In general, in a classification problem where the goal is to accurately predict out-of-sample class membership, when should I not to use an ensemble classifier?
This question is closely related to Why not always use ensemble learning?. That question asks why we don't use ensembles all the time. I want to know if there are cases in which ensembles are known to be worse (not just "not better and a waste of time") than a non-ensemble equivalent.
And by "ensemble classifier" I'm specifically referring to classifiers like AdaBoost and random forests, as opposed to, e.g., a roll-your-own boosted support vector machine.