About this book
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.
- Book Title Machine Learning Risk Assessments in Criminal Justice Settings
- DOI https://doi.org/10.1007/978-3-030-02272-3
- Copyright Information Springer Nature Switzerland AG 2019
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-030-02271-6
- eBook ISBN 978-3-030-02272-3
- Edition Number 1
- Number of Pages IX, 178
- Number of Illustrations 5 b/w illustrations, 27 illustrations in colour
Probability and Statistics in Computer Science
Data Mining and Knowledge Discovery
- Buy this book on publisher's site