logit is not a "probability function", quite the opposite. You can see this in the image in the link you posted (the x-axis is from 0-1, the y-axis is from -inf

logit is not a "probability function", quite the opposite. You can see this in the image in the link you posted (the x-axis is from 0-1, the y-axis is from -inf to inf). It transforms probabilities into log odds which is a linear space, and make combining probabilities much nicer. The inverse logit or logistic function takes log odds and transforms them back into probabilities. Most machine learning relies heavily on manipulating probabilities, but since probabilities are not linear, the logit/logistic transformations become essential to correctly modeling complex problems involving probabilities.