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MLS-C01 Modeling Practice Question

A data scientist is evaluating a binary classification model that predicts whether a customer will churn. The model achieves an AUC of 0.85 on the test set. Which TWO statements about AUC are correct? (Choose two.)

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

AUC represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.

AUC measures the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance, which is correctly stated in option A. AUC is also threshold-independent, evaluating model ranking across all thresholds, as stated in option E. Option B is incorrect because an AUC of 0.85 is better than random (0.5). Option C is incorrect because AUC is not average precision; average precision is a different metric. Option D is incorrect because AUC is not equivalent to accuracy at any specific threshold.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • AUC represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.

    Why this is correct

    This is the statistical interpretation of AUC.

  • An AUC of 0.85 indicates the model is no better than random guessing.

    Why it's wrong here

    AUC of 0.5 is random; 0.85 is significantly better.

  • AUC is the average precision across all thresholds.

    Why it's wrong here

    Average precision is a different metric (PR AUC).

  • AUC is equivalent to the accuracy of the model at the default threshold of 0.5.

    Why it's wrong here

    AUC is not accuracy; it is area under the ROC curve.

  • AUC is threshold-independent, meaning it evaluates the model's ranking performance across all thresholds.

    Why this is correct

    AUC summarizes the ROC curve across all thresholds.

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Last reviewed: Jun 20, 2026

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