Answers.Body: <p>It implies that your regularization effects are too much, and prevent the model from learning from data. Also, at such a low accuracy (~10%), we can't really talk about overfitting.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-09-13T00:15:57.620Z
Answers.LastActivityDate: 2021-09-13T00:15:57.620Z
Answers.UserDisplayName: SpiderRico
Body: <p>Accuracy of my regularized model is higher for training set than for validation set.</p>
  <p><a href="https://i.sstatic.net/DGgTO.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/DGgTO.png" alt="enter image description here" /></a></p>
  <p>The situation improves when regularization coeefficient is reduced:
  <a href="https://i.sstatic.net/ea5wP.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ea5wP.png" alt="enter image description here" /></a></p>
  <p>What does this really imply?</p>
  <p>From my understanding, this seems to suggest that regularization is actually resulting in the model overfitting training set, which is the opposite of the intended outcome</p>
  
Tags: <neural-networks><regularization>
Title: What does it mean when accuracy of regularized model is higher for training set than for validation set?
UserDisplayName: Aadith Ramia
ThreadText: # What does it mean when accuracy of regularized model is higher for training set than for validation set?
  
  (Asked by: username_0 on 2021-09-13)
  
  Accuracy of my regularized model is higher for training set than for validation set.
  
  [![enter image description here](https://i.sstatic.net/DGgTO.png)](https://i.sstatic.net/DGgTO.png)
  
  The situation improves when regularization coeefficient is reduced:
  [![enter image description here](https://i.sstatic.net/ea5wP.png)](https://i.sstatic.net/ea5wP.png)
  
  What does this really imply?
  
  From my understanding, this seems to suggest that regularization is actually resulting in the model overfitting training set, which is the opposite of the intended outcome
  
  **Tags:** neural-networks, regularization
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-09-13. Score: 2
  
  It implies that your regularization effects are too much, and prevent the model from learning from data. Also, at such a low accuracy (~10%), we can't really talk about overfitting.
  
  
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