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AIF-C01 Practice Question: A data scientist is evaluating a regression model…
A data scientist is evaluating a regression model and wants to understand its prediction errors. Which TWO metrics should they use? (Select TWO.)
⚠ Common exam trap
AWS often tests the distinction between classification and regression metrics, and the trap here is that candidates mistakenly apply classification metrics like F1 score, Accuracy, or Precision to a regression problem because they are more commonly discussed in introductory ML courses.
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
✓
Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is correct because it directly quantifies regression prediction error as the average of the absolute differences between predicted and actual values, expressed in the same units as the target variable. Root Mean Squared Error (RMSE) is also correct because it measures regression error as the square root of the mean of squared differences, penalizing larger errors more heavily and remaining in the target's units. Both MAE and RMSE are standard regression error metrics that summarize how far predictions deviate from actual continuous values. F1 score is incorrect because it is a classification metric combining precision and recall, not applicable to continuous regression errors. Accuracy is incorrect because it measures the proportion of correct class predictions in classification. Precision is incorrect because it is a classification metric for the proportion of positive predictions that are truly positive, not a regression error 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.
- ✗
F1 score
Why it's wrong here
F1 score combines precision and recall into a single classification measure, so it cannot quantify the size or direction of regression residuals. It is tempting as a familiar model-quality metric, but it would be correct when balancing false positives and false negatives for a classifier.
- ✓
Mean Absolute Error (MAE)
Why this is correct
Mean Absolute Error averages the absolute differences between predicted and actual values, expressing typical error magnitude in the target's original units. This directly satisfies the goal of understanding prediction errors, and it is less sensitive to outliers than squared-error metrics.
- ✓
Root Mean Squared Error (RMSE)
Why this is correct
Root Mean Squared Error takes the square root of mean squared error, returning error in the target's original units while penalising large deviations more heavily. This satisfies the requirement to understand prediction errors, particularly their magnitude and the impact of outliers.
- ✗
Accuracy
Why it's wrong here
Accuracy reports the fraction of correct class predictions, which requires discrete labels rather than continuous target values. It tempts because it is the most familiar performance measure, but it would be correct when evaluating a classification model on roughly balanced classes.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of positive identifications that were actually correct, so it applies to classification, not continuous error magnitude. It tempts because it is a standard evaluation metric, but it would be the right choice when assessing a classifier's false-positive behaviour on imbalanced classes.
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