A medical diagnostic model was trained on an imbalanced dataset where healthy patients far outnumber diseased patients. False positives (incorrectly labeling healthy patients as diseased) increase company costs. Which metric best reflects model performance given this concern?

Precision is the most suitable metric because it directly addresses the concern of increasing company costs due to false positives. Precision measures the proportion of true positive predictions among all positive predictions made by the model (True Positives / (True Positives + False Positives)). A high precision score indicates that when the model predicts a patient is diseased, it is very likely correct, thus minimizing costly false positives. Recall (True Positives / (True Positives + False Negatives)) focuses on minimizing false negatives, which is not the primary concern here. F1 score is the harmonic mean of precision and recall, providing a balance, but precision is more targeted for this specific problem. Accuracy ((True Positives + True Negatives) / Total) can be misleading in imbalanced datasets, as a model could achieve high accuracy by simply predicting the majority class (healthy patients) most of the time, while still having many false positives for the minority class.