nemshan/naive-bayes-algorithm-from-scratch

replicating Naive Bayes algorithms from scratch that # Restrict to only categorical features # Give an error message if continuous features are provided # Function must have at least three parameters: Train, Test, Classification variable # Output produces predicted probability and classification #Use Titanic data to predict survival.

naive-bayes-algorithm-from-scratch's Issues

can you please clarify , what you mean under feature distributions

can you please clarify , what you mean under feature distributions

for example
discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.
https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.CategoricalNB.html#sklearn.naive_bayes.CategoricalNB

seems to be you mean that distributions are estimated from data?
for given categorical feature it is probabilities and conditional probability (values and target) from calculated from data?

as mentioned in
https://datascience.stackexchange.com/questions/58720/naive-bayes-for-categorical-features-non-binary
Some people recommend using MultinomialNB which according to me doesn't make sense because it considers feature values to be frequency counts