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
|September 22, 2025
概括
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.
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.
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