MLA-C01 ML Model Development Practice Question

A data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing model performance?

⚠ Common exam trap

The trap is defaulting to accuracy because it is the most familiar metric — on a 95/5 imbalance, accuracy is dominated by the majority class and hides poor positive-class performance.

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

AUC (Area Under the ROC Curve) evaluates the model's ability to rank positive instances above negatives across all classification thresholds, making it robust to class imbalance because it does not depend on a single threshold or on the majority class dominating the score. With only 5% positives, accuracy and threshold-dependent metrics can be misleading, but AUC remains a reliable discriminator measure.

Answer analysis

Option-by-option breakdown

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

✗
Precision
Why it's wrong here
Precision ignores the true positives missed entirely, so a model catching only a fraction of the 5% positives can still score highly. It is the right metric when false positives are the costly error and the positive class is well represented.
✗
Accuracy
Why it's wrong here
Accuracy counts all predictions equally, so a model labelling every case negative scores 95% while detecting no positives — it cannot expose performance on the minority class. It is tempting because accuracy suits balanced datasets where class frequencies are comparable, but with a 5% positive rate it masks the failures that matter here.
✗
Recall
Why it's wrong here
Recall alone counts false positives as harmless, so flagging every case as positive scores 100%. It is the right metric when missing a positive is the costly error, but it must be paired with precision to be meaningful.
✓
AUC
Why this is correct
AUC measures ranking quality across all thresholds and stays insensitive to class prevalence, so the 5% positive rate does not distort it. Accuracy would mislead here, since predicting all negatives scores 95%. AUC therefore satisfies the stem's imbalanced-classification constraint by summarising separability between classes.

Go deeper

MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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