When prioritize precision vs recall
Company: TikTok
Role: Data Scientist
Category: Machine Learning
Difficulty: easy
Interview Round: Technical Screen
## 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
Overview: 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.
Read the full TikTok Data Scientist interview experience this question came from