RT Article
T1 Out With the Old and in With the New? An Empirical Comparison of Supervised Learning Algorithms to Predict Recidivism
JF Criminal justice policy review
VO 28
IS 6
SP 570
OP 600
A1 Duwe, Grant 1971-
A2 Kim, KiDeuk
LA English
YR 2017
UL https://krimdok.uni-tuebingen.de/Record/1725655268
AB Recent research has produced mixed results as to whether newer machine learning algorithms outperform older, more traditional methods such as logistic regression in predicting recidivism. In this study, we compared the performance of 12 supervised learning algorithms to predict recidivism among offenders released from Minnesota prisons. Using multiple predictive validity metrics, we assessed the performance of these algorithms across varying sample sizes, recidivism base rates, and number of predictors in the data set. The newer machine learning algorithms generally yielded better predictive validity results. LogitBoost had the best overall performance, followed by Random forests, MultiBoosting, bagged trees, and logistic model trees. Still, the gap between the best and worst algorithms was relatively modest, and none of the methods performed the best in each of the 10 scenarios we examined. The results suggest that multiple methods, including machine learning algorithms, should be considered in the development of recidivism risk assessment instruments.
K1 Machine learning
K1 Predictive discrimination
K1 Calibration
K1 Recidivism
K1 Risk assessment
DO 10.1177/0887403415604899