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There are three common evaluation metrics for regression problems: 

1. Mean Absolute Error (MAE).

2. Mean Squared Error (MSE).

3. Root Mean Squared Error (RMSE). 

Note that:

1. MAE (Mean Absolute Error): 

   It is easy to understand as it represents the average absolute difference between predicted and actual values. However, it treats all errors equally without emphasizing larger errors. 

2.  MSE (Mean Squared Error): 

   MSE is more popular than MAE in some contexts because it not only considers the magnitude of errors but also penalizes larger errors more significantly due to the squaring. This can be beneficial in real-world scenarios where larger errors may have more severe consequences. 

3.  RMSE (Root Mean Squared Error): 

   RMSE is even more popular than MSE because it returns to the same scale as the target variable, making it more interpretable in the context of the original "y" units. It addresses the issue of squared errors in MSE by taking the square root of the average squared differences, providing a measure of the typical size of the errors. 

 Therefore, the idea is that while MAE is straightforward, MSE and RMSE introduce additional considerations for larger errors and provide a more interpretable measure in the original units of the target variable. The choice among these metrics depends on the specific requirements and characteristics of the regression problem at hand. 

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