You have built a model that flags fake accounts; leadership wants evidence it works well in production.

Which evaluation metrics would you choose to judge the fake-account classifier and why? Explain the trade-offs among precision, recall, F1, ROC-AUC, and business costs of false positives versus false negatives.

Discuss class imbalance, threshold tuning, and cost-based metric selection.

Meta Data Scientist interview question: Evaluate Fake-Account Classifier with Precision and Recall Metrics. {"blocks": [{"key": "bf3e4d6e", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...