I've run random forest on my dataset (imbalanced, binary target class) and used cross validation to tune the parameter and use recursive feature elimination with cross-validation to get the subset of features.
Then I can present the optimal parameter setting as well as the set of features for model fitting.
But how about the probabilistic threshold?
How can I conclude a threshold as the important part of the final model?
Also using CV?
If this threshold is not stable among folds of CV, what to do next?