文章基本信息

标题：Comparing conventional and machine-learning approaches to risk assessment in domestic abuse cases
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作者：Jeffrey Grogger ; Ria Ivandic ; Tom Kirchmaier 等
期刊名称：CEP Discussion Paper
出版年度：2020
卷号：2020
语种：English
出版社：Centre for Economic Performance
摘要：We compare predictions from a conventional protocol-based approach to risk assessment with those based on a machine-learning approach. We first show that the conventional predictions are less accurate than, and have similar rates of negative prediction error as, a simple Bayes classifier that makes use only of the base failure rate. A random forest based on the underlying risk assessment questionnaire does better under the assumption that negative prediction errors are more costly than positive prediction errors. A random forest based on two-year criminal histories does better still. Indeed, adding the protocol-based features to the criminal histories adds almost nothing to the predictive adequacy of the model. We suggest using the predictions based on criminal histories to prioritize incoming calls for service, and devising a more sensitive instrument to distinguish true from false positives that result from this initial screening.
关键词：domestic abuse;risk assessment;machine learning