| 书目名称 | Criminal Justice Forecasts of Risk | | 副标题 | A Machine Learning A | | 编辑 | Richard Berk | | 视频video | http://file.papertrans.cn/240/239771/239771.mp4 | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 描述 | Machine learning and nonparametric function estimation procedures can be effectively used in forecasting. One important and current application is used to make forecasts of “future dangerousness" to inform criminal justice decision. Examples include the decision to release an individual on parole, determination of the parole conditions, bail recommendations, and sentencing. Since the 1920s, "risk assessments" of various kinds have been used in parole hearings, but the current availability of large administrative data bases, inexpensive computing power, and developments in statistics and computer science have increased their accuracy and applicability. In this book, these developments are considered with particular emphasis on the statistical and computer science tools, under the rubric of supervised learning, that can dramatically improve these kinds of forecasts in criminal justice settings. The intended audience is researchers in the social sciences and data analysts in criminal justice agencies. | | 出版日期 | Book 2012 | | 关键词 | Forecasting; Future Dangerousness; Machine Learning; Parole; Probation; Public Safety; Random Forecasts; Ri | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4614-3085-8 | | isbn_softcover | 978-1-4614-3084-1 | | isbn_ebook | 978-1-4614-3085-8Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Author 2012 |
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