Answers.Body: <p>If you train the same model with two different loss functions, such as MAE and RMSE, the model may reach a plateau at different places for each loss function. This is because these loss functions have different sensitivities to errors and are optimized differently during training.</p>
  <p>In your specific case, where there are not many outliers in the dataset, the differences in the training dynamics between MAE and RMSE might be less pronounced. However, it is still possible for the models to reach a plateau at different points, because the two loss functions handle errors differently. The model with RMSE may converge slightly later than the one with MAE because it focuses more on reducing larger errors.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-03-25T23:36:07.423Z
Answers.LastActivityDate: 2023-03-25T23:36:07.423Z
Answers.UserDisplayName: Hans-Peter Schrei
Body: <p>If we use two loss functions that are in the same &quot;units&quot; (say MAE and RMSE). Should training the same model with these two different loss functions result in plateau happening at a different place?</p>
  <p>Say in this particular case our dataset does not have many outliers so there's not much penalizing the outliers.</p>
  
Tags: <deep-learning><training>
Title: Does changing deep learning model loss function affect the occurence of plateau?
UserDisplayName: Darren Rahnemoon
ThreadText: # Does changing deep learning model loss function affect the occurence of plateau?
  
  (Asked by: username_0 on 2023-03-25)
  
  If we use two loss functions that are in the same "units" (say MAE and RMSE). Should training the same model with these two different loss functions result in plateau happening at a different place?
  
  Say in this particular case our dataset does not have many outliers so there's not much penalizing the outliers.
  
  **Tags:** deep-learning, training
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-03-25. Score: 0 | Accepted answer
  
  If you train the same model with two different loss functions, such as MAE and RMSE, the model may reach a plateau at different places for each loss function. This is because these loss functions have different sensitivities to errors and are optimized differently during training.
  
  In your specific case, where there are not many outliers in the dataset, the differences in the training dynamics between MAE and RMSE might be less pronounced. However, it is still possible for the models to reach a plateau at different points, because the two loss functions handle errors differently. The model with RMSE may converge slightly later than the one with MAE because it focuses more on reducing larger errors.
  
  
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