Mass violence, almost no matter how defined, is (thankfully) rare. Rare events are 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 aimed at learning about the attributes of very‐high‐risk intimate partner violence (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 reported to the police are rare. To learn about them nevertheless, we sequentially apply in a novel fashion three algorithms 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 reoffend but also who are very likely to reoffend in a manner that leads to victim injuries. Important lessons for forecasts of mass violence are presented.

中文翻译：

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预测罕见暴力事件的算法方法

幸好，几乎无论如何定义，大规模暴力都是罕见的。难得的事件很难系统地研究。标准的统计程序可能会严重失败，并且有用的，对稀有事件的准确预测通常只是期望。我们通过广泛的案例研究提供了一种罕见事件统计分析的非常规方法。我们报告的研究旨在了解极高风险亲密伴侣暴力（IPV）犯罪者的属性以及向警察报告的与IPV事件有关的情况。“极高风险”被定义为极有可能再次造成受害者受伤的IPV袭击。这些人只占所有IPV犯罪者的很小一部分；这些向警察举报的暴力行为很少见。尽管如此，要了解它们，我们以新颖的方式将三种算法依次应用于从大型都会警察局收集的数据：随机梯度增强，受自然选择启发的遗传算法以及聚集聚类。我们不仅要描述被预料为总会犯罪的肇事者，而且还很可能以导致受害者伤害的方式犯罪。介绍了预测大规模暴力的重要教训。我们不仅要描述被预料为总会犯罪的肇事者，而且还很可能以导致受害者伤害的方式犯罪。介绍了预测大规模暴力的重要教训。我们不仅要描述被预料为总会犯罪的肇事者，而且还很可能以导致受害者伤害的方式犯罪。介绍了预测大规模暴力的重要教训。