I have a question about ensemble learning methods.
When should ensemble learning be used and when is better performance than a single model guaranteed?
More specifically:
- Are there theoretical guarantees or conditions under which ensemble methods are provably better than individual base models?
- What are the practical indicators that suggest ensemble learning might improve performance?
- In which scenarios might ensemble methods fail to improve (or even worsen) results compared to a well-tuned single model?
I'm particularly interested in both the theoretical foundations and practical heuristics for deciding when to employ ensemble methods. Any references to relevant articles or theoretical findings would be helpful, as I have not been able to find any good sources in my research so far.
