NDLI: A Comparison of Logistic Regression, Classification and Regression Tree, and Neural Networks Models in Predicting Violent Re-Offending

Content Provider	Springer Nature Link
Author	Liu, Yuan Y. Yang, Min Ramsay, Malcolm Li, Xiao S. Coid, Jeremy W.
Copyright Year	2011
Abstract	Previous studies that have compared logistic regression (LR), classification and regression tree (CART), and neural networks (NNs) models for their predictive validity have shown inconsistent results in demonstrating superiority of any one model. The three models were tested in a prospective sample of 1225 UK male prisoners followed up for a mean of 3.31 years after release. Items in a widely-used risk assessment instrument (the Historical, Clinical, Risk Management-20, or HCR-20) were used as predictors and violent reconvictions as outcome. Multi-validation procedure was used to reduce sampling error in reporting the predictive accuracy. The low base rate was controlled by using different measures in the three models to minimize prediction error and achieve a more balanced classification. Overall accuracy of the three models varied between 0.59 and 0.67, with an overall AUC range of 0.65–0.72. Although the performance of NNs was slightly better than that of LR and CART models, it did not demonstrate a significant improvement.
Starting Page	547
Ending Page	573
Page Count	27
File Format	PDF
ISSN	07484518
Journal	Journal of Quantitative Criminology
Volume Number	27
Issue Number	4
e-ISSN	15737799
Language	English
Publisher	Springer US
Publisher Date	2011-04-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Violence reconviction Risk assessment Neural networks Classification and regression tree HCR-20 Statistics Methodology of the Social Sciences Sociology Criminology & Criminal Justice
Content Type	Text
Resource Type	Article
Subject	Law Pathology and Forensic Medicine

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