Answers.Body: <p>Boosted trees iteratively train on misclassified examples.  This makes it focus on hard to classify examples.  This would typically cause overfitting but the ensemble helps.  But from a training perspective it will look like overfitting (high training, lower validation).</p>
  <p>In your case as you increase training examples the training accuracy is always overfit but as more examples are included validation accuracy rises.</p>
  
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Answers.CreationDate: 2024-09-24T02:10:26.383Z
Answers.Text: Thanks for your answer.But I tried another two times, I got a decrese and a increse trainng curve, y-label is accuracy, which is right?
Answers.UserDisplayName: Chiatung Liang
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Answers.CreationDate: 2024-09-24T03:16:17.340Z
Answers.Text: How are you choosing training samples?  Deterministic or random?
Answers.UserDisplayName: foreverska
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Answers.CreationDate: 2024-10-04T07:11:59.787Z
Answers.Text: Yes， I use  GridSearchCV and Cross-Validation to search best params.
Answers.UserDisplayName: Chiatung Liang
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Answers.CreationDate: 2024-09-22T14:35:44.880Z
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  I am having a problem with a machine learning model used to monitor alerts for binary classification, the image of the learning curve looks like this, obviously there is something wrong because the training curve is almost a horizontal straight line, but what is the problem?</p>
  <p>I have tuned the parameters of the model many times and it still looks like this. The model I'm using is XGboost.</p>
  
Tags: <machine-learning><python>
Title: Learning Curve of Learnig Machine is not good, how to improve?
UserDisplayName: Chiatung Liang
ThreadText: # Learning Curve of Learnig Machine is not good, how to improve?
  
  (Asked by: username_0 on 2024-09-21)
  
  [![enter image description here](https://i.sstatic.net/VCrrdj6t.png)](https://i.sstatic.net/VCrrdj6t.png)[![enter image description here](https://i.sstatic.net/kniVDFb8.png)](https://i.sstatic.net/kniVDFb8.png)
  
  [![enter image description here](https://i.sstatic.net/9EYIV5KN.png)](https://i.sstatic.net/9EYIV5KN.png)
  I am having a problem with a machine learning model used to monitor alerts for binary classification, the image of the learning curve looks like this, obviously there is something wrong because the training curve is almost a horizontal straight line, but what is the problem?
  
  I have tuned the parameters of the model many times and it still looks like this. The model I'm using is XGboost.
  
  **Tags:** machine-learning, python
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-09-22. Score: 0
  
  Boosted trees iteratively train on misclassified examples. This makes it focus on hard to classify examples. This would typically cause overfitting but the ensemble helps. But from a training perspective it will look like overfitting (high training, lower validation).
  
  In your case as you increase training examples the training accuracy is always overfit but as more examples are included validation accuracy rises.
  
  > **Comments:**
  > **username_0**: Thanks for your answer.But I tried another two times, I got a decrese and a increse trainng curve, y-label is accuracy, which is right?  
  > **username_1**: How are you choosing training samples?  Deterministic or random?  
  > **username_0**: Yes， I use  GridSearchCV and Cross-Validation to search best params.  
  
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