While accuracy is a commonly used metric, it can be misleading in the context of imbalanced datasets, which are common in epidemiology. For example, in a dataset where 95% of cases are negative and 5% are positive, a model that predicts all cases as negative will have an accuracy of 95%, but it fails to identify any positive cases. The F1 score, on the other hand, provides a more balanced view by considering both false positives and false negatives.
