Threshold Value for Random Forest Classifier
Yes and the most important parameter is the tree depth. It's a pre-pruning technique that allows to prevent overfitting. Specifically for sklearn is: 

estimator.tree_.max_depth


I suggest you to perform GridSearch on max_depth:

params = {'max_depth':[1,50]}
gs = GridSearchCV(DecisionTreeClassifier(), params)
gs.fit(X,y)


where X is you training set containing instances
and y are the labels.

There are packages that also support post-pruning like this one https://svaante.github.io/decision-tree-id3/