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NCJ Number: 250851 Find in a Library
Title: Sacrificing Accuracy for Transparency in Recidivism Risk Assessment: The Impact of Classification Method on Predictive Performance
Journal: Corrections  Volume:1  Issue:3  Dated:May 2016  Pages:155-176
Author(s): G. Duwe; K. Kim
Date Published: July 2016
Page Count: 22
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
Grant Number: 2013-AW-BX-0053
Document: HTML
Type: Report (Grant Sponsored); Report (Study/Research); Research (Applied/Empirical)
Format: Article; Document (Online)
Language: English
Country: United States of America
Annotation: Using multiple performance metrics and measures of recidivism on 40,740 Minnesota offenders released from prison between 2006 and 2011, this study evaluated the performance of prediction models developed with both Burgess methodology and supervised learning algorithms (i.e., statistical and machine learning algorithms).
Abstract: Recent studies have compared the performance of machine learning algorithms versus logistic regression models in predicting recidivism. Existing research, however, has not compared their performance to Burgess methodology—a transparent, simplistic and summative classification technique used to develop some of the most widely used risk and needs assessment instruments currently used in corrections. The results of the current study show that, compared to the best supervised learning classifiers, use of Burgess methodology yielded inferior performance in terms of predictive discrimination, accuracy, and calibration. (Publisher abstract modified)
Main Term(s): Corrections policies
Index Term(s): Comparative analysis; Estimating methods; National Institute of Justice (NIJ); NIJ grant-related documents; Recidivism prediction; Research methods; Risk management
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