🟪 1-Minute Summary
Precision measures “of all predicted positives, how many were actually positive?” Formula: TP / (TP + FP). High precision means low false alarm rate. Use when false positives are costly (e.g., spam filter marking important emails as spam, recommending irrelevant products). Trade-off with recall: being more selective (higher precision) means catching fewer positives (lower recall).
🟦 Core Notes (Must-Know)
Formula
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Interpretation
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When to Optimize for Precision
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Precision-Recall Tradeoff
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“What does precision measure?”
- [Answer: Of predicted positives, how many are actually positive]
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“When would you prioritize precision over recall?”
- [Answer: When false positives are costly]
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“Your precision is 90% but recall is 20%. What’s happening?”
- [Answer: Model is very selective, catches few positives but rarely wrong]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Confusing precision with recall
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Mistake 2: Optimizing only precision
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🟩 Mini Example (Quick Application)
Scenario
[Spam filter evaluation]
Solution
from sklearn.metrics import precision_score
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