Evaluation of Random Forest in Crime Prediction: Comparing Three-Layered Random Forest and Logistic Regression

Abstract

This study evaluated random forest's accuracy in predicting violent or criminal behavior of juveniles compared to that of conventional logistic regression using different sets of risk factors. Drawing on the National Longitudinal Study of Adolescent Health (Add Health), we predicted three outcomes - arrests, convictions, and incarcerations - using three sets of predictors, starting with sociodemographic variables only (Model 1) and incrementally adding behavioral/situational (Model 2) and emotional/environmental risk factors (Model 3). Although both prediction methods yielded similar levels of "overall" predictive accuracy (measured by the area under the receiver operating characteristic curve), our balanced random forest model, with a cost ratio of 10 (false negatives) to 1 (false positives), substantially improved prediction of who will be arrested, convicted, and incarcerated, which is of paramount importance for many researchers and practitioners. In addition to its capability to enhance sensitivity (prediction of "true positives"), random forest is more effective in forecasting juvenile criminal behavior than is conventional logistic regression in that the former is less susceptible to the influences of added predictors than is the latter.