I fit the random forest to my dataset with a binary target class. I reset the probabilistic cutoff to a much lower value rather than the default 0.5 according to the ROC curve. Then I can improve the sensitivity (recall) but meanwhile sacrificed the precision.
Just wanna confirm that the default 0.5 is not much meaningful and a practical probabilistic cutoff was often derived from ROC curve in practice. Am I on the right track on the application of random forest and other tree based models.