Criminal justice forecasts of risk a machine learning approach / Richard Berk.

Author/creator Berk, Richard
Format Electronic
Publication InfoLondon : Springer-Verlag, 2012.
Descriptionix, 115 pages : illustrations (some color) ; 23 cm.
Supplemental ContentFull text available from Springer Nature - Springer Computer Science eBooks 2012 English International
Supplemental ContentFull text available from Springer Books
Subjects

SeriesSpringerBriefs in computer science
SpringerBriefs in computer science. ^A1144214
Contents Getting started -- Some important background material -- A conceptual introduction to classification and forecasting -- A more formal treatment of classification and forecasting -- Tree-based forecasting methods -- Examples -- Implementation -- Some concluding observations about actuarial justice and more.
Abstract "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."--Publisher's website.
Bibliography noteIncludes bibliographical references (pages 113-115).
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2012933586
ISBN9781461430841
ISBN1461430844