How do ensemble methods like Random Forest and Gradient Boosting help in improving the model's performance?
- By focusing on one strong model
- By increasing overfitting
- By leveraging multiple models to achieve better accuracy and robustness
- By reducing computational complexity
Ensemble methods like Random Forest and Gradient Boosting combine the strengths of multiple models to achieve better accuracy and robustness. By leveraging a diverse set of models, they often outperform single models, especially on complex tasks, and reduce the risks of overfitting.
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