In machine learning, in which of these scenarios should a model be biased towards recall over precision?

Question

BlackTom AI · Accepted Answer

Recall quantifies the fraction of actual positives the model correctly identifies: recall = TP / (TP + FN). Precision quantifies how many predicted positives are actually positive: precision = TP / (TP + FP). There is a trade-off: lowering a decision threshold typically increases TP (raising recall) but also increases FP (lowering precision).

Step-by-step reasoning:
1. Define metrics with formulas:
   - Precision = TP / (TP + FP)
   - Recall = TP / (TP + FN)
2. Show an illustrative numeric trade-off:
   - Suppose there are 100 true positive cases. Model A finds 90 TP and misses 10 FN: recall = 90/(90+10) = 0.90. If Model A also has 20 FP, precision = 90/(90+20) = 0.818.
   - If we lower the threshold to favor recall, Model B finds 95 TP and misses 5 FN: recall = 95/(95+5) = 0.95. But FP rises to 50, so precision = 95/(95+50) = 0.655.
   - This demonstrates raising recall often increases FP and decreases precision.
3. Why favor recall: when false negatives (FN) have severe real-world consequences, it is more important to catch as many true positives as possible even at the cost of more false alarms. Medical diagnosis is a canonical example: failing to identify a disease (an FN) can lead to harm or death, so models are biased toward recall to minimize missed cases.

Why the correct option is correct:
- The medical-diagnosis example directly highlights the high cost of FN and justifies prioritizing recall over precision.

Why other options are incorrect:
- "Precision and recall equally important": if both truly matter, you should aim to balance them (e.g., maximize F1 or use business constraints), not bias solely toward recall.
- "When false positives are more tolerable... marketing campaign": while tolerating FP can justify higher recall, this option is less decisive and context-dependent; marketing often still values precision (cost per contact), so it is not the canonical high-stakes scenario.
- "Small dataset/avoid overfitting, focus on precision": dataset size and overfitting concern model complexity or regularization, not directly a reason to favor recall.
- "Minimize false positives (financial fraud)": this calls for prioritizing precision, the opposite of favoring recall.

Conclusion: Prioritizing recall is appropriate when avoiding false negatives is crucial (e.g., medical diagnoses). This matches the provided correct answer.

In machine learning, in which of these scenarios should a model be biased towards recall over precision?单项选择题

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