What are the potential drawbacks or challenges when using ensemble methods like Random Forest and Gradient Boosting?
- Always leads to overfitting
- Always underperforms single models
- Can be computationally expensive and lack interpretability
- No potential drawbacks
Ensemble methods like Random Forest and Gradient Boosting can be computationally expensive due to the training of multiple models. Additionally, they may lack interpretability compared to simpler models, making them challenging to explain and understand.
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