  Mass violence, almost no matter how defined, is (thankfully) rare. Rare
events are very difficult to study in a systematic manner. Standard statistical
procedures can fail badly and usefully accurate forecasts of rare events often
are little more than an aspiration. We offer an unconventional approach for the
statistical analysis of rare events illustrated by an extensive case study. We
report research whose goal is to learn about the attributes of very high risk
IPV perpetrators and the circumstances associated with their IPV incidents
reported to the police. Very high risk is defined as having a high probability
of committing a repeat IPV assault in which the victim is injured. Such
individuals represent a very small fraction of all IPV perpetrators; these acts
of violence are relatively rare. To learn about them nevertheless, we apply in
a novel fashion three algorithms sequentially to data collected from a large
metropolitan police department: stochastic gradient boosting, a genetic
algorithm inspired by natural selection, and agglomerative clustering. We try
to characterize not just perpetrators who on balance are predicted to
re-offend, but who are very likely to re-offend in a manner that leads to
victim injuries. With this strategy, we learn a lot. We also provide a new way
to estimate the importance of risk predictors. There are lessons for the study
of other rare forms of violence especially when instructive forecasts are
sought. In the absence of sufficiently accurate forecasts, scarce prevention
resources cannot be allocated where they are most needed.
