Predicting Recidivism in Homicide Offenders Using Classification Tree Analysis

Abstract

Given the severity of the crime and the lengthy sentences often accompanying convictions, homicide tends to be seen as the culminating event in a criminal career. In an attempt to better understand the types of individuals who commit homicide, many studies have examined the offense history of those convicted of murder and manslaughter. Only recently have researchers begun to realize that in some cases homicide is not an end point in the trajectory of one’s criminal career but rather a potential predictor in a continuing criminal career. Building on existing research, the present study uses a sample of 320 homicide offenders convicted, sentenced, imprisoned, and released in New Jersey from 1990 to 2000 to assess which factors predict future recidivism. We find that classification tree analysis in random forests outperform logistic regressions in classification and prediction of recidivism.