I have been looking into interpreting feature coefficient in a bounded capacity (0,1) as probability scores. So for example, if one has the following linear model for classification(Logistic regression),then inverse logit should return one linear probability scores right as it is the inverse logs of odd ratio?y<- c(1:3, 7, 6)x1<- 1:5x2<- c(20:25)temp<-coef(lm(y ~ x1 + x2))library('arm')invlogit(temp[1]+temp[2]) However, in-case of linear regression for continuous value prediction, coeff1/sum(coeff)should return one probability scores right ? Did I miss something ?
Converting feature coefficients to probabilities scores for linear models
python;r;linear regression