This report was submitted by Team Aurors as a participant in the U.S. Justice Department’s National Institute of Justice’s (NIJ’s) Recidivism Forecasting Challenge, which aimed to improve the ability to forecast recidivism.
The work reported by the Aurors Team addresses forecasting models that include regression analysis methods (binary logit and LASSO regressions) and machine learning techniques (random forest), combined through a model averaging procedure. The output consists of the percent likelihood of individuals recidivating within one, two, or three years from release. Although this report explains the database construction process and the modeling approach, the results focus on the section of female parolees recidivating within 3 years, since that is the category for which the Aurors team came in second place.
Downloads
Similar Publications
- Second Chance Act (SCA) Grant Program Evaluation: Interim Report on Program Implementation in Three SCA Sites
- A Black Box Study of the Accuracy and Reproducibility of Tire Evidence Examiners’ Conclusions
- Assessment of the Effectiveness of Emergency Lighting, Retroreflective Markings, and Paint Color on Policing and Law Enforcement Safety