Predicting Arrest Release Outcomes: A Comparative Analysis of Machine Learning Models

This comparative study evaluates machine learning models for predicting arrest release outcomes using 5,226 marijuana possession cases from the Toronto Police Service (1997-2002). The dataset exhibited significant class imbalance, with only 17.1% detention outcomes versus 82.9% releases. After preprocessing to handle missing values and convert categorical variables, we implemented two modeling approaches: a 500-tree Random Forest classifier with feature importance measurement and a binomial Logistic Regression model. Both algorithms demonstrated strong predictive capability for release cases, achieving comparable overall accuracy (83.2-83.4%) and excellent sensitivity (>98%), though they struggled with the critical minority class as evidenced by poor specificity (<7%). The models showed similar discriminative power, with Logistic Regression achieving a marginally higher AUC-ROC (0.733 vs 0.726). Feature importance analysis identified employment status and prior police background checks as the strongest predictors, while demographic factors, including race, also contributed significantly to predictions. These results highlight both the technical challenges of imbalanced classification in justice system data and the ethical considerations surrounding potential algorithmic bias, particularly given the high false positive rate for detention predictions that could exacerbate existing disparities. The study underscores the need for careful model evaluation and responsible implementation when applying predictive analytics to sensitive criminal justice decisions, balancing statistical performance with considerations of fairness and social impact.