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Exploratory Data AnalysismediumMultiple ChoiceObjective-mapped

MLS-C01 Exploratory Data Analysis Practice Question

A machine learning engineer trains a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. After training, the model achieves 99% accuracy but only 10% recall on the positive class. Which metric should the engineer focus on to evaluate the model's performance on the minority class?

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

F1 score

(F1 score) is the correct metric because it balances precision and recall, providing a single measure that is robust to class imbalance. With only 1% positive class, accuracy (Option B) is misleadingly high due to the majority class. AUC-ROC (Option C) can still be high even if recall is low, as it evaluates ranking rather than absolute performance. Precision (Option D) only considers the proportion of correct positive predictions, ignoring false negatives, which is not suitable when recall is poor. The F1 score captures both aspects, making it the best choice for evaluating minority class performance in this scenario.

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 this is correct

    F1 score considers both precision and recall, giving a better measure for imbalanced data.

  • Accuracy

    Why it's wrong here

    Accuracy is high due to class imbalance and does not reflect minority class performance.

  • AUC-ROC

    Why it's wrong here

    AUC-ROC can be high even when recall is low if the model ranks positives well.

  • Precision

    Why it's wrong here

    Precision alone may be high if few false positives, but recall could be low.

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Last reviewed: Jun 20, 2026

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