I am trying to predict the probability of occurrence of a low event rate outcome (~2% readmission risk after hospital discharge in the population of interest).
With the available limited predictors, I am not expecting accurate classification -- I would be satisfied with a prediction of the probability of readmission (e.g., if the predicted risk was 6%, simply knowing that there was a 3-fold higher risk of readmission would be helpful). There is no specific "threshold" risk that would be meaningful, so approaches of weighting / using ROC curves for sensitivity/specificity tradeoffs would not be helpful.
Logistic regression was my obvious choice, but I am wondering if there are other classifiers that are well-suited to reporting percent probabilities of classification outcome.
There seem to be multiple related questions re: class imbalance, but most seem to focus on feature selection, and here, weighting, performance metrics, and still focus on pure "classification" as the output, rather than a continuous rather than discrete output of relative / percent risk.
My preference would be solutions that are implemented in R.
Apologies for such a basic question -- I am a clinician with no formal training in data science.
Edited to add: maybe some more searching would have been wise. For Random Forests, is this giving me what I'm asking for?
predict(model_rf, test_cases, type = "prob")
Do other classification models typically offer this option for output?