I'm building a logistic regression model in R using
glm(y ~ x1 + x2 + x3 + x4, data = train.set, family = binomial(link = 'logit')). Among 4 predictors
x1, x2, x3, x4, they all are categorical. However
x1, x2, x3 are on a scale of 0 to 10, and
x4 is binary (0 or 1).
My question is how should i properly pre-process
x4? I'm asking because I know it is a really important variables in terms of prediction, but it's showing a pretty low importance in the
summary() due to the fact that it's on a different scale as the rest of predictors.
Could someone who has experienced similar situation share your approach? Thanks a lot!
edit: all predictors
x1, x2, x3, x4 are factors. I do understand that as long as they are all factors, it shouldn't matter what values each predictors they have. but we expect
x4 to be a more important predictors, but
varImp shows the opposite.