How does the choice of loss function such as MSE or MAE affect the training of a regression model?
- MSE and MAE have no significant difference in the training process
- MSE emphasizes larger errors more; MAE treats all errors equally
- MSE is less sensitive to outliers; MAE is more computationally intensive
- MSE requires more computational resources; MAE is more robust to noise
The choice between Mean Squared Error (MSE) and Mean Absolute Error (MAE) has a significant impact on the training process. MSE squares the errors, emphasizing larger mistakes more, while MAE takes the absolute value of the errors, treating all errors equally. This means that models using MSE are more sensitive to outliers, while those using MAE may be more robust.
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