MLS-C01 Modeling Practice Question
A data scientist is working on a multi-class classification problem with 10 classes. The model outputs probabilities and the scientist wants to evaluate the model's ability to rank classes correctly. Which metric is most appropriate?
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
✓
Area Under the ROC Curve (AUC-ROC)
The most appropriate metric for evaluating a multi-class classifier's ability to rank classes (i.e., order classes by predicted probability) is the Area Under the ROC Curve (AUC-ROC). AUC-ROC measures the model's ability to distinguish between classes across all thresholds, and for multi-class problems it can be extended using one-vs-rest or macro/micro averaging. Log loss (Option D) measures probability calibration, not ranking. F1 score (Option A) is a threshold-dependent metric suitable for binary or per-class evaluation, not overall ranking. Accuracy (Option B) is also threshold-dependent and does not consider probability ranking. Therefore, Option C (AUC-ROC) is correct.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
F1 score
Why it's wrong here
F1 score is for binary classification or per-class, but not a ranking metric for multi-class.
- ✗
Accuracy
Why it's wrong here
Accuracy does not consider ranking or probability outputs.
- ✓
Area Under the ROC Curve (AUC-ROC)
Why this is correct
Area Under the ROC Curve measures ranking ability; one-vs-rest AUC can be used for multi-class.
- ✗
Log loss
Why it's wrong here
Log loss measures calibration of probabilities, not ranking performance.
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