Answers.Body: <p><strong>Why validation accuracy be greater than training accuracy for deep learning models?</strong></p>
  <p>You are probably thinking that the training process is supposed to reduce the training loss (and increase training accuracy).  The influence on the validation loss (and validation accuracy) should be secondary, no?  And you are 100% right to think that because the loss function only considers training labels (y_train) and how far model predictions (y_pred) deviate from those training labels.  So why is the validation accuracy higher than the training accuracy?  So long as you are not overfitting (which you clearly are not), the training process should have a benefit for both the training accuracy and validation accuracy because the datasets come from the same distribution.  The reason why you may have small differences like you are seeing here has to do with random sampling of the two datasets from that identical distribution.  (You may have seen i.i.d. elsewhere.  It means independent identically distributed.  Each example in the two datasets are presumed to be i.i.d.)</p>
  <p><strong>Bottom line:</strong>  The two samples are different, so they have some differences in accuracy.  It just so happens that the accuracy is higher for the validation set in this case (probably within some margin of error).</p>
  <p>On a separate note, to get a better estimate of your true accuracy along with error bounds, you may consider performing a cross-validation of some sort.  You can review the <a href="https://scikit-learn.org/stable/modules/cross_validation.html" rel="nofollow noreferrer">scikit learn article</a> to see how this is done.</p>
  
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Answers.UserDisplayName: Snehal Patel
Answers.Body: <p>Let consider this example:</p>
  <p>You have a Neural Network for binary classification. The NN is classifying A and B. Let say the NN is not very sophisticated, and it does just always pick A for classification. So on balanced Test-Set the accuracy would always be 50%.</p>
  <p>The Validation accuracy depends on your Validation-Set. Imagine you have the NN from above and your validation-set consists only of A-labeled examples. The validation accuracy would be 100%, while your Training metric would be 50% with a balanced data set.</p>
  <p>When validation metrics perform better than the training ones, this could be the case if the validation set is &quot;simpler&quot; than the training set.</p>
  <p>I also strongly disagree with the other answer, that &quot;you clearly do not overfit&quot;. You can't say for sure based on the data we see. I don't say you do, neither. It depends on whether your data is balanced and diverse.</p>
  <p>One of the best ways to see if you do overfit is to make a k-fold split.</p>
  
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Answers.UserDisplayName: Skobo Do
Answers.Body: <p>Validation accuracy being higher than training accuracy can typically happen for three major reasons:</p>
  <ul>
  <li><p>You have some kind of a regularization that is switched off during validation. Usually this happens with dropout regularization.</p>
  </li>
  <li><p>Validation score is computed <em>once</em> after the whole epoch (after the net have seen whole dataset), while training score is <em>averaged</em> over the  epoch.</p>
  </li>
  <li><p>Random fluctuation.</p>
  </li>
  </ul>
  
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Answers.UserDisplayName: Kostya
Body: <p>I hope you are well.</p>
  <p>I had a problem and didn't understand the answers given on questions similar to my question.</p>
  <p>If possible, please answer this problem in a simpler way.</p>
  <p>Val_acc : %99.4 _
  Train_acc : 97.24</p>
  <p>Thank you for attention.</p>
  <p><a href="https://i.sstatic.net/nQwz2.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/nQwz2.jpg" alt="enter image description here" /></a></p>
  
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Comments.CreationDate: 2022-11-02T06:16:02.370Z
Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: Community
Tags: <neural-networks><machine-learning><deep-learning><convolutional-neural-networks><deep-neural-networks>
Title: why validation accuracy be greater than training accuracy for deep learning models?
UserDisplayName: maserati urm
ThreadText: # why validation accuracy be greater than training accuracy for deep learning models?
  
  (Asked by: username_0 on 2022-11-01)
  
  I hope you are well.
  
  I had a problem and didn't understand the answers given on questions similar to my question.
  
  If possible, please answer this problem in a simpler way.
  
  Val\_acc : %99.4 \_
  Train\_acc : 97.24
  
  Thank you for attention.
  
  [![enter image description here](https://i.sstatic.net/nQwz2.jpg)](https://i.sstatic.net/nQwz2.jpg)
  
  **Tags:** neural-networks, machine-learning, deep-learning, convolutional-neural-networks, deep-neural-networks
  
  > **Comments:**
  > **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_2 on 2022-11-02. Score: 1
  
  **Why validation accuracy be greater than training accuracy for deep learning models?**
  
  You are probably thinking that the training process is supposed to reduce the training loss (and increase training accuracy). The influence on the validation loss (and validation accuracy) should be secondary, no? And you are 100% right to think that because the loss function only considers training labels (y\_train) and how far model predictions (y\_pred) deviate from those training labels. So why is the validation accuracy higher than the training accuracy? So long as you are not overfitting (which you clearly are not), the training process should have a benefit for both the training accuracy and validation accuracy because the datasets come from the same distribution. The reason why you may have small differences like you are seeing here has to do with random sampling of the two datasets from that identical distribution. (You may have seen i.i.d. elsewhere. It means independent identically distributed. Each example in the two datasets are presumed to be i.i.d.)
  
  **Bottom line:** The two samples are different, so they have some differences in accuracy. It just so happens that the accuracy is higher for the validation set in this case (probably within some margin of error).
  
  On a separate note, to get a better estimate of your true accuracy along with error bounds, you may consider performing a cross-validation of some sort. You can review the [scikit learn article](https://scikit-learn.org/stable/modules/cross_validation.html) to see how this is done.
  
  
  ---
  
  ### Answer by username_3 on 2022-11-02. Score: 0
  
  Let consider this example:
  
  You have a Neural Network for binary classification. The NN is classifying A and B. Let say the NN is not very sophisticated, and it does just always pick A for classification. So on balanced Test-Set the accuracy would always be 50%.
  
  The Validation accuracy depends on your Validation-Set. Imagine you have the NN from above and your validation-set consists only of A-labeled examples. The validation accuracy would be 100%, while your Training metric would be 50% with a balanced data set.
  
  When validation metrics perform better than the training ones, this could be the case if the validation set is "simpler" than the training set.
  
  I also strongly disagree with the other answer, that "you clearly do not overfit". You can't say for sure based on the data we see. I don't say you do, neither. It depends on whether your data is balanced and diverse.
  
  One of the best ways to see if you do overfit is to make a k-fold split.
  
  
  ---
  
  ### Answer by username_4 on 2022-11-02. Score: 0
  
  Validation accuracy being higher than training accuracy can typically happen for three major reasons:
  
  * You have some kind of a regularization that is switched off during validation. Usually this happens with dropout regularization.
  * Validation score is computed *once* after the whole epoch (after the net have seen whole dataset), while training score is *averaged* over the epoch.
  * Random fluctuation.
  
  
  ---
  
  