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Titlebook: Machine Learning Risk Assessments in Criminal Justice Settings; Richard Berk Book 2019 Springer Nature Switzerland AG 2019 Risk Assessment

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发表于 2025-3-21 16:47:36 | 显示全部楼层 |阅读模式
书目名称Machine Learning Risk Assessments in Criminal Justice Settings
编辑Richard Berk
视频video
概述Discussions of neural networks, with extensions into deep learning, and of the tradeoffs between transparency, accuracy, and fairness.Throughout, difficult issues are clearly explained, supported by m
图书封面Titlebook: Machine Learning Risk Assessments in Criminal Justice Settings;  Richard Berk Book 2019 Springer Nature Switzerland AG 2019 Risk Assessment
描述.This book puts in one place and in accessible form Richard Berk’s most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk.. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than “predictive policing” for locations in time and space, which is a very different enterprise that uses different data different data analysis tools.. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations..
出版日期Book 2019
关键词Risk Assessment; machine learning; risk forecasting; criminal justice; future dangerousness; fair algorit
版次1
doihttps://doi.org/10.1007/978-3-030-02272-3
isbn_ebook978-3-030-02272-3
copyrightSpringer Nature Switzerland AG 2019
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发表于 2025-3-21 23:25:23 | 显示全部楼层
Some Important Background Material,hat constructing statistical forecasts of criminal behavior is primarily a technical enterprise. But the forecasted risks inform real decisions by criminal justice officials and other stakeholders. As a result, a wide range of matters can arise, only some of which are technical. Although for exposit
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A Conceptual Introduction to Classification and Forecasting,fication problem. The goal is to assign classes to cases. There may be two classes or more than two. Machine learning is broadly considered before turning in later chapters to random forests as a preferred forecasting tool. There is no use of models and at best a secondary interest in explanation. M
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A More Formal Treatment of Classification and Forecasting,of the model used to characterize how the data were generated. That model is very different from the one used in conventional regression. Then, classification is considered followed by the estimation issues it raises. The bias-variance tradeoff is front and center. So is post-selection statistical i
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Tree-Based Forecasting Methods,in criminal justice forecasting. The joint probability distribution model, data partitioning, and asymmetric costs should now be familiar. These features combine to make tree-based methods of recursive partitioning the fundamental building blocks for the machine learning procedures discussed. The ma
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Real Applications,applications that led to procedures adopted by criminal justice agencies. As such, they combine a number of technical matters with the practical realities of criminal justice decisions-making. For the reasons already addressed, random forests will be the machine learning method of choice with one ex
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