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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 company is building a text classification model to categorize customer support tickets. The dataset is highly imbalanced with 95% of tickets belonging to 'General Inquiry' and 5% to 'Complaint'. The data scientist is using a random forest classifier. Which metric is most appropriate for evaluating model performance on the minority class?
Accuracy
Why wrong: Accuracy would be high even if the model predicts only the majority class.
F1-score for 'Complaint'
F1-score balances precision and recall, making it suitable for imbalanced classification.
ROC AUC
Why wrong: ROC AUC can be optimistic for imbalanced datasets and does not directly focus on the minority class.
Precision for 'Complaint'
Why wrong: Precision alone ignores recall, which could miss many complaints.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
F1-score for 'Complaint'
In a highly imbalanced dataset (95% General Inquiry, 5% Complaint), accuracy is misleading because a model that predicts 'General Inquiry' for every ticket would achieve 95% accuracy but completely fail on the minority class. The F1-score for 'Complaint' is the harmonic mean of precision and recall, providing a balanced evaluation of the model's ability to correctly identify complaints without being skewed by the majority class. For a random forest classifier, this metric directly addresses the minority class performance, which is the primary concern.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 would be high even if the model predicts only the majority class.
F1-score for 'Complaint'
Why this is correct
F1-score balances precision and recall, making it suitable for imbalanced classification.
Related concept
Read the scenario before looking for a memorised answer.
ROC AUC
Why it's wrong here
ROC AUC can be optimistic for imbalanced datasets and does not directly focus on the minority class.
Precision for 'Complaint'
Why it's wrong here
Precision alone ignores recall, which could miss many complaints.
Common exam traps
The trap here is that candidates often choose accuracy due to its simplicity, failing to recognize that on imbalanced datasets it is a deceptive metric, or they select ROC AUC because it is commonly used for binary classification, but it does not isolate minority class performance as effectively as the F1-score.
Detailed technical explanation
The F1-score is particularly useful when the class distribution is skewed because it penalizes models that sacrifice recall for precision or vice versa. In random forests, the class imbalance can cause the majority class to dominate the voting mechanism; techniques like class weighting or balanced subsampling are often used to mitigate this, and the F1-score on the minority class directly reflects the effectiveness of such adjustments. A real-world scenario is a customer support system where missing a complaint (false negative) leads to customer churn, while false positives (misclassifying general inquiries as complaints) waste resources—F1-score balances these costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: F1-score for 'Complaint' — In a highly imbalanced dataset (95% General Inquiry, 5% Complaint), accuracy is misleading because a model that predicts 'General Inquiry' for every ticket would achieve 95% accuracy but completely fail on the minority class. The F1-score for 'Complaint' is the harmonic mean of precision and recall, providing a balanced evaluation of the model's ability to correctly identify complaints without being skewed by the majority class. For a random forest classifier, this metric directly addresses the minority class performance, which is the primary concern.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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