Machine learning risk assessments in criminal justice settings

In this book, the author attempts to increase the accessibility of the most recent work on forecasts of re-offending by individuals already in criminal justice custody. The book’s target audience is graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. The author assumes the reader has a working knowledge of the generalized linear model, and notes that all empirical examples were constructed using the R programming language, which is an open access software. The goal of this work, aside from aggregating research, is to use machine learning statistical procedures trained on very large datasets, and emphasize the maximization of forecasting accuracy.