Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness

A 2016 report claims that annually upwards of 235 000 Canadians endure periods of homelessness, with approximately 35 000 individuals lacking a place to stay each night [1]. Between 2005 and 2014, there was a downward trend in the total number of Canadians using shelters; however, the occupancy rates of shelters has been increasing [1]. One factor accounting for this ongoing decrease in the number of homeless individuals paired with an increase in shelter occupancy is an increase in chronic homelessness. London's Homeless Prevention division identifies an individual as chronically homelessness if they have spent 6 or more months ( 180 days) of the last year in a shelter, which was based on the definition of chronic homelessness outlined by the Canadian government's homelessness strategy directives [2]. In addition to this trend, the demographics of homelessness are changing in Canada. In preceding decades, older, single males are over-represented in the homeless population; in contrast, the homeless population of today is increasingly diverse, with families, women, and youth comprising a greater fraction [1].