• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Predicting failure to appear: A comparison of statistical techniques.
【24h】

Predicting failure to appear: A comparison of statistical techniques.

机译：预测失败的出现：统计技术的比较。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The criminal justice system has a long history of attempting to predict outcomes for several criminal justice decision points. Failure to appear in court is one such area of research. The current study compares the accuracy of deductive and inductive statistical models in predicting failure to appear in court. To evaluate this question, logistic regression was compared to random forests, support vector machines, and naive Bayes models. The efficacy of a stacked ensemble model, a model developed from the predicted probabilities of the aforementioned individual models, was compared as well. Model accuracy assessment was determined using an identical holdout set of cases across all models. The results indicate that the random forest model outperforms logistic regression at both overall accuracy and a one percent false positive threshold. This adds to a growing body of literature that evaluates the efficacy of inductive models of prediction in criminal justice applications. Future research should continue to evaluate the efficacy of inductive and deductive statistical models in various criminal justice applications. Indeed, despite the performance of the random forest model presented here, many statistical models should be considered whenever any new prediction model is developed. Each model is developed in a unique way, with specific strengths and weaknesses, and only when several are considered is it possible to identify the best for a specific application. Since even modest increases in predictive accuracy can improve the efficiency and outcome of the criminal justice system, the search for the accurate prediction should continue.
机译：刑事司法系统在尝试预测几个刑事司法判决点的结果方面有着悠久的历史。未能出庭就是此类研究领域之一。当前的研究比较了演绎和归纳统计模型在预测出庭失败方面的准确性。为了评估该问题，将逻辑回归与随机森林，支持向量机和朴素贝叶斯模型进行了比较。还比较了堆叠集成模型的功效，该模型是根据上述单个模型的预测概率开发的模型。使用所有模型中相同的案例集来确定模型准确性评估。结果表明，随机森林模型在总体准确性和百分之一的假阳性阈值上均优于逻辑回归。这增加了越来越多的文献，这些文献评估了归纳预测模型在刑事司法应用中的功效。未来的研究应继续评估归纳和演绎统计模型在各种刑事司法应用中的效力。确实，尽管此处介绍的随机森林模型具有出色的性能，但只要开发了任何新的预测模型，都应考虑许多统计模型。每个模型都以独特的方式开发，具有特定的优势和劣势，只有考虑多个模型，才有可能针对特定应用确定最佳方案。由于预测准确度即使适度增加也可以提高刑事司法系统的效率和结果，因此应继续寻求准确的预测。

著录项

  • 作者

    Clipper, Stephen James.;

  • 作者单位

    The University of Texas at Dallas.;

  • 授予单位 The University of Texas at Dallas.;
  • 学科 Criminology.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 102 p.
  • 总页数 102
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 康复医学;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号