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Related Questions
- What are the key techniques used in ensemble methods to prevent overfitting in machine learning models?
- Can you explain the concept of bagging and its role in ensemble methods?
- How does stacking ensemble methods, such as stacking or blending, improve the generalization of machine learning models?
- What is the difference between bagging and boosting, and how do they contribute to reducing overfitting?
- How do ensemble methods handle class imbalance and feature selection in reducing overfitting?
- Can you discuss the trade-off between increasing the number of ensemble members and reducing overfitting?
- What are some common pitfalls to avoid when implementing ensemble methods to reduce overfitting in machine learning models?
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