skip navigation

Add your conference to our Justice Events calendar


Register for Latest Research

Stay Informed
Register with NCJRS to receive NCJRS's biweekly e-newsletter JUSTINFO and additional periodic emails from NCJRS and the NCJRS federal sponsors that highlight the latest research published or sponsored by the Office of Justice Programs.

NCJRS Abstract

The document referenced below is part of the NCJRS Library collection. To conduct further searches of the collection, visit the NCJRS Abstracts Database. See the Obtain Documents page for direction on how to access resources online, via mail, through interlibrary loans, or in a local library.

  NCJ Number: NCJ 238082   Add to Shopping cart   Find in a Library
  Title: Classifying Adult Probationers by Forecasting Future Offending
  Document URL: PDF 
  Author(s): Geoffrey C. Barnes Ph.D. ; Jordan M. Hyatt, J.D., M.S.
  Date Published: 03/2012
  Page Count: 64
  Annotation: This report presents results from a demonstration project focused on the development of a risk-prediction model for a local probation and parole department in a large urban city.
  Abstract: Study results demonstrate that the project’s risk-forecasting models were able to increase the Philadelphia Adult Probation and Parole Department’s (APPD’s) ability to predict recidivism, leading to the restructuring of agency supervision protocols. The project resulted in the construction of three different prediction models based on a statistical process known as “random forest.” One benefit of random forest modeling is that there is no theoretical limit on the number of predictors that can be included in the model. Throughout the duration of the project, hundreds of different predictor variables were drawn from electronically available administrative records, and they were tested for possible use in these models. The most recent version of APPD’s model produced an accurate forecast for 79,299 of the 119,935 probation case starts in the construction sample. These estimates suggest that this model can be correct nearly two-thirds (66.1 percent) of the time; however, a more reasonable method of measuring the model’s accuracy is to re-examine forecasted and actual outcomes separately, focusing on each of the three different outcome categories. The power and promise of the random forest forecasting methods is clear in Philadelphia; their introduction has allowed the agency to stratify offenders by the risk they pose, to tailor supervision requirements, to focus resources in accordance with policy directives, and to balance caseload sizes in the face of budgetary constraints. Twelve recommendations for building random forest prediction models in any jurisdiction are offered. Figures, tables, and references
  Main Term(s): Corrections policies
  Index Term(s): Offender classification ; Risk management ; Recidivism prediction ; Probation management ; NIJ final report ; Pennsylvania
  Sponsoring Agency: National Institute of Justice (NIJ)
US Department of Justice
Office of Justice Programs
United States of America
  Grant Number: 2008-IJ-CX-0024
  Sale Source: National Institute of Justice/NCJRS
Box 6000
Rockville, MD 20849
United States of America

NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
  Type: Report (Study/Research)
  Country: United States of America
  Language: English
  To cite this abstract, use the following link:

*A link to the full-text document is provided whenever possible. For documents not available online, a link to the publisher's website is provided. Tell us how you use the NCJRS Library and Abstracts Database - send us your feedback.