Mean Absolute Error MAE is correct because it is defined as the mean of the absolute differences between predicted and actual values and it is measured in the same units as the target where a smaller value signals better accuracy.

This metric computes the average of absolute deviations from the ground truth and it is directly interpretable in the original units of the target variable which matches the description in the question.

Coefficient of Determination R2 is incorrect because it is a unitless measure of the proportion of variance explained by the model and it does not represent an average absolute difference in the target units.

Root Mean Squared Error RMSE is incorrect because although it is measured in the same units it computes the square root of the mean of squared errors and therefore emphasizes larger errors rather than taking the mean of absolute differences.

Mean Absolute Percentage Error MAPE is incorrect because it reports errors as percentages of the actual values and therefore is not measured in the same units as the target and can be unstable when actual values are near zero.

Relative Absolute Error RAE is incorrect because it expresses the total absolute error relative to a baseline such as the error of a naive predictor and it is a relative ratio rather than the simple mean of absolute differences in the target units.

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