The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied machine learning (ML) algorithms (decision trees, random forests, and support vector machines) to a data set with nearly 100,000 LS/CMI administrations to provincial corrections clientele in Ontario, Canada, and approximately 3 years follow-up. The overall accuracies and areas under the receiver operating characteristic curve (AUCs) were comparable, although ML outperformed LS/CMI in terms of predictive accuracy for the middle scores where it is hardest to predict the recidivism outcome. Moreover, ML improved the AUCs for individual scores to near 0.60, from 0.50 for the LS/CMI, indicating that ML also improves the ability to rank individuals according to their probability of recidivating. Potential considerations, applications, and future directions are discussed.

服务水平/案例管理清单（LS / CMI）是评估涉及司法的个人所需要的犯罪风险的最常用工具之一。荟萃分析研究表明，各种累犯结果具有很强的预测准确性。在这项探索性研究中，我们将机器学习（ML）算法（决策树，随机森林和支持向量机）应用于具有近100,000个LS / CMI管理部门的数据集，以管理加拿大安大略省的省级校正客户，并在大约3年后-向上。总体准确性和接受者工作特征曲线（AUC）下的区域具有可比性，尽管就最难以预测累犯结果的中级评分而言，ML优于LS / CMI。此外，ML将个人分数的AUC改善了近0.60，从LS / CMI的0.50开始，表明ML还提高了根据个人再犯的可能性对个人进行排名的能力。讨论了潜在的注意事项，应用和未来的方向。