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The answer to this question very much depends on the application and the specific classification that's being done. SVM methods are, in general, simpler and less computationally expensive. KNN can produce great results, but is prone to over-fitting because of the highly non-linear nature. Additionally, naive (and exact) KNN is very expensive. This can be leveraged, though, by using approximative algorithms.

In general, from my experience, SVM tends to be universally applicable whereas KNN is not suitable for some applications. Usually, SVM ends up in the top 3 or 5 classifiers for a given problem. SVM may not always be the best, but there's a good chance it's close to the best. But, it's generally easier to deal with multiple-class problems with KNN than SVM.

In summary, it depends on what 'better' means and what scenario you're working with, but if it's a 2-class problem I'd say SVM....

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Q: Which is better either SVM or Knn classification?
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