探索父亲与青少年的亲密关系:一个随机的森林方法
Garrett T Pace1, Joyce Y Lee2, Kaitlin P Ward3
1School of Social Work, University of Nevada, Las Vegas, Las Vegas, NV.
Family relations
|September 25, 2025
概括
机器学习,特别是随机森林,通过克服数据挑战,增强了父子关系的家庭科学研究. 父亲的居住状况是青少年亲密关系的关键预测因素.
科学领域:
- 家庭科学 家庭科学
- 计算社会科学 计算社会科学
- 在社会研究中的人工智能.
背景情况:
- 父亲研究面临招聘,保留和数据复杂性的挑战.
- 机器学习 (ML) 提供了分析大型复杂数据集和处理缺失数据的解决方案.
- ML可以减轻研究父子关系的方法障碍.
研究的目的:
- 为了证明随机森林的实用性,机器学习算法,在家庭科学中.
- 为了确定父亲与青少年亲密关系的关键预测因素.
- 为了增强回归模型使用ML-informed变量选择.
主要方法:
- 应用随机森林算法来预测父亲和青少年之间的亲密关系.
- 利用了来自家庭未来和儿童福利研究 (n=2,927) 的数据.
- 包括131个童年的第一个十年测量预测因素.
主要成果:
- 父亲与孩子的居住状态是亲密关系的最强有力的预测因素.
- 随机森林改进了增强回归模型的变量选择.
- 证明了ML在识别复杂家庭背景预测因素方面的能力.
结论:
- 随机森林是家庭科学家研究复杂关系的宝贵工具.
- 整合人工智能,就像随机森林一样,促进了家庭科学研究.
- 这种方法为了解父子动态提供了新的方向.
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