The Development and Validation of a Classification System Predicting Severe and Frequent Prison Misconduct

Abstract

This study presents the results from the development and validation of a fully automated, gender-specific risk assessment system designed to predict severe and frequent prison misconduct on a recurring, semiannual basis. K-fold and split-population methods were applied to train and test the predictive models. Regularized logistic regression was the classifier used on the training and test sets that contained 35,506 males and 3,849 females who were released from Minnesota prisons between 2006 and 2011. Using multiple metrics, the results showed the models achieved a relatively high level of predictive performance. For example, the average area under the curve (AUC) was 0.832 for the female prisoner models and 0.836 for the male prisoner models. The findings provide support for the notion that better predictive performance can be obtained by developing assessments that are customized to the population on which they will be used.