Epidemiologists often work with data that may be skewed or imbalanced, such as rare disease occurrences or outbreak detection. In such scenarios, conventional metrics like accuracy may not provide a clear picture of a model's performance. The F1 score helps to address this issue by considering both the precision (the proportion of true positives among the predicted positives) and recall (the proportion of true positives among the actual positives).
