  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.
