一个好日子是什么样子的? :对美国时间使用调查的可解释机器学习方法
Dunigan Folk1, Mirka Henninger2, Elizabeth Dunn1
1Department of Psychology, University of British Columbia, Vancouver, Canada V6T 1Z4.
PNAS nexus
|March 16, 2026
概括
快乐的日子需要特定的活动模式. 根据对时间使用调查的机器学习分析,社交可以提高快乐的时间长达两小时,而工作超过六小时可以减少快乐.
科学领域:
- 行为科学 行为科学
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
背景情况:
- 了解导致"比一般更好"的一天的因素对于健康研究至关重要.
- 以前的研究已经探讨了幸福的相关性,但详细的时间使用分析不太常见.
研究的目的:
- 识别与报告"比典型"日相比"比典型"日相关的特定活动及其持续时间.
- 利用可解释的机器学习来分析大规模的时间使用数据.
主要方法:
- 利用可解释的机器学习,特别是随机森林模型,分析美国时间使用调查 (2013年和2021年) 的数据.
- 评估了在100多项活动中花费的时间与日常幸福度等级之间的关系.
- 比典型日期更好地区分模型准确度为62-63%的平衡准确度.
主要成果:
- 社会化显著增加了比典型的一天更好的可能性,但两个小时后的好处停滞不前.
- 长达六小时的工作没有对每日幸福度评级产生负面影响.
- 工作超过六个小时与报告比典型一天更好的一天的概率下降密切相关.
结论:
- 对活动的具体时间分配,特别是社交和工作,是快乐日子的关键区分因素.
- 这些发现提供了对促进主观幸福感的日常活动的经验洞察.
- 机器学习为剖析时间使用和幸福数据中的复杂关系提供了一个强大的工具.
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