Answers.Body: <p>It would be hard to tell if you don't provide what kind of data/problem you are working on, but LDA works well when data that are grouped in gaussian blobs surrounding centroids while vanilla SVM works well when the data is almost linearly separable and naive bayes works well when your features are relatively independent of each other.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-10-24T18:43:54.140Z
Answers.LastActivityDate: 2018-10-24T18:43:54.140Z
Answers.UserDisplayName: Kevin He
Answers.Body: <p>If I had to guess(and it is nothing more), I would say it has quite a bit to do with the problem itself and the architectures involved. Simply, the problem is less suited to a bayesian approach(Highly dependent features, linear distribution). </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-06-25T14:42:57.533Z
Answers.LastActivityDate: 2018-06-25T14:54:02.697Z
Answers.LastEditDate: 2018-06-25T14:54:02.697Z
Answers.UserDisplayName: hisairnessag3
Body: <p>In a final project in diagnosing Attention deficit hyperactivity disorder (ADHD) using Machine Learning, we obtained parameters from real patients. We used this data and got much higher success rates with LDA than with SVM and Naive Bayes. We had only 100 examples in our training set. We are wondering why LDA specifically succeeded much more than the others?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-24T12:38:10.087Z
Comments.Text: By LDA, you mean "linear discriminant analysis", right? Feel free to edit this post to add the tag [tag:linear-discriminant-analysis] if that's the case.
Comments.UserDisplayName: nbro
Tags: <machine-learning><comparison><support-vector-machine><naive-bayes>
Title: Why would LDA have performed much better than SVM and Naive Bayes in diagnosing ADHD?
UserDisplayName: roi mal
ThreadText: # Why would LDA have performed much better than SVM and Naive Bayes in diagnosing ADHD?
  
  (Asked by: username_0 on 2018-06-25)
  
  In a final project in diagnosing Attention deficit hyperactivity disorder (ADHD) using Machine Learning, we obtained parameters from real patients. We used this data and got much higher success rates with LDA than with SVM and Naive Bayes. We had only 100 examples in our training set. We are wondering why LDA specifically succeeded much more than the others?
  
  **Tags:** machine-learning, comparison, support-vector-machine, naive-bayes
  
  > **Comments:**
  > **username_1**: By LDA, you mean "linear discriminant analysis", right? Feel free to edit this post to add the tag [tag:linear-discriminant-analysis] if that's the case.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2018-10-24. Score: 2
  
  It would be hard to tell if you don't provide what kind of data/problem you are working on, but LDA works well when data that are grouped in gaussian blobs surrounding centroids while vanilla SVM works well when the data is almost linearly separable and naive bayes works well when your features are relatively independent of each other.
  
  
  ---
  
  ### Answer by username_3 on 2018-06-25. Score: 0
  
  If I had to guess(and it is nothing more), I would say it has quite a bit to do with the problem itself and the architectures involved. Simply, the problem is less suited to a bayesian approach(Highly dependent features, linear distribution).
  
  
  ---
  
  