Answers.Body: <p>Note the X index is training set size. For the first and second case, teh training set size starts at 0(or 1). The model will overfit certainly at that data size. When data size increases, the model overfits less and less and eventually the model have enough data samples that it won't overfit. The data size continue to increase and the model performance increase as well. To a certain point, the validation loss increase start to diminish and the model slightly overfits the samples.  For the third graph, it seems like originally teh loss is low and started to increase.
  Hopes it help</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-20T23:34:27.090Z
Answers.LastActivityDate: 2019-10-20T23:34:27.090Z
Answers.UserDisplayName: Clement
Body: <p>Bellow I have a Learning Curve plot How should I interpret this plot for my random forrest algorithm (the second one the most complex one)? 
  Which one is the best?</p>
  
  <p><a href="https://i.sstatic.net/v4P65.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/v4P65.png" alt="enter image description here"></a></p>
  
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Tags: <machine-learning><cross-validation><random-forests>
Title: How to interpret this learning curve plot
UserDisplayName: jennifer ruurs
ThreadText: # How to interpret this learning curve plot
  
  (Asked by: username_0 on 2019-10-19)
  
  Bellow I have a Learning Curve plot How should I interpret this plot for my random forrest algorithm (the second one the most complex one)?
  Which one is the best?
  
  [![enter image description here](https://i.sstatic.net/v4P65.png)](https://i.sstatic.net/v4P65.png)
  
  [![enter image description here](https://i.sstatic.net/Iz5ZP.png)](https://i.sstatic.net/Iz5ZP.png)
  
  [![enter image description here](https://i.sstatic.net/W44dm.png)](https://i.sstatic.net/W44dm.png)
  
  **Tags:** machine-learning, cross-validation, random-forests
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-10-20. Score: 1 | Accepted answer
  
  Note the X index is training set size. For the first and second case, teh training set size starts at 0(or 1). The model will overfit certainly at that data size. When data size increases, the model overfits less and less and eventually the model have enough data samples that it won't overfit. The data size continue to increase and the model performance increase as well. To a certain point, the validation loss increase start to diminish and the model slightly overfits the samples. For the third graph, it seems like originally teh loss is low and started to increase.
  Hopes it help
  
  
  ---
  
  