Answers.Body: <p>Without any additional information, lean towards the vote of the best performing classifier when it comes to ties. </p>
  
  <p>However, as others have stated already, it is best to analyze the performances in more detail (e.g. confusion matrices). </p>
  
  <p>For instance, it could be that model B almost always classifies class X correctly (hardly any false positives). In that case, you could lean towards the prediction of model B if it predicts class X and model A does not. In other words; you could weigh the votes of the models based on how well they did in similar, previous predictions. </p>
  
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Answers.CreationDate: 2018-11-02T10:45:37.110Z
Answers.LastActivityDate: 2018-11-02T11:42:05.923Z
Answers.LastEditDate: 2018-11-02T11:42:05.923Z
Answers.UserDisplayName: Saber
Answers.Body: <p>Given only the fact that model A has a higher accuracy than model B you should just use model A.  More information on the performance of the two classifiers should be provided for a better answer.  </p>
  
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Answers.CreationDate: 2018-11-02T01:46:40.517Z
Answers.Text: But what is the margin of error?  imho I don't see a lot of difference between 82% and 79%...
Answers.UserDisplayName: DukeZhou
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Answers.Text: We don't have any information to determine a 'margin of error'.  Without any other performance data I would say 82% is 3% more than 79% which is enough to be the winner.  With more detailed information from Siddarth we could make a better analysis.  Accuracy is not a lot to make a determination especially when the application is not known.  I have seen high accuracy classifiers get beaten by much lower accuracy classifiers.
Answers.UserDisplayName: Brian O'Donnell
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Answers.CreationDate: 2018-11-01T23:23:00.207Z
Answers.LastActivityDate: 2018-11-02T02:07:40.520Z
Answers.LastEditDate: 2018-11-02T02:07:40.520Z
Answers.UserDisplayName: Brian O'Donnell
Body: <p>I am building 2 models using XGboost, one with x number of parameters and the other with y number of parameters of the data set.</p>
  
  <p>It is a classification problem.
  A yes-yes, no-no case is easy, but what should I do when one model predicts a yes and the other model predicts a no ?</p>
  
  <p>Model A with x parameters has an accuracy of 82% and model B with y parameters has accuracy of 79%.  </p>
  
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Comments.CreationDate: 2018-11-01T03:14:51.617Z
Comments.Text: You haven't given any information on how well these two models perform.  Show their ROC curves, confusion matrices, accuracies, etc.  Once you summarize how the two models perform you can pick the better of the two and ignore the other one.
Comments.UserDisplayName: Brian O'Donnell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-11-01T19:52:26.883Z
Comments.Text: @siddharth I approved the edit, but can I ask, are siddharth and Siddharth Srivastava the same person?  (If so, please consider merging the two accounts:)
Comments.UserDisplayName: DukeZhou
Tags: <data-science>
Title: Ensemble models - XGboost
UserDisplayName: Siddharth Srivastava
ThreadText: # Ensemble models - XGboost
  
  (Asked by: username_0 on 2018-10-31)
  
  I am building 2 models using XGboost, one with x number of parameters and the other with y number of parameters of the data set.
  
  It is a classification problem.
  A yes-yes, no-no case is easy, but what should I do when one model predicts a yes and the other model predicts a no ?
  
  Model A with x parameters has an accuracy of 82% and model B with y parameters has accuracy of 79%.
  
  **Tags:** data-science
  
  > **Comments:**
  > **username_1**: You haven't given any information on how well these two models perform.  Show their ROC curves, confusion matrices, accuracies, etc.  Once you summarize how the two models perform you can pick the better of the two and ignore the other one.  
  > **username_2**: @username_0 I approved the edit, but can I ask, are username_0 and username_0 username_0 the same person?  (If so, please consider merging the two accounts:)  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2018-11-02. Score: 1
  
  Without any additional information, lean towards the vote of the best performing classifier when it comes to ties.
  
  However, as others have stated already, it is best to analyze the performances in more detail (e.g. confusion matrices).
  
  For instance, it could be that model B almost always classifies class X correctly (hardly any false positives). In that case, you could lean towards the prediction of model B if it predicts class X and model A does not. In other words; you could weigh the votes of the models based on how well they did in similar, previous predictions.
  
  
  ---
  
  ### Answer by username_1 on 2018-11-01. Score: 0
  
  Given only the fact that model A has a higher accuracy than model B you should just use model A. More information on the performance of the two classifiers should be provided for a better answer.
  
  > **Comments:**
  > **username_2**: But what is the margin of error?  imho I don't see a lot of difference between 82% and 79%...  
  > **username_1**: We don't have any information to determine a 'margin of error'.  Without any other performance data I would say 82% is 3% more than 79% which is enough to be the winner.  With more detailed information from Siddarth we could make a better analysis.  Accuracy is not a lot to make a determination especially when the application is not known.  I have seen high accuracy classifiers get beaten by much lower accuracy classifiers.  
  
  ---
  
  