A financial company is building an ML model to classify fraudulent transactions based on customer...

The verified answer is C. F1 score. This is a classification problem: the model classifies transactions as fraudulent or non-fraudulent. AWS machine learning documentation identifies accuracy, precision, recall, and F1 score as classification evaluation metrics. F1 score is especially appropriate when the evaluation must consider both correct positive predictions and the cost of incorrect positive predictions. AWS defines F1 as the harmonic mean of precision and recall, which means it balances how many positive predictions are correct with how many actual positive cases are found.

The key issue in the question is minimizing false positives. In fraud detection, a false positive means a legitimate transaction is incorrectly classified as fraudulent. AWS documentation explains that precision evaluates how well the model avoids false positives by measuring the percentage of positive predictions that were actually correct. Because F1 includes precision, it penalizes models that generate too many false positives while also considering recall.

Accuracy is not the best answer because it only measures the overall fraction of correct predictions. In fraud detection, the dataset is often imbalanced, so a model can appear accurate while still performing poorly on fraud-related predictions. R-squared and RMSE are regression metrics, not classification metrics. R-squared measures how well a regression model explains variance, and RMSE measures prediction error magnitude for continuous numeric outputs.

Because the company needs a classification metric that reflects correct predictions and penalizes false-positive behavior, F1 score is the strongest option among the choices.