Simplifying Research Oriented to Practitioners and Policymakers: A Case Study of Predictive Modelling with Naive Bayes

The study examines the applicability of Naïve Bayes in predictive classification modelling using a case study of cybercrime victimization data. The goal of which was a targeted presentation of the benefits of Bayesian analysis in crime research geared to policymakers. The method is assessed using a Model-Comparison Approach and model performance metrics. The study shows that Naïve Bayes can be useful in predictive classification where the target population is small or difficult to acquire such as offender profiling and analysis of high crime areas. This is important as it provides a plausible option to traditional Frequentist methods, that overcome statistical limitations and provides results in a form easily conveyable to policymakers. Further, the conditional probability data produced makes future prediction transparent and can foster confidence in predicted outcomes. In particular, Directed Acyclic Graph can be easily used to represent the Naïve Bayes output allowing visualization of the relationships between variables.