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                            Library & Multimedia
 NIJ Recidivism Challenge Report: Team Smith
NCJ Number
              305051

Author(s)
          Andy J. Smith
Date Published

2021
Length
              12 pages
Annotation
              This is the NIJ Recidivism Challenge Report submitted by Team Smith (Andy J. Smith) for the competition.
Abstract
              For the competition, contestants were provided with a training data set and three test sets with the objective of using the training data to develop and train a machine learning (ML) model that can predict the recidivism of the individuals in the data set. The training data provided were intended to represent the data a parole officer would have at the time the individual was released on parole. The data covered a wide range of inputs from the education and mental health fields to previous arrest and conviction information. Using the training data, Smith developed and tested a variety of traditional ML models to predict the recidivism of each person. To support this, Smith brought a variety of geographic data to inform on the environment each person was returning to on parole, although they provided little significance to the final model. The final model selected was an ensemble method of four traditional ML models. In addition to the data provided by the NIJ for the challenge, Smith added additional data taken from the PUMA of the individual as part of the normalization process. Smith had initially assumed that bringing in additional data about the PUMA zone would inform on the individual’s recidivism. The data brought in included the average income, average lot size, etc., but none of these variables were statistically significant.  Smith concludes that given the variable performance, it is clear that maintaining employment throughout parole is key to preventing recidivism.
      
  
          Additional Details
Grant Number(s)
        NIJ Recidivism Forecasting Challenge Winning Paper


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
    
  
  
Date Published: January 1, 2021
    
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