Towards more accurate classification of risk of arrest among offenders on community supervision: An application of machine learning versus logistic regression.

BACKGROUND: Although there is general consensus about the behavioural, clinical and sociodemographic variables that are risk factors for reoffending, optimal statistical modelling of these variables is less clear. Machine learning techniques offer an approach that may provide greater accuracy than traditional methods. AIM: To compare the performance of advanced machine learning techniques (classification trees and random forests) to logistic regression in classifying correlates of rearrest among adult probationers and parolees in the United States. METHOD: Data were from the subgroup of people on probation or parole who had taken part in the National Survey on Drug Use and Health for the years 2015-2019. We compared the performance of logistic regression, classification trees and random forests, using receiver operating characteristic curves, to examine the correlates of arrest within the past 12 months. RESULTS: We found that machine learning techniques, specifically random forests, possessed significantly greater accuracy than logistic regression in classifying correlates of arrest. CONCLUSIONS: Our findings suggest the potential for enhanced risk classification. The next step would be to develop applications for criminal justice and clinical practice to inform better support and management strategies for former offenders in the community.

Correlates of cannabis use disorder in the United States: A comparison of logistic regression, classification trees, and random forests.

Although several recent studies have examined psychosocial and demographic correlates of cannabis use disorder (CUD) in adults, few, if any, recent studies have evaluated the performance of machine learning methods relative to standard logistic regression for identifying correlates of CUD. The present study used pooled data from the 2015-2018 National Survey on Drug Use and Health to evaluate psychosocial and demographic correlates of CUD in adults. In addition, we compared the performance of logistic regression, classification trees, and random forest methods in classifying CUD. When comparing the performance of each method on the test data set, classification trees (AUC = 0.84, 95%CI: 0.82, 0.85) and random forest (AUC = 0.83, 95%CI: 0.82, 8.05) performed similarly and superior to logistic regression (AUC = 0.77, 95%CI: 0.74, 0.79). Results of the random forests reveal that marital status, risk propensity, age, and cocaine dependence variables contributed most to node purity, whereas model accuracy would decrease significantly if county type, income, race, and education variables were excluded from the model. One possible approach to improving the efficiency, interpretability, and clinical insights of CUD correlates is the employment of machine learning techniques.

Binge drinking in early adulthood: A machine learning approach.

Binge drinking among young adults (18-25) has been recognized as a public health concern. Considerable variation among drinking behaviors have been found among this group. Several statistical methods are available to identify theoretically and empirically meaningful correlates of binge drinking. The present study evaluated three methods for identifying correlates of binge drinking, comparing logistic regression to two machine learning methods-classification tress and random forests. While each model identified similar correlates of binge drinking-such as propensity for engaging in risky behaviors, marijuana dependence, cocaine dependence, identifying as non-Hispanic white, and higher education-the AUC analysis showed that the random forest analysis more accurately classified positive cases of binge drinking. Random forests modelling of psychosocial data is a feasible approach for identifying correlates of binge drinking behaviors among young adults. Clinical implications are discussed related to screening for binge drinking in behavioral health organizations.