Q1. [1] § Case study-based questions
In spam email detection, which of the following will be considered as "False Negative" ?
- (a) When a legitimate email is accurately identified as not spam.
- (b) When a spam email is mistakenly identified as legitimate.
- (c) When an email is accurately recognised as spam.
- (d) When an email is inaccurately labelled as important.
Previously asked in CBSE board exam
2024 104 Q3 (v)
Generated by claude-sonnet-4-6 · 2026-06-21 03:18 · grounding rag
Model Answer
(b) When a spam email is mistakenly identified as legitimate.
False Negative (FN) means the model wrongly predicts the positive class as negative — i.e., a spam email (positive) is incorrectly classified as not spam (legitimate).
Explanation
- False Negative = actual positive, predicted negative. In spam detection, "spam" is the positive class. So FN occurs when a spam email slips through and is labelled as legitimate.
- Option (a) describes True Negative; option (c) describes True Positive; option (d) is not a standard confusion matrix term.
- Remember the pattern: False = wrong prediction; Negative = predicted as the negative class (not spam).
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