- A
F1 score (macro-averaged)
Why wrong: F1 score is based on thresholded predictions, not raw probabilities.
- B
Accuracy
Why wrong: Accuracy does not consider probability calibration and is not sensitive to ranking.
- C
Mean Absolute Error
Why wrong: MAE is for regression, not classification.
- D
Log loss (cross-entropy)
Log loss directly measures the quality of probability predictions for multiclass problems.
- E
ROC AUC (one-vs-rest macro-averaged)
Why wrong: ROC AUC is appropriate for binary classification, not directly for multiclass probabilities.
Question 1,314 of 1,755
Log Loss: The Best Metric for Multiclass Classification
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. A key principle to apply: log Loss (Cross-Entropy). Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist wants to evaluate the performance of a multiclass classification model. The model outputs probabilities for 10 classes. Which metric is most appropriate for evaluating the model's ranking performance across all classes?
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
✓
Log loss (cross-entropy)
Option D is correct because log loss measures the performance of a classification model where the prediction is a probability value, and it penalizes false classifications. Option A (F1 score macro-averaged) is not appropriate because it focuses on threshold-based metrics rather than probability ranking. Option B (Accuracy) ignores probability calibration. Option C (Mean Absolute Error) is for regression, not classification. Option E (ROC AUC one-vs-rest macro-averaged) can be used for multiclass ranking, but log loss is more appropriate when the model outputs probabilities and you want to evaluate the overall probability quality across all classes.
Key principle: Log Loss (Cross-Entropy)
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 (macro-averaged)
Why it's wrong here
F1 score is based on thresholded predictions, not raw probabilities.
- ✗
Accuracy
Why it's wrong here
Accuracy does not consider probability calibration and is not sensitive to ranking.
- ✗
Mean Absolute Error
Why it's wrong here
MAE is for regression, not classification.
- ✓
Log loss (cross-entropy)
Why this is correct
Log loss directly measures the quality of probability predictions for multiclass problems.
Related concept
Log Loss (Cross-Entropy)
- ✗
ROC AUC (one-vs-rest macro-averaged)
Why it's wrong here
ROC AUC is appropriate for binary classification, not directly for multiclass probabilities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Log Loss (Cross-Entropy)
- Multiclass Classification
- Probabilistic Evaluation
- Macro-Averaging
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Log Loss (Cross-Entropy)
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Log Loss (Cross-Entropy) Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review log Loss (Cross-Entropy), then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Log Loss (Cross-Entropy).
What is the correct answer to this question?
The correct answer is: Log loss (cross-entropy) — Option D is correct because log loss measures the performance of a classification model where the prediction is a probability value, and it penalizes false classifications. Option A (F1 score macro-averaged) is not appropriate because it focuses on threshold-based metrics rather than probability ranking. Option B (Accuracy) ignores probability calibration. Option C (Mean Absolute Error) is for regression, not classification. Option E (ROC AUC one-vs-rest macro-averaged) can be used for multiclass ranking, but log loss is more appropriate when the model outputs probabilities and you want to evaluate the overall probability quality across all classes.
What should I do if I get this MLS-C01 question wrong?
Review log Loss (Cross-Entropy), then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Log Loss (Cross-Entropy)
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Last reviewed: Jun 20, 2026
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