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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?

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  • $\begingroup$ By LDA, you mean "linear discriminant analysis", right? Feel free to edit this post to add the tag linear-discriminant-analysis if that's the case. $\endgroup$
    – nbro
    Commented Jul 24, 2021 at 12:38

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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.

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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).

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