What is the primary benefit of using ensemble methods in machine learning?
- Improved generalization and robustness
- Faster model training
- Simplicity in model creation
- Reduced need for data preprocessing
Ensemble methods in machine learning, such as bagging and boosting, aim to improve the generalization and robustness of models. They combine multiple models to reduce overfitting and improve predictive performance, making them a valuable tool for creating more accurate and reliable machine learning models.
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