MLA-C01 ML Model Development Practice Question
A team wants to evaluate a binary classification model for credit risk. They need to understand the trade-off between false positives and false negatives. Which TWO metrics should they use? (Select TWO.)
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
Precision and recall are complementary; precision measures false positives, recall measures false negatives. AUC-ROC summarizes the trade-off across thresholds. RMSE is for regression. NDCG is for ranking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recall
Why this is correct
Recall focuses on false negatives.
- ✓
Precision
Why this is correct
Precision focuses on false positives.
- ✗
NDCG
Why it's wrong here
NDCG is for ranking.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC is a single metric, not two separate metrics.
- ✗
RMSE
Why it's wrong here
RMSE is for regression.
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