MLS-C01 Modeling Practice Question
A data scientist is evaluating a regression model. Which TWO metrics are appropriate for evaluating regression performance?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between classification and regression metrics, and the trap here is that candidates mistakenly apply classification metrics like F1, AUC, or Precision to regression problems because they confuse evaluation domains.
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
✓
Root Mean Squared Error (RMSE)
Root Mean Squared Error (RMSE) is a standard metric for regression models because it measures the average magnitude of prediction errors in the same units as the target variable. It penalizes larger errors more heavily due to squaring, making it sensitive to outliers, which is useful for evaluating model accuracy in continuous value prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Root Mean Squared Error (RMSE)
Why this is correct
RMSE measures average prediction error.
- ✗
F1 score
Why it's wrong here
F1 score is for classification.
- ✗
Area Under the ROC Curve (AUC)
Why it's wrong here
AUC is for classification.
- ✓
R-squared
Why this is correct
R-squared measures proportion of variance explained.
- ✗
Precision
Why it's wrong here
Precision is for classification.
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