Are validation sets necessary for Random Forest Classifier?
You can use Out-of-bag error as you validation error, if you are short of data.

As you may know, Random Forest fits multiple decision trees, and for each tree it only fits on a subset of data. So data that hasn't been used for fitting a given tree is called Out of Bag data, and it could be used as your validation set 1

Sklearn in Python has a hyperparameter of Out-of-bag error