MLS-C01 Modeling Practice Question
Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall
When false negatives are costly, we want to minimize them, so recall (true positive rate) is important. Precision is also important to avoid too many false positives, but F1 score balances both. Recall directly measures false negatives, and F1 combines precision and recall. AUC-ROC is a general measure, and accuracy can be misleading. Therefore, the two appropriate metrics are Recall (option C) and F1 score (option D).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading for imbalanced datasets.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC is a general measure of separability.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified.
- ✓
F1 score
Why this is correct
F1 score balances precision and recall.
- ✗
Precision
Why it's wrong here
Precision focuses on false positives, not false negatives.
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