Abstract

This paper investigates how to best compare algorithms for predicting chronic homelessness for the purpose of identifying good candidates for housing programs. Predictive methods can rapidly refer potentially chronic shelter users to housing but also sometimes incorrectly identify individuals who will not become chronic (false positives). We use shelter access histories to demonstrate that these false positives are often still good candidates for housing. Using this approach, we compare a simple threshold method for predicting chronic homelessness to the more complex logistic regression and neural network algorithms. While traditional binary classification performance metrics show that the machine learning algorithms perform better than the threshold technique, an examination of the shelter access histories of the cohorts identified by the three algorithms show that they select groups with very similar characteristics. This has important implications for resource constrained not-for-profit organizations since the threshold technique can be implemented using much simpler information technology infrastructure than the machine learning algorithms.

摘要

本文研究了如何最好地比较预测慢性无家可归者的算法，以便确定适合住房计划的候选人。预测方法可以迅速将潜在的慢性庇护所使用者转介到住房，但有时也会错误地识别不会成为慢性病的个体（误报）。我们使用庇护所访问历史来证明这些误报通常仍然是住房的良好候选者。使用这种方法，我们将预测慢性无家可归的简单阈值方法与更复杂的逻辑回归和神经网络算法进行了比较。虽然传统的二元分类性能指标表明机器学习算法的性能优于阈值技术，对由三种算法确定的群体的庇护所访问历史的检查表明，它们选择的群体具有非常相似的特征。这对资源受限的非营利组织具有重要意义，因为阈值技术可以使用比机器学习算法更简单的信息技术基础设施来实施。