| Summary: | Survey datasets are often wider than they are long. This high ratio of variables to observations raises concerns about overfitting during prediction, making informed variable selection important. Recent applications in computer science have sought to incorporate human knowledge into machine learning methods to address these problems. We implement such a “human-in-the-loop” approach in the Fragile Families Challenge. We use surveys to elicit knowledge from experts and laypeople about the importance of different variables to different outcomes. This strategy gives us the option to subset the data before prediction or to incorporate human knowledge as scores in prediction models, or both together. We find that human intervention is not obviously helpful. Human-informed subsetting reduces predictive performance, and considered alone, approaches incorporating scores perform marginally worse than approaches which do not. However, incorporating human knowledge may still improve predictive performance, and future research should consider new ways of doing so.
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