Evaluate Fake-Account Classifier with Precision and Recall Metrics

Q: Evaluate Fake-Account Classifier with Precision and Recall Metrics

This question evaluates a candidate's understanding of classifier evaluation metrics (precision, recall, F1, ROC-AUC), cost-sensitive decision making, threshold tuning, and handling class imbalance for production ML systems.

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Question

Evaluating a Fake-Account Classifier in Production

Scenario

You have trained a model that flags fake accounts. Leadership wants clear, defensible evidence that it works well in production and understands the trade-offs of using it to take actions (e.g., auto-ban vs. human review).

Task

Recommend the evaluation metrics you would use to judge the fake-account classifier and explain why. Discuss the trade-offs among:

Precision, recall, F1
ROC-AUC
Business costs of false positives (blocking a real user) vs. false negatives (missing a fake)

Include in your answer:

How class imbalance affects metric choice
How you would pick and tune thresholds
How you would incorporate cost-based metric selection

Evaluate Fake-Account Classifier with Precision and Recall Metrics

Evaluating a Fake-Account Classifier in Production

Scenario

Task

Solution

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