Statistical Procedures for Forecasting Criminal Behavior: A Comparative Assessment

Statistical Procedures for Forecasting Criminal Behavior: A Comparative Assessment

Source: 

University of Pennsylvania

Author(s): 

Richard Berk Justin

There is a substantial and powerful literature in statistics and computer science clearly demonstrating that modern machine learning procedures can forecast more accurately than conventional parametric statistical models such as logistic regression.  Yet, several recent studies have claimed that for criminal justice applications, forecasting accuracy is about the same. In this paper, we address the apparent contradiction.  Forecasting accuracy will depend on the complexity of the decision boundary.  When that boundary is simple, most forecasting tools will have similar accuracy. When that boundary is complex, procedures such as machine learning that proceed adaptively from the data will improve forecasting accuracy, sometimes dramatically. Machine learning has other benefits as well, and effective software is readily available.

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