MLS-C01 Modeling Practice Question

A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. Which metric is most appropriate for evaluating model 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-ROC

AUC-ROC is robust to class imbalance and measures the trade-off between true positive rate and false positive rate. Option A is wrong because accuracy can be misleading with imbalanced data. Option C is wrong because RMSE is for regression. Option D is wrong because R-squared is for regression.

Answer analysis

Option-by-option breakdown

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 is misleading for imbalanced classes because a model predicting the majority class always would achieve 95% accuracy.
✓
AUC-ROC
Why this is correct
AUC-ROC evaluates the model's ability to distinguish between classes regardless of threshold and is robust to imbalance.
✗
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is used for regression, not classification.
✗
R-squared
Why it's wrong here
R-squared is used for regression, not classification.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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