I'm confused by the comments here, does "logit" here not mean "log odds" like it does in virtually every other context related to machine learning? Generally I

I'm confused by the comments here, does "logit" here not mean "log odds" like it does in virtually every other context related to machine learning? Generally I'm a huge fan of not getting too caught up in theory before diving into practice, but I'm seeing multiple responses to this comment without a single mention of "log odds". The logit function transforms probabilities into the log of the odds (ln P(X)/(1-P(X)), which is important because it makes probabilities linear, which they are not in their standard [0,1] form. It's the foundation of logistic regression, which is, despite much misinformation, quite literally linear regression with a transformed target. The logistic function is the inverse of the logit: it turns log odds values into probabilities once again. Logistic regression actually transforms the model not the target (most of the time) because the labels 0 and 1 are negative and positive infininity which can't be handled by linear regression (so we transform the model using the inverse instead). I don't think I can stress enough how important is its to really understand logistic regression (which is also the basic perceptron) before diving into neural networks (which are really just an extension of logistic regression).