I am interested in finding the OOB score for random forest using sklearn, when it is used for a binary classification task, and there are unbalanced samples. What does the oob decision function mean in random forest, and how get class predictions from it?
I read RandomForestClassifier OOB scoring method but am still not clear. Does the oob decision function provide class probabilities, and if so, do I get the class predictions by taking whichever number is higher (e.g. by doing something like pred_train = np.argmax(forest.oob_decision_function_,axis=1))?
Since my classes are unbalanced, would it be correct to say I can't used sklearn's default OOB score here, and I should do the above to get some kind of F1 score from the OOB predictions, to get a better estimate of my random forest's error?