
- 层次分析将集群分配纳入集群模型中的特征.
- 利用世界幸福报告的数据进行实证分析.
主要成果:
- 一个新的框架显著提高了幸福预测约12% R2.
- 全球幸福分数被有效地分为三个不同的组:高,中,低.
- 社会支持和国内生产总值 (GDP) 被确定为最重要的幸福驱动因素.
结论:
- 提出的等级机器学习方法提高了全球幸福预测的准确性.
- 社会支持和GDP是国家福祉的关键因素.
- 调查结果为旨在改善公众幸福和社会进步的政策制定者提供了宝贵的见解.
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