2024
DOI: 10.1038/s43856-024-00468-0
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Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies

Johannes Allgaier,
Rüdiger Pryss

Abstract: Background Machine learning (ML) models are evaluated in a test set to estimate model performance after deployment. The design of the test set is therefore of importance because if the data distribution after deployment differs too much, the model performance decreases. At the same time, the data often contains undetected groups. For example, multiple assessments from one user may constitute a group, which is usually the case in mHealth scenarios. Methods … Show more

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