Answers.Body: <p>Overfitting occurs when the trained model fails to generalize performance to an independent test set.  To assess overfitting, a metric for assessing model performance is needed.  This can be measures of loss (e.g. log loss, Gini index) or &quot;accuracy&quot; (e.g. accuracy, F1 score).  When the losses are higher or the accuracy is lower on the test data, you may have overfitting.  Overfitting can be assessed as a function of multiple factors, including the number of training examples (as you have here) or model hyperparameters (e.g., maximum tree depth, minimum number of examples in leaf nodes).</p>
  <p>Your plot seems to show that with fewer (100) training examples, your model does not generalize as well to test data (lower accuracy compared to training data). This may be overfitting.  As you increase the amount of data, the performance on the test data gradually approaches the training data.</p>
  <p>Overfitting is more common in &quot;small&quot; datasets, and increasing the size of a dataset is a known method of reducing overfitting.  Thus, your observations appear to be consistent with known phenomena.</p>
  <p><strong>Do you think there is a problem with overfitting or others problems?</strong>  There may be overfitting with using less training data, but this seems to be addressed by adding more training data, as explained above.  Data leakage is also a consideration, but you would have to apply your domain knowledge to assess for that.</p>
  
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Answers.CreationDate: 2023-01-08T20:22:09.010Z
Answers.Text: Is this an answer generated by ChatGPT? Because it does not answer the question, and does not consider that this curve is not the one used to detect overfitting.
Answers.UserDisplayName: Dr. Snoopy
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Answers.CreationDate: 2023-01-08T20:44:57.397Z
Answers.Text: I generated this 100% of this response.  The question in the post was asking, "Do you think there is a problem with overfitting or others problems?".  The response is "There may be overfitting with using less training data, but this seems to be addressed by adding more training data, as explained above. Data leakage is also a consideration, but you would have to apply your domain knowledge to assess for that."
Answers.UserDisplayName: Snehal Patel
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-01-08T20:46:10.190Z
Answers.Text: This [SO article](https://stackoverflow.com/questions/54621429/what-does-the-learning-curve-in-classification-decision-tree-mean) addresses the issue of whether the plot can evaluate overfitting.  Main quote from here is "A learning curve shows the validation and training score of an estimator for varying numbers of training samples. It is a tool to find out how much we benefit from adding more training data and whether the estimator suffers more from a variance error or a bias error."  The bias-variance issue is at the heart of evaluating overfitting.
Answers.UserDisplayName: Snehal Patel
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-01-08T21:04:49.847Z
Answers.Text: I provide one more [article](https://www.geeksforgeeks.org/using-learning-curves-ml/) of where these types of plots are used to assess overfitting.  The main quote here is: "Since the training score is very accurate, this indicates low bias and high variance. So this model also begins overfitting the data because the cross-validation score is relatively lower and increases very slowly as the size of the training set increases."
Answers.UserDisplayName: Snehal Patel
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-01-08T23:05:02.547Z
Answers.Text: These articles are wrong, in a learning curve you train multiple models, overfitting is seen in a learning curve of loss vs training steps, not versus the size of the training set. Also to evaluate bias/variance trade-off, you do this in a plot against the number of parameters in your model. These are important differences that are easy to overlook.
Answers.UserDisplayName: Dr. Snoopy
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-01-09T00:02:19.687Z
Answers.Text: Thank you for your input.  I would like to research this topic a bit more.  Stay tuned.
Answers.UserDisplayName: Snehal Patel
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Answers.LastEditDate: 2023-01-08T18:28:25.687Z
Answers.UserDisplayName: Snehal Patel
Body: <p>I'm using a Decision Tree that gave me great test metrics. Then I checked the learning curve, but it seems a little strange to me regarding the training score. Do you think there is a problem with overfitting or others problems? How can I solve it?</p>
  <p><a href="https://i.sstatic.net/Izka7.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Izka7.png" alt="enter image description here" /></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-08T15:29:06.287Z
Comments.Text: How exactly did you make this learning curve?
Comments.UserDisplayName: Dr. Snoopy
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-08T15:32:41.227Z
Comments.Text: I used the LearningCurveDisplay function from sklearn
Comments.UserDisplayName: giovasbr
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-08T15:40:04.423Z
Comments.Text: That learning curve is not used to detect overfitting, only a curve of loss vs training time (epochs) an be used for this purpose.
Comments.UserDisplayName: Dr. Snoopy
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-08T17:30:29.360Z
Comments.Text: If my training and test score are close to 1.0, with respect to fit_time/score_time, is it a possible overfitting?
Comments.UserDisplayName: giovasbr
Tags: <machine-learning><overfitting><decision-trees>
Title: Does this look like overfitting?
UserDisplayName: giovasbr
ThreadText: # Does this look like overfitting?
  
  (Asked by: username_0 on 2023-01-08)
  
  I'm using a Decision Tree that gave me great test metrics. Then I checked the learning curve, but it seems a little strange to me regarding the training score. Do you think there is a problem with overfitting or others problems? How can I solve it?
  
  [![enter image description here](https://i.sstatic.net/Izka7.png)](https://i.sstatic.net/Izka7.png)
  
  **Tags:** machine-learning, overfitting, decision-trees
  
  > **Comments:**
  > **username_1**: How exactly did you make this learning curve?  
  > **username_0**: I used the LearningCurveDisplay function from sklearn  
  > **username_1**: That learning curve is not used to detect overfitting, only a curve of loss vs training time (epochs) an be used for this purpose.  
  > **username_0**: If my training and test score are close to 1.0, with respect to fit_time/score_time, is it a possible overfitting?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-01-08. Score: 1
  
  Overfitting occurs when the trained model fails to generalize performance to an independent test set. To assess overfitting, a metric for assessing model performance is needed. This can be measures of loss (e.g. log loss, Gini index) or "accuracy" (e.g. accuracy, F1 score). When the losses are higher or the accuracy is lower on the test data, you may have overfitting. Overfitting can be assessed as a function of multiple factors, including the number of training examples (as you have here) or model hyperparameters (e.g., maximum tree depth, minimum number of examples in leaf nodes).
  
  Your plot seems to show that with fewer (100) training examples, your model does not generalize as well to test data (lower accuracy compared to training data). This may be overfitting. As you increase the amount of data, the performance on the test data gradually approaches the training data.
  
  Overfitting is more common in "small" datasets, and increasing the size of a dataset is a known method of reducing overfitting. Thus, your observations appear to be consistent with known phenomena.
  
  **Do you think there is a problem with overfitting or others problems?** There may be overfitting with using less training data, but this seems to be addressed by adding more training data, as explained above. Data leakage is also a consideration, but you would have to apply your domain knowledge to assess for that.
  
  > **Comments:**
  > **username_1**: Is this an answer generated by ChatGPT? Because it does not answer the question, and does not consider that this curve is not the one used to detect overfitting.  
  > **username_2**: I generated this 100% of this response.  The question in the post was asking, "Do you think there is a problem with overfitting or others problems?".  The response is "There may be overfitting with using less training data, but this seems to be addressed by adding more training data, as explained above. Data leakage is also a consideration, but you would have to apply your domain knowledge to assess for that."  
  > **username_2**: This [SO article](https://stackoverflow.com/questions/54621429/what-does-the-learning-curve-in-classification-decision-tree-mean) addresses the issue of whether the plot can evaluate overfitting.  Main quote from here is "A learning curve shows the validation and training score of an estimator for varying numbers of training samples. It is a tool to find out how much we benefit from adding more training data and whether the estimator suffers more from a variance error or a bias error."  The bias-variance issue is at the heart of evaluating overfitting.  
  > **username_2**: I provide one more [article](https://www.geeksforgeeks.org/using-learning-curves-ml/) of where these types of plots are used to assess overfitting.  The main quote here is: "Since the training score is very accurate, this indicates low bias and high variance. So this model also begins overfitting the data because the cross-validation score is relatively lower and increases very slowly as the size of the training set increases."  
  > **username_1**: These articles are wrong, in a learning curve you train multiple models, overfitting is seen in a learning curve of loss vs training steps, not versus the size of the training set. Also to evaluate bias/variance trade-off, you do this in a plot against the number of parameters in your model. These are important differences that are easy to overlook.  
  > **username_2**: Thank you for your input.  I would like to research this topic a bit more.  Stay tuned.  
  
  ---
  
  