MLS-C01 Modeling Practice Question
Which TWO metrics are suitable for evaluating a regression model? (Select TWO.)
⚠ 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 Accuracy, F1-score, or Precision to regression problems because they are familiar with them from other contexts.
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
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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 and useful for comparing model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Accuracy
Why it's wrong here
Accuracy is for classification problems.
- ✓
Root Mean Squared Error (RMSE)
Why this is correct
RMSE measures average prediction error in regression.
- ✓
R-squared
Why this is correct
R-squared indicates proportion of variance explained.
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F1-score
Why it's wrong here
F1-score is for classification.
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Precision
Why it's wrong here
Precision is for classification.
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