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Answer: Exponentiating the RMSE is necessary to convert the error back to the original scale of the target variable, providing a more interpretable measure of error.
When the log of the target variable is used, the predictions are also in the log scale. The RMSE calculated in this log space represents the error in terms of log units. Exponentiating the RMSE converts this error back to the original scale of the target variable, allowing for a more intuitive understanding of the model's performance in terms of the actual units of the target variable.
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In a regression problem where the log of the target variable is used, explain why it is necessary to exponentiate the Root Mean Square Error (RMSE) when reporting the error. Provide a detailed explanation and include a hypothetical example.
A
Exponentiating the RMSE is necessary to convert the error back to the original scale of the target variable, providing a more interpretable measure of error.
B
Exponentiating the RMSE increases the error, making the model appear worse than it actually is.
C
Exponentiating the RMSE is only necessary for linear regression models, not for other types of regression models.
D
Exponentiating the RMSE is a statistical convention but has no practical impact on model interpretation.
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