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
- Improving and Evaluating Computed Tomography and Magnetic Resonance Imaging in the Investigation of Fatalities Involving Suspected Head Trauma
- Environmental Predictors Impact Microbial-based Postmortem Interval (PMI) Estimation Models within Human Decomposition Soils
- Incorporating Those with Lived Experience to Improve Community Supervision Outcomes