MLS-C01 Modeling Practice Question
Which TWO metrics are MOST appropriate for evaluating a regression model that predicts house prices, where the business is most sensitive to large errors?
⚠ Common exam trap
Watch out — candidates often choose MAE (Option D) because it is a common regression metric, but they fail to recognize that MAE does not penalize large errors more heavily, which is the key business requirement in this scenario.
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)
RMSE is most appropriate because it squares the errors before averaging, which heavily penalizes large errors. Since the business is most sensitive to large errors in house price predictions, RMSE directly aligns with this requirement by amplifying the impact of outliers, making it a suitable metric for evaluating model performance in this context.
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 squares errors, so large errors are penalized heavily.
- ✓
Mean Absolute Percentage Error (MAPE)
Why this is correct
MAPE is scale-independent and penalizes large errors proportionally.
- ✗
Accuracy
Why it's wrong here
Accuracy is for classification.
- ✗
Mean Absolute Error (MAE)
Why it's wrong here
MAE does not penalize large errors more.
- ✗
R-squared
Why it's wrong here
R-squared measures variance explained, not error magnitude.
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