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In a final project in diagnosing ADHD using Machine Learning we obtained parameters from real patients. We used this data and got much higher success rates in LDA than in SVM and Naive Base, 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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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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