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Answer: Yes, you need to exponentiate the RMSE to interpret it correctly in the original scale of the label variable.
When the label variable is log-transformed, the RMSE calculated on the log scale does not directly represent the error in the original scale. To interpret the RMSE correctly in the original scale, you need to exponentiate the RMSE. This is because the RMSE on the log scale corresponds to the geometric mean of the squared errors in the original scale, and exponentiating the RMSE gives you the geometric mean of the errors, which is a more interpretable measure of error in the original scale.
Author: LeetQuiz Editorial Team
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You are working on a regression problem where the label variable has been log-transformed. You have calculated the root mean squared error (RMSE) of your model's predictions. Do you need to exponentiate the RMSE to interpret it correctly in the original scale of the label variable?
A
Yes, you need to exponentiate the RMSE to interpret it correctly in the original scale of the label variable.
B
No, you do not need to exponentiate the RMSE because it is already in the original scale of the label variable.
C
Yes, you need to exponentiate the RMSE to interpret it correctly, but only if you are using a specific loss function.
D
No, you do not need to exponentiate the RMSE because it is not affected by the log transformation of the label variable.
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