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Related Questions
- Can you explain the concept of overfitting in machine learning models and why it occurs?
- How does bagging, a type of ensemble method, help to reduce overfitting in machine learning models?
- What is the key difference between bagging and boosting, and how do they address overfitting?
- Can you provide examples of scenarios where bagging or boosting is more suitable for addressing overfitting?
- How does the choice of ensemble method (bagging or boosting) affect the performance of the model on unseen data?
- Can you discuss the trade-offs between ensemble methods and traditional model selection methods in addressing overfitting?
- What are some common pitfalls to avoid when using ensemble methods to address overfitting, and how can they be mitigated?
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