A contestant’s view on the WARN-D machine learning competition: Pros and worrisome cons
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On January 7 2026, the WARN-D machine learning competition went live on Codabench (link). Researchers from around the world were invited to build prediction models to forecast depression onset in young adults. The data came from the WARN-D trial and included baseline questionnaires, ambulatory assessments, smartwatch measurements, and follow-up questionnaires. I was immediately captivated by the competition and eager to put my machine learning skills to work, so I signed up right away. Now that the first phase of the competition has concluded, and before the results are made public, I want to reflect on my experience as a contestant. I also want to reflect on a broader question: Is the competition format really a good fit for building prediction models in mental health?
