Problem with naive bayes(not the kernel ones)

johnny5550822johnny5550822 Member Posts: 12 Contributor I
edited November 2018 in Help
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)??

Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 2,078  RM Data Scientist
    The naive bayes in Rapidminer is assuming a normal distribution for numerical features
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • johnny5550822johnny5550822 Member Posts: 12 Contributor I
    Great, this is for the non-kernel one naive bayes, right? For the kernel ones, it will determine it, right?
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