There are three nice Bayes classifier techniques: Bernoulli, Multinomial, and Gaussian
If we have a dataset whose samples have continuous-valued features, then Gaussian Bayes Classifier is used.
In this point, I am a little bit confused. Some people told me that I do not check the features are normally distributed in the classes to be able to use Gaussian Bayes Classifier. I can directly use it.
I think that this is not true, since when we build classifier discriminant functions, we benefit from Naive Bayes probability theorem, and we assume that probability functions are probability density function of normal distribution. If features of our samples are not normally distributed, we cannot use gaussian density function directly.
Which one is true ?
Do we have to check if the samples are normally distributed or not before using Gaussian Bayes Classifier ?