Answers.Body: <p>Yes, you can. There are a lot of different techniques, usually called <a href="https://en.wikipedia.org/wiki/Ensemble_learning" rel="nofollow noreferrer">Ensemble Methods</a>. </p>
  
  <p>A better approach might be to use something like <a href="https://en.wikipedia.org/wiki/AdaBoost" rel="nofollow noreferrer">AdaBoost</a> along with a cheaper method like the decision trees you looked at. AdaBoost explicitly tries to train classifiers to correctly handle different parts of the data, rather than hoping that different methods turn out to do so by chance. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-08T15:51:27.610Z
Answers.LastActivityDate: 2018-08-08T15:51:27.610Z
Answers.UserDisplayName: John Doucette
Body: <p>I have a dataset with 2,23,586 samples out of which i used 60% for training and 40% for testing. I used 5 classifiers individually, SVM, LR, decision tree, random forest and boosted decision trees. SVM and LR performed well with close to 0.9 accuracy and recall also 0.9 but tree based classifiers reported an accuracy of 0.6. After a careful observation, I found out that SVM and LR did not predict the labels of 20,357 samples identically. So Can I apply voting and resolve this conflict wrt prediction outcome? Can this conflict be due to an imbalanced dataset?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-01-13T14:09:23.053Z
Comments.Text: welcome to AI SE. Your question is ok, but the title could be more descriptive to your problem. Could you please clarify it a little bit?
Comments.UserDisplayName: mico
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-02-13T20:44:28.630Z
Comments.Text: How many of the 20357 cases that SVM and LR fails has been correctly classified by the remainder methods?
Comments.UserDisplayName: pasaba por aqui
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-06-19T07:56:57.243Z
Comments.Text: Your question leaves a lot of questions, but will try to assist in what l understood. when gathering your data please try by all means to make sure that there are equal samples per channels and monitor your model for label leaking
Comments.UserDisplayName: Simbarashe Timothy Motsi
Tags: <classification>
Title: Can I combine two classifiers that make different kinds of errors to get a better classifier?
UserDisplayName: Sudha
ThreadText: # Can I combine two classifiers that make different kinds of errors to get a better classifier?
  
  (Asked by: username_0 on 2018-01-13)
  
  I have a dataset with 2,23,586 samples out of which i used 60% for training and 40% for testing. I used 5 classifiers individually, SVM, LR, decision tree, random forest and boosted decision trees. SVM and LR performed well with close to 0.9 accuracy and recall also 0.9 but tree based classifiers reported an accuracy of 0.6. After a careful observation, I found out that SVM and LR did not predict the labels of 20,357 samples identically. So Can I apply voting and resolve this conflict wrt prediction outcome? Can this conflict be due to an imbalanced dataset?
  
  **Tags:** classification
  
  > **Comments:**
  > **username_1**: welcome to AI SE. Your question is ok, but the title could be more descriptive to your problem. Could you please clarify it a little bit?  
  > **username_2**: How many of the 20357 cases that SVM and LR fails has been correctly classified by the remainder methods?  
  > **username_3**: Your question leaves a lot of questions, but will try to assist in what l understood. when gathering your data please try by all means to make sure that there are equal samples per channels and monitor your model for label leaking  
  
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  ## 1 answers
  
  ### Answer by username_4 on 2018-08-08. Score: 1
  
  Yes, you can. There are a lot of different techniques, usually called [Ensemble Methods](https://en.wikipedia.org/wiki/Ensemble_learning).
  
  A better approach might be to use something like [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost) along with a cheaper method like the decision trees you looked at. AdaBoost explicitly tries to train classifiers to correctly handle different parts of the data, rather than hoping that different methods turn out to do so by chance.
  
  
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