Is the probabilistic cutoff in random forest flexible? 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.
Yes, you are exactly right. 0.5 is just a heuristic, ROC curve and precision-recall curve give a much better idea of what the cut off should be. You can then use predict_proba, extract the probabilities and do the classification based on the cut-off you have inferred from ROC curve and.  precision-recall curve