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I'm using the following dataset with 2 columns (features) and 1 label to train a Gaussian Naive Bayes classifier. How would you determine (using a stastiscal normality test) whether the data is normally distributed?

weather=['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny', 'Rainy','Sunny','Overcast','Overcast','Rainy']

temp=['Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild']

play=['No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No']

Encoded:

weather: [2 2 0 1 1 1 0 2 2 1 2 0 0 1]

Temp: [1 1 1 2 0 0 0 2 0 2 2 2 1 2]

Play: [0 0 1 1 1 0 1 0 1 1 1 1 1 0]

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  • $\begingroup$ Categorical data can't be normally distributed. Unless this question is something like a typo, I think you need to go back and take a basic statistics course (or several) before you start doing things like "Gaussian naive Bayes classifiers". I don't mean that as a put-down or anything -- you need the foundations before you can build a skyscraper. $\endgroup$
    – Peter Flom
    Commented Oct 13, 2019 at 11:18

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None of your variables can possibly be normally distributed. They are factors (categorical variables), some of which are ordered.

It would be pointless to test them for normality.

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