🟪 1-Minute Summary
Recall measures “of all actual positives, how many did we find?” Formula: TP / (TP + FN). High recall means low miss rate. Use when false negatives are costly (e.g., disease detection - missing a sick patient is worse than false alarm). Trade-off with precision: being less selective (higher recall) means more false alarms (lower precision).
🟦 Core Notes (Must-Know)
Formula
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Interpretation
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When to Optimize for Recall
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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 recall measure?”
- [Answer: Of actual positives, how many did we find]
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“When would you prioritize recall over precision?”
- [Answer: When false negatives are costly (disease, fraud)]
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“Recall is 95% but precision is 10%. What’s happening?”
- [Answer: Model predicts positive liberally, catches most but many false alarms]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Confusing recall with precision
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Mistake 2: Ignoring the precision-recall tradeoff
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🟩 Mini Example (Quick Application)
Scenario
[Disease detection evaluation]
Solution
from sklearn.metrics import recall_score
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