Webinar of paper 2013, Which method predicts recidivism best? A comparison of statistical, machine learning and data mining predictive models

Research output: Contribution to journalArticleAcademicpeer-review


Using criminal population criminal conviction history information, prediction models are
developed that predict three types of criminal recidivism: general recidivism, violent recidivism and
sexual recidivism. The research question is whether prediction techniques from modern statistics,
data mining and machine learning provide an improvement in predictive performance over classical
statistical methods, namely logistic regression and linear discriminant analysis. These models are
compared on a large selection of performance measures. Results indicate that classical methods do
equally well as or better than their modern counterparts. The predictive performance of the different
techniques differs only slightly for general and violent recidivism, while differences are larger for
sexual recidivism. For the general and violent recidivism data we present the results of logistic
regression and for sexual recdivisim of linear discriminant analysis.
Original languageUndefined/Unknown
Pages (from-to)565-584
Number of pages20
JournalJournal of the Royal Statistical Society. Series A, statistics in society
Publication statusPublished - 2013


  • recidivism
  • prediction
  • predictive performance
  • logistic regression
  • linear discriminant analysis
  • machine learning
  • data mining

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