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Related Questions
- What are ensemble methods, and how are they used for oversampling minority classes in machine learning?
- Can you explain the different types of ensemble methods, such as bagging and boosting, and their applications?
- How do ensemble methods like AdaBoost and Random Forest address overfitting in minority class classification problems?
- What is the key difference between oversampling minority classes using simple repetition versus using ensemble methods like bagging or boosting?
- Can you discuss the trade-offs between model complexity and overfitting when using ensemble methods for minority class oversampling?
- How can ensemble methods be used in conjunction with other techniques, such as SMOTE and undersampling, to further improve minority class classification accuracy?
- What are some common challenges or limitations associated with using ensemble methods for minority class oversampling, and how can they be addressed?
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