Logistic regression fits a model that predicts a binary variable whilst performing a logit transformation of the linear combination (LC) of predictors: 1/1 + exp(-LC).
I have a working machine learning algorithm which does the same but fits a non linear combination of predictors by maximising the log likelihood. I would like to apply this algorithm to presence only data as suggested here:
Could I just use formula (4) (its log likelihood version)? How do I use the resulting fitted model to perform predictions if I do not know the value of z but just the predictors/co-variates? I also do not understand the difference between observed and full likelihood? Any clarification would be very much appreciated.