Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes.

ABSTRACT: In a recidivism prediction context, there is no consensus on which modeling strategy should be followed for obtaining an optimal prediction model. In previous papers, a range of statistical and machine learning techniques were benchmarked on recidivism data with a binary outcome. However, two important tree ensemble methods, namely gradient boosting and random forests were not extensively evaluated. In this paper, we further explore the modeling potential of these techniques in the binary outcome criminal prediction context. Additionally, we explore the predictive potential of classical statistical and machine learning methods for censored time-to-event data. A range of statistical manually specified statistical and (semi-)automatic machine learning models is fitted on Dutch recidivism dat

SUBMITTER: Tollenaar N 

PROVIDER: S-EPMC6407787 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature