ROC curve for different hyperparameters of `RandomForestClassifier`? I'm currently trying to train a RandomForestClassifier on a dataset consisting of 5000 instances with 12 (now) encoded features and a binary target label. Through GridSearchCV I found out, that 

best_parameters = {
    'criterion':    'gini', 
    'max_depth':    12, 
    'max_features': 'log2', 
    'n_estimators': 300
}


works best out of

hyperparameters = {
    "n_estimators": [9,10,20,30,40,50,60,100,150,200,300,1000],
    "max_depth":    [3,6,9,12,20],
    "criterion":    ["gini", "entropy"],
    "max_features": ["log2", "auto"]
}


And gives a mean_test_score of 0.8546 which is quite good already, as I think.

Now I would like to get some kind of visual interpretation like a ROC curve for each parameter. But does it actually make sense in the case of a RandomForestClassifier to create a ROC curve for each hyperparameter? Or are there other ways to tune my classifier?
Seems like you already tuned your algorithm based on the mean_test_score. You also could tune the AUC, which is the area under the ROC curve. Tuning this can even provide better mean_test_score as I show in my blog post: http://philipppro.github.io/Tuning_random_forest/