Question 867 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is evaluating a binary classification model. The model's AUC-ROC is 0.95. Which TWO statements are true?
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
✓
The model has excellent discriminative ability
AUC-ROC measures the model's ability to distinguish between classes across all thresholds. A high AUC (close to 1) indicates good performance. AUC-ROC is threshold-independent. It does not directly indicate accuracy or calibration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model has no false positives
Why it's wrong here
AUC-ROC does not imply zero false positives.
- ✓
The model has excellent discriminative ability
Why this is correct
AUC close to 1 indicates strong separation between classes.
- ✓
The model's performance is independent of the decision threshold
Why this is correct
AUC-ROC aggregates performance over all thresholds, so it is threshold-independent.
- ✗
The model is well-calibrated
Why it's wrong here
AUC does not measure calibration; it measures rank ordering.
- ✗
The model's accuracy is at least 95%
Why it's wrong here
AUC does not directly translate to accuracy; accuracy depends on threshold.
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Last reviewed: Jun 20, 2026
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