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Problem with naive bayes(not the kernel ones)

I have a set of continuous features. I suppose to use the kernel naive bayes because the features are continuous. However, I use the non-kernel naive bayes, and still give me some predict result. How does the non-kernel naive bayes handle the continuous features? (Does it assume each feature to have a normal distribution)??

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MartinLiebig

The naive bayes in Rapidminer is assuming a normal distribution for numerical features

johnny5550822

Great, this is for the non-kernel one naive bayes, right? For the kernel ones, it will determine it, right?

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