Linear model coefficients into prediction confidence
The question might seem weird, but. I rare use linear models, but should use more!
Is there any obvious way to build an equation from linear model coefficients that would derive binominal label prediction confidence?
I am applying GLM to the dataset which contains polynominal attributes which were derived from discretizing numericals by enthropy to get ranges. Also there is a binominal label. If for example I initially had one variable named 'total_changes', at the end I have this kind of attributes and their coefficients:
So this should be interpreted that total_changes between 13 and 14 adds to the confidence, while over 14 negatively impacts it, while less than 13 has no effect on it. Same with other variables.
So, question is, is it possible to make an equation from this coefficients, which, given the ranges of variables, would calculate the confidence between 0 and 1? Or maybe any other way to make a meaningful equation which can be applied to new unseen data?