机器学习回归模型用于内部羞耻感
Nataša Kovač1, Kruna Ratković1, Hojjatollah Farahani2
1Department of Mathematics, Faculty of Applied Sciences, University of Donja Gorica, Oktoih 1, Podgorica, 81000, Montenegro.
Acta psychologica
|October 8, 2025
概括
这项研究在伊朗样本中确定了内部羞耻 (IS) 的关键预测因素. 应急耐受性成为最强的预测因素,而XGBoost机器学习模型在预测IS方面表现出卓越的表现.
科学领域:
- 心理学 心理学 心理学
- 机器学习 机器学习
- 临床心理学 临床心理学
背景情况:
- 内部羞耻 (IS) 是一个重要的心理构造.
- 了解IS的预测因素对于心理健康干预至关重要.
- 童年创伤和情绪调节技能与IS发展有关.
研究的目的:
- 用各种心理因素来预测内部羞耻 (IS).
- 评估用于IS预测的多个机器学习模型的性能.
- 确定伊朗人口中IS最重要的预测因素.
主要方法:
- 一个伊朗人的样本被评估为童年创伤,社会情感能力,认知灵活性,痛苦耐受性和alexithymia.
- 最初使用了9种机器学习回归技术.
- 评估了模型性能,从而选择了五种模型进行详细分析,包括XGBoost.
主要成果:
- 痛苦耐受性被确定为内在羞耻 (IS) 的最重要的预测因素.
- 认知灵活性对内在羞耻感的影响最小.
- 与其他评估的机器学习方法相比,XGBoost回归模型表现出优异的预测性能.
结论:
- 耐受苦难是发展内在羞耻感的一个关键因素.
- 机器学习,特别是XGBoost,为预测内部羞耻提供了一种强大的方法.
- 这些发现对有针对性的治疗干预对体验内在羞耻的人有影响.
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