When prioritize precision vs recall

Q: When prioritize precision vs recall

This question evaluates a data scientist's competency with binary classification metrics (precision and recall), cost-sensitive decision-making, and operating-threshold selection for productized models in the Machine Learning domain.

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Question

Context

You are working on a product team and building (or evaluating) a binary classifier that triggers an action (e.g., show a warning, block content, route to manual review, send a notification, or prioritize leads).

Question

Explain what precision and recall mean in this business context.
For what types of business scenarios would you prioritize precision over recall ? Give 2–3 concrete examples.
For what types of business scenarios would you prioritize recall over precision ? Give 2–3 concrete examples.
Describe how you would choose an operating threshold (or decision rule) in practice, including:
- the cost/benefit tradeoff and asymmetric error costs
- capacity constraints (e.g., manual review bandwidth)
- base-rate / class imbalance considerations
- what metrics/curves you would look at (e.g., PR curve)
- any guardrail metrics you would monitor after launch

When prioritize precision vs recall

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When prioritize precision vs recall

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