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Abstract

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