Ebook: Criminal Justice Forecasts of Risk: A Machine Learning Approach
Author: Richard Berk (auth.)
- Tags: Artificial Intelligence (incl. Robotics), Statistical Theory and Methods, Probability and Statistics in Computer Science
- Series: SpringerBriefs in Computer Science
- Year: 2012
- Publisher: Springer-Verlag New York
- Edition: 1
- Language: English
- pdf
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.
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Content:
Front Matter....Pages i-ix
Getting Started....Pages 1-6
Some Important Background Material....Pages 7-25
A Conceptual Introduction to Classification and Forecasting....Pages 27-41
A More Formal Treatment of Classification and Forecasting....Pages 43-57
Tree-Based Forecasting Methods....Pages 59-79
Examples....Pages 81-100
Implementation....Pages 101-105
Some Concluding Observations About Actuarial Justice and More....Pages 107-112
Back Matter....Pages 113-115
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Content:
Front Matter....Pages i-ix
Getting Started....Pages 1-6
Some Important Background Material....Pages 7-25
A Conceptual Introduction to Classification and Forecasting....Pages 27-41
A More Formal Treatment of Classification and Forecasting....Pages 43-57
Tree-Based Forecasting Methods....Pages 59-79
Examples....Pages 81-100
Implementation....Pages 101-105
Some Concluding Observations About Actuarial Justice and More....Pages 107-112
Back Matter....Pages 113-115
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