Predicting recidivism among youth offenders: Augmenting professional judgement with machine learning algorithms.

This article identifies how analysis of administrative data at the discrete level using statistical learning methods is more accurate in predicting recidivism than using conventional statistical methods. This provides an opportunity to direct intervention efforts at individuals who are more likely to reoffend.