I'm working with a supervised learning problem and trying to predict a binary label and using a Random Forest to do so.
I'm trying to tune my hyper-parameters to give me a best model based on my data.
I can do this with
GridSearchCV(), but is this correct to do with a random forest?
If I'm using
GridSearchCV(), the training set and testing set change with each fold. From my understanding we can we set
oob_true = True in
RandomForestClassifier(), we are already evaluating on the out-of-bag samples (so CV is kind of already built in RF).
What is the convention to hyper-parameter tune with Random Forest to get the best OOB score in sklearn? Can I just loop through a set of parameters and fit on the same training and testing set? Can I use
GridSearchCV(), or does that make no sense with RF?