Classifying Adult Probationers by Forecasting Future Offending
Random forest modeling techniques represent an improvement over the methodologies of traditional risk prediction instruments. Random forests allow for the inclusion of a large number of predictors, the use of a variety of different data sources, the expansion of assessments beyond binary outcomes, and taking the costs of different types of forecasting errors into account when constructing a new model. This study explores the application of random forest statistical learning techniques to a criminal risk forecasting system, which is now used to classify adult probationers by the level of risk they pose to the community. To download this report, click here.