preprocessing data before GaussianNB for categorical for numerical features
The 'standard Gaussian'- which is the basic/hypothetical Gaussian distribution - has its mean at 0 and its variance at 1. For example people's height around the world has mean of 175cm and variance of 10cm, yet it is still Gaussian (bell shaped).
I'm using GaussianNB for classification problem
all the features are numerical and they represent marks about something  (every mark is between 1 and 8 and they are results to exams)

The algorithm seems to work enough well in my case: I got 0,85 as an accuracy score.
But I read that Gaussian should have mean 0 and variance 1 but they don't.
Should I use a scaler such as Standard Scaler before?
Is it compulsory in my case?
I tried it but performances didn't go up