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Is there any threshold value that can be set for results for Random Forest Classifier in sklearn package in order yo prevent any misclassification for incoming test data.

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    $\begingroup$ Your question is too broad. In any case, I recommend you to check this link $\endgroup$ – Ripstein Apr 9 '18 at 14:20
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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/

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