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Answer: Complex base models increase model complexity and reduce performance in all ensemble methods.
Complex base models can increase model complexity and potentially reduce performance in ensemble methods. In bagging, complex models may lead to overfitting and increased variance. In boosting, complex models may focus too much on correcting errors, leading to overfitting and reduced bias. In stacking, complex models may increase the complexity of the meta-model, potentially reducing overall performance. Therefore, it is important to balance model complexity with the goals of the ensemble method.
Author: LeetQuiz Editorial Team
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Discuss the impact of model complexity on ensemble learning. How does the complexity of base models affect the performance of bagging, boosting, and stacking?
A
Complex base models improve performance in bagging by reducing variance, in boosting by reducing bias, and in stacking by combining diverse predictions.
B
Complex base models increase model complexity and reduce performance in all ensemble methods.
C
Complex base models are not relevant for ensemble methods. All methods rely on simple, low-complexity models.
D
Complex base models are only relevant for stacking. Bagging and boosting do not benefit from complex base models.
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