i've been tying to wrap my head around logistic regression, the logit transform, and the sigmoid function.
from what i understand, in practice all we want to do is maximize the following log likelihood (to get the sigmoid parameters that best fit the data): which involves some form of gradient descent to find the weights that maximize the above expression.
what i'm a bit hung up on is i don't see the logit transform anywhere here, ie, it looks like it isn't even used in the modeling process.
so is the logit transform actually ever computed in logistic regression? or is it just used to set up the problem, as a rationale for maximizing the log likelihood (rather than computing linear least squares)?
its a bit similar to the question below, i understand that the logit is the inverse of the sigmoid, and in that sense it is "used" in the modeling process, but i don't see the log odds anywhere in the modeling process. thanks,