A financial firm deployed an ML model to predict customer churn and has one week of production data. They want to measure how accurately the model predicts churn against actual outcomes. Which metric should they use?

The F1 score is appropriate because it is a classification metric that balances precision and recall, which are crucial for evaluating models predicting binary outcomes like customer churn. It's particularly useful when the classes are imbalanced, meaning fewer customers churn than don't. RMSE is incorrect because it's a regression metric used for continuous predictions, not binary classification. ROI is a business metric, not a model performance metric. BLEU score is used for evaluating natural language generation tasks, such as machine translation, and is irrelevant for churn prediction.