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Related Questions
- What are the primary differences between bagging and boosting in ensemble methods?
- How does bagging reduce overfitting in machine learning models?
- Can you explain the concept of cognitive bias in the context of machine learning and how ensemble methods address it?
- What are some common techniques used in bagging to reduce overfitting, such as random forest and gradient boosting?
- How does the concept of 'ensemble' help in reducing overfitting and improving model generalizability?
- What are some real-world applications of ensemble methods in reducing overfitting and cognitive biases?
- Can you provide examples of how boosting algorithms like AdaBoost and XGBoost help in reducing overfitting and improving model performance?
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