I have a continuous target column that consists of IQ scores for kids of age 10. My main purpose is to forecast the probability that a kid is genius given 3 covariates. If a kid has IQ over 160 she/he will be labelled 1(genius) if not 0.I am told rather than using logistic regression and converting my continuous target column to a categorical one, I can also use a continuous distribution to fit the data and then compute probability from the distribution.
So I fit a Glm Gamma model to the data and now I have a model that I can get forecasts on IQ score given a test data. From this point on how can I compute the probability that a kid is genius? I have the parameters from the summary of the model. I thought I could use R's dgamma(). But how will I set threshold as 160 and given my test data how will I compute probabilities from independent variables?