I am currently working on a supervised learning project with sklearn. According to my experiments I observe DecisionTreeClassifier(DTC) performs better than RandomForestClassifier(RFC), both in term of training and testing error (for several test sets actually). Given the fact that RFC is an ensemble of DTCs, would it be acceptable to report that DTC is a better method for a given dataset over RFC? Or is it always that case that you can get a better RFC, but I have not tuned it enough to outperform DTC?
Example of tunegrids I 've tried with grid search cv, where DTC outperforms RFC when the best found specifications are tested on independent test sets:
In short, is RFC always a better choice (or at least as good as) the DTC?