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使用机器学习预测青少年犯罪和成年犯罪行为.

Ulrich Schroeders1, Antonia Mariss1, Julia Sauter1

  • 1Department of Psychology, University of Kassel, Germany.

International journal of behavioral development
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此摘要是机器生成的。

机器学习模型,特别是弹性净回归,可以预测青少年犯罪,但成人犯罪行为预测的准确性下降. 青少年和成人犯罪的关键预测因素有很大的不同.

关键词:
犯罪行为犯罪行为.青少年犯罪率是一个问题.机器学习是机器学习.预测 预测 预测 预测有关风险因素的风险因素.

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科学领域:

  • 犯罪学 犯罪学
  • 心理学 心理学 心理学
  • 社会学 社会学 社会学

背景情况:

  • 偏差行为违反社会规范,影响个人和社会.
  • 现有的理论很难准确地预测偏差行为.
  • 预测青少年犯罪和成人犯罪行为对于干预至关重要.

研究的目的:

  • 使用机器学习检查青少年犯罪和成人犯罪行为的可预测性.
  • 将传统回归与机器学习算法的预测精度进行比较.
  • 确定区分青少年和成人犯罪行为的关键预测因素.

主要方法:

  • 利用了从全国青少年到成人健康研究 (添加健康) 的数据.
  • 采用了弹性净回归和梯度增强机器.
  • 在毒品,财产和暴力犯罪之间进行区分.

主要成果:

  • 弹性净回归与项目级数据显示出最好的预测准确性.
  • 青少年犯罪率的预测相对准确 (R2 .39.57).
  • 成人犯罪行为预测准确度显著下降 (R2 .13.16).

结论:

  • 青少年犯罪和成人犯罪行为的预测因素是不同的.
  • 成人犯罪的早期风险因素包括以前的青少年犯罪,性和学校问题.
  • 调查结果为有关犯罪行为发展和预防策略的理论提供了信息.