In this blog they’ve mentioned that we should use standardization only when data is normally distributed
https://towardsdatascience.com/why-not-mse-as-a-loss-function-for-logistic-regression-589816b5e03c. I think it is wrong, as standardization actually changes the data distribution to normal. And as standardization changes the data distribution (to normal) unlike normalization so we should use standardization when the data distribution isn’t important for training. Which one seems correct?
Pls help! Thanks in advance.