Machine Learning Risk Assessments in Criminal Justice Settings
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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 polic...
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.
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.
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