Body: <p>I am working on a Neural Network and have plotted the performance of my model. However the plots seem not to fit the &quot;trends&quot; (which help you identify the issue with your model) presented in this illustration.<a href="https://i.sstatic.net/f18sB.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/f18sB.png" alt="enter image description here" /></a></p>
  <p>Here is the performance of my model<a href="https://i.sstatic.net/QPgJG.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/QPgJG.png" alt="enter image description here" /></a>
  The loss metric I used was Binary Cross Entropy (due to my problem being a binary classification task).
  Is my model over or under-fitting? and how can you tell?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-17T02:45:32.037Z
Comments.Text: What I find weird is that my Validation accuracy is so high, while the Test one is so low
Comments.UserDisplayName: jr123456jr987654321
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-17T03:48:23.833Z
Comments.Text: Something is wrong with your testing and validation sets. They are not representative of each other, because if they were they would get similar results (with the test set being slightly worse normally)
Comments.UserDisplayName: Recessive
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-17T04:04:52.943Z
Comments.Text: Thanks, this may have been due to the fact that I have used SMOTE in order to do some data augmentation (that data made up 30% of my dataset)
Comments.UserDisplayName: jr123456jr987654321
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-04-17T04:12:17.017Z
Comments.Text: In that case I would guess your augmented data isn't representative, and the test set is indicating this. Either way, you can definitely narrow down the issue to whatever differences lie between the validation and test set. The network is learning fine and doesn't seem to be overfitting
Comments.UserDisplayName: Recessive
Tags: <neural-networks><machine-learning><overfitting><performance><underfitting>
Title: Identifying if a model is over or under-fitting via graphs
UserDisplayName: jr123456jr987654321
ThreadText: # Identifying if a model is over or under-fitting via graphs
  
  (Asked by: username_0 on 2021-04-17)
  
  I am working on a Neural Network and have plotted the performance of my model. However the plots seem not to fit the "trends" (which help you identify the issue with your model) presented in this illustration.[![enter image description here](https://i.sstatic.net/f18sB.png)](https://i.sstatic.net/f18sB.png)
  
  Here is the performance of my model[![enter image description here](https://i.sstatic.net/QPgJG.png)](https://i.sstatic.net/QPgJG.png)
  The loss metric I used was Binary Cross Entropy (due to my problem being a binary classification task).
  Is my model over or under-fitting? and how can you tell?
  
  **Tags:** neural-networks, machine-learning, overfitting, performance, underfitting
  > **Comments:**
  > **username_0**: What I find weird is that my Validation accuracy is so high, while the Test one is so low  
  > **username_1**: Something is wrong with your testing and validation sets. They are not representative of each other, because if they were they would get similar results (with the test set being slightly worse normally)  
  > **username_0**: Thanks, this may have been due to the fact that I have used SMOTE in order to do some data augmentation (that data made up 30% of my dataset)  
  > **username_1**: In that case I would guess your augmented data isn't representative, and the test set is indicating this. Either way, you can definitely narrow down the issue to whatever differences lie between the validation and test set. The network is learning fine and doesn't seem to be overfitting  
  
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  ## 0 answers
  
  