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Related Questions
- What are the key differences between bagging, boosting, and stacking in ensemble methods, and how do they address overfitting in multi-task learning?
- Can you explain how bagging reduces overfitting in multi-task learning, and provide examples of its application in real-world scenarios?
- How does boosting, specifically AdaBoost, handle overfitting in multi-task learning, and what are its strengths and weaknesses compared to bagging?
- In what ways does stacking, also known as hybrid ensemble learning, mitigate overfitting in multi-task learning, and how does it combine the predictions of different models?
- How do the hyperparameters of ensemble methods, such as the number of iterations and learning rates, impact the mitigation of overfitting in multi-task learning?
- Can you discuss the relationship between ensemble methods and regularization techniques, such as L1 and L2 regularization, in mitigating overfitting in multi-task learning?
- How do ensemble methods handle class imbalance and outliers in multi-task learning, and what techniques can be used to address these issues and reduce overfitting?
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