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Mass violence, almost no matter how defined, is (thankfully) rare. Rareevents are very difficult to study in a systematic manner. Standard statisticalprocedures can fail badly and usefully accurate forecasts of rare events oftenare little more than an aspiration. We offer an unconventional approach for thestatistical analysis of rare events illustrated by an extensive case study. Wereport research whose goal is to learn about the attributes of very high riskIPV perpetrators and the circumstances associated with their IPV incidentsreported to the police. Very high risk is defined as having a high probabilityof committing a repeat IPV assault in which the victim is injured. Suchindividuals represent a very small fraction of all IPV perpetrators; these actsof violence are relatively rare. To learn about them nevertheless, we apply ina novel fashion three algorithms sequentially to data collected from a largemetropolitan police department: stochastic gradient boosting, a geneticalgorithm inspired by natural selection, and agglomerative clustering. We tryto characterize not just perpetrators who on balance are predicted tore-offend, but who are very likely to re-offend in a manner that leads tovictim injuries. With this strategy, we learn a lot. We also provide a new wayto estimate the importance of risk predictors. There are lessons for the studyof other rare forms of violence especially when instructive forecasts aresought. In the absence of sufficiently accurate forecasts, scarce preventionresources cannot be allocated where they are most needed.

An Algorithmic Approach to Forecasting Rare Violent Events: An Illustration Based in IPV Perpetration