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In the U.S., prison administrators often rely on risk assessment instruments to place and supervise inmates, as well as manage, plan and allocate resources. Hence, any improvement in the accuracy performance of risk assessment instruments is likely to result in significant benefits for offender classification and rehabilitation, management systems, and public safety. To date, researchers have explored the relative predictive performance between regression and non-regression methods and the overall evidence is inconclusive. In this study, we seek to advance the debate regarding the efficacy of traditional regression methods versus the utility of machine learning techniques in forecasting inmate misconduct by exploring the prospect that each technique may be more suitable for a specific performance measure. We examined the relative performance of a traditional regression method, logistic regression, and two machine learning techniques, random forest and neural networks, in classifying...

This study assesses the relative utility of a traditional regression approachlogistic regression (LR)-and three classification techniques-classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), and multi-layer perceptron neural network (MLPNN)-in predicting inmate misconduct. The four models were tested using a sample of inmates held in state and federal prisons and predictors derived from the importation model on inmate adaptation. Multivalidation procedure and multiple evaluation indicators were used to evaluate and report the predictive accuracy. The overall accuracy of the four models varied between 0.60 and 0.66 with an overall AUC range of 0.60-0.70. The LR and MLPNN methods performed significantly better than the CART and CHAID techniques at identifying misbehaving inmates and the CHAID method outperformed the CART approach in classifying defied inmates. The MLPNN method performed significantly better than the LR technique in predicting inmate misconduct among the training samples.

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