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The famous and controversial Stanford Prison Experiment, conducted by social psychologist Philip Zimbardo and his colleagues at Stanford University, demonstrated the power of social roles, social norms, and scripts.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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相关实验视频

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在智能监狱中,通过可解释的集体机器学习方法预测囚犯的自杀行为.

Khayyam Akhtar1, Muhammad Usman Yaseen1, Muhammad Imran1

  • 1COMSATS University Islamabad, Islamabad, Pakistan.

PeerJ. Computer science
|July 10, 2024
PubMed
概括

这项研究引入了使用机器学习预测囚犯自杀风险的新方法. 我们的方法提高了模型的解释性,并在检测潜在的紧急信号时实现了高精度.

关键词:
合唱团组合在一起.机器学习 机器学习减少模型的模型.这就是 SHAP SHAP 的意思.智能监狱 智能监狱

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科学领域:

  • 人工智能的人工智能
  • 犯罪学 犯罪学
  • 公共卫生 公共卫生

背景情况:

  • 智能技术和预测建模为监禁者行为监测和自杀风险减轻提供了潜在的潜力.
  • 现有的自杀预测机器学习模型往往缺乏互操作性和详细的解释性.
  • 当前的解释方法侧重于特征的重要性,忽视了基于规则的解释.

研究的目的:

  • 开发一种可解释的机器学习框架,用于预测囚犯自杀风险.
  • 在自杀预测模型中生成人类可读规则的Anchor解释.
  • 通过将SHAP和Anchor解释与整体方法相结合来增强模型性能.

主要方法:

  • 使用了SHapley添加式扩展 (SHAP) 来减少高维数据集的初始特征.
  • 采用解释来创建简单,人类可读的模型解释规则.
  • 开发了一个组合模型,将XGBoost和随机森林结合起来,并通过SHAP和Anchor的解释来改进.

主要成果:

  • 在自杀风险预测方面,与最先进的模型相比,取得了显著的改进.
  • 获得了98.6%的准确性和98.9%的精度.
  • 最好的自杀想法模型显示F1得分为96.7%.

结论:

  • 这种新的方法提高了自杀风险预测模型在惩戒机构中的解释性和准确性.
  • 结合SHAP和Anchor解释,为理解复杂的预测模型提供了一个强大的方法.
  • 这项研究为监狱中更有效,技术驱动的自杀预防策略铺平了道路.