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                            Library & Multimedia
 Sacrificing Accuracy for Transparency in Recidivism Risk Assessment: The Impact of Classification Method on Predictive Performance
NCJ Number
              250851
Journal

*Corrections* Volume: 1 Issue: 3 Dated: May 2016 Pages: 155-176
Author(s)
          G.  Duwe;
                        K.  Kim
Date Published

July 2016
Length
              22 pages
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 methodologya 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)
      
  
          Additional Details
Grant Number(s)
        2013-AW-BX-0053


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)


  
  
        
  Award
        Development and Validation of an Actuarial Risk Assessment Tool for Juvenile Sex Offenders
    
  
  
Date Published: July 1, 2016
    
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Related Datasets
        
                      https://doi.org/10.3886/ICPSR38335.v1
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