Threshold Value for Random Forest Classifier Is there any way to set a threshold value that can be set for voting results for Random Forest Classifier in sklearn package in order to prevent any misclassification for incoming test data.
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/