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When prioritize precision vs recall

TikTokData Scientist · Technical Screen
EasyData ScientistTechnical ScreenMachine Learning

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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.

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

  1. Explain what precision and recall mean in this business context.
  2. For what types of business scenarios would you prioritize precision over recall? Give 2–3 concrete examples.
  3. For what types of business scenarios would you prioritize recall over precision? Give 2–3 concrete examples.
  4. 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