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相关实验视频

Updated: Jun 29, 2026

Cheek Injection Model for Simultaneous Measurement of Pain and Itch-related Behaviors
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Published on: September 27, 2019

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
PubMed
概括
此摘要是机器生成的。

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.

关键词:
这是一种内在的羞耻,一种内在的羞耻.机器学习是机器学习.这是一个回归回归的回归.

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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相关实验视频

Last Updated: Jun 29, 2026

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04:59

Cheek Injection Model for Simultaneous Measurement of Pain and Itch-related Behaviors

Published on: September 27, 2019

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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.