Sometimes you do not have firm Y/N labels, but e.g. 80% probability of Y as a label. E.g. this happens, if you train a model on a small amount of labelled data, predict for a large amount of unlabelled data and then want to use the predictions as soft-labels.
If I want to use soft labels, then a lot of software for elastic net logistic regression (e.g. glmnet in R) does not allow labels in (0,1). Ideas for software that can handle such soft labels directly is also welcome. One potential solution that occurred to me is to use observation weights - e.g. instead of a 0.8 soft label use create two observations with weights 0.2 and 0.8 and outcome 0 and 1, respectively.
Am I assuming correctly that that would work fine?
Any other special considerations (I could only think of perhaps making sure that such pairs of observations always end top in the same fold when doing cross-validation and to certainly not split such a pair across training and test data)?