Basically, sklearn
has naive bayes with Gaussian kernel which can class numeric variables.
However, how to deal with data set containing numeric variables and category variables together.
For example, give a dataset below, how use sklearn train mixed data type together without discreting numeric variables?
+-------+--------+-----+-----------------+
| Index | Gender | Age | Product_Reviews |
+-------+--------+-----+-----------------+
| A | Female | 20 | Good |
| B | Male | 21 | Bad |
| C | Female | 25 | Bad |
+-------+--------+-----+-----------------+
I mean, for Bayes classification, P(A|B)= P(B|A)*P(A)/P(B).
For category variables, P(B|A) is easy to count out, but for numeric variables, it should follows Gaussian distribution. And assume we have got P(B|A) with Gaussian distribution.
Is there any package can directly work with these together?
Please be note: this question is not duplicated with How can I use sklearn.naive_bayes with (multiple) categorical features? and Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn
Because this question is not wanna do a naive bayes with dummy variables(1st question) and also do not wanna do a model ensemble(2nd question solution2).
The mathematic algorithm is here https://tom.host.cs.st-andrews.ac.uk/ID5059/L15-HsuPaper.pdf , which calculates conditional probabilities with Gaussian distribution instead of counting number with numeric variables. And make classification with all conditional probabilities including category variables(by counting number) and numeric variables(Gaussian distribution)