Criminal Justice Forecasts of Risk A Machine Learning Approach
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 d
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Series Editors Stan Zdonik Peng Ning Shashi Shekhar Jonathan Katz Xindong Wu Lakhmi C. Jain David Padua Xuemin Shen Borko Furht VS Subrahmanian
For further volumes: http://www.springer.com/series/10028
Richard Berk
Criminal Justice Forecasts of Risk A Machine Learning Approach
Richard Berk University of Pennsylvania School of Arts & Sciences Philadelphia, PA [email protected]
ISSN 2191-5768 e-ISSN 2191-5776 ISBN 978-1-4614-3084-1 e-ISBN 978-1-4614-3085-8 DOI 10.1007/978-1-4614-3085-8 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012933586 © The Author 2012 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
This book is an effort to put in one place and in accessible form the most recent work on forecasting re-offending by individuals already in criminal justice custody. Much of that work is my own and comes from over two decades of close collaborations with a number of criminal justice agencies. After many requests to provide in one place an account of the procedures I have used, I agreed to write this book. What I hope distinguishes the material from what has come before is the use of machine learning statistical procedures coupled with very large datasets, an explicit introduction of the relative costs of forec
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