A data scientist is evaluating a regression model and wants to understand its prediction errors. Which TWO metrics should they use? (Select TWO.)
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.
Why this answer
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.
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.