Explain Key Terms in Model Evaluation for Fraud Detection

Q: Explain Key Terms in Model Evaluation for Fraud Detection

This question evaluates understanding of classification model-evaluation metrics (precision, recall/sensitivity, specificity, F1-score) and basic statistical inference (p-value), along with the competency to map metric trade-offs to business cost and user-experience implications in a fraud-detection scenario.

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Question

Model Evaluation Terminology and Business Trade-offs

Scenario

Phone screen focused on understanding core model-evaluation metrics and their business implications.

Tasks

Define the following classification metrics and provide their formulas:
- Precision
- Recall (Sensitivity)
- Specificity
- F1-score
Explain what a p-value represents in hypothesis testing.
In a fraud-detection scenario, argue whether the false positive rate (FPR) or false negative rate (FNR) is more critical, and justify in terms of business cost and user experience.

Note: Tie each metric to business cost and clearly explain trade-offs.

Explain Key Terms in Model Evaluation for Fraud Detection

Model Evaluation Terminology and Business Trade-offs

Scenario

Tasks

Solution

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