I came across the assumptions of linear regression that said: -->The residuals should be normally distributed.
GLM(Generalized Linear model) assumes that target variable should follow one of the exponential family.
So does linear regression needs residuals as well as target variable to be distributed normally?
In the above mentioned, it is written -
There are three components to any GLM:
- Random Component – refers to the probability distribution of the response variable (Y); e.g. normal distribution for Y in the linear regression, or binomial distribution for Y in the binary logistic regression.
Moreover in the assumption section,
The dependent variable Yi does NOT need to be normally distributed, but it typically assumes a distribution from an exponential family (e.g. binomial, Poisson, multinomial, normal,...)
I am new to machine learning, forgive me if i'm asking stupid question.