An ML team must choose a metric to compare two binary classifiers for a dataset where fraudulent transactions represent only 0.5% of all records. Which metric will BEST reflect how well the models handle the rare positive class?
Silhouette coefficient
Mean squared error (MSE)
Overall accuracy
F1 score
The F1 score combines precision and recall, so it captures how many fraudulent transactions are found (recall) and how many flagged transactions are actually fraud (precision). This balance is critical when the positive class is rare. Overall accuracy can appear high by simply predicting the majority class and ignoring fraud. Mean squared error is intended for regression problems, and the silhouette coefficient measures cluster quality, not classification performance.
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