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1 . Discuss why heterogeneous mixtures of experts are expected to perform better than homogeneous mixtures of experts. ( 4 ) 2 . Why is
Discuss why heterogeneous mixtures of experts are expected to perform better than homogeneous mixtures of experts. Why is weighted voting used in AdaBoost, instead of allowing each expert in the boosting pipeline to have the same weight? Are boosting algorithms robust to outliers? Motivate your answer. Consider an ensemble of kNN algorithms. Why does it make sense to include kNN experts with dierent values for k How do random forests ensure that the dierent decision trees in the forest exhibit dierent behavior? Are regression random forests sensitive to outliers? Motivate your answer. Explain how bagging ensemble learning models address the biasvariance dilemma.
Discuss why heterogeneous mixtures of experts are expected to perform better than homogeneous mixtures
of experts.
Why is weighted voting used in AdaBoost, instead of allowing each expert in the boosting pipeline to have
the same weight?
Are boosting algorithms robust to outliers? Motivate your answer.
Consider an ensemble of kNN algorithms. Why does it make sense to include kNN experts with dierent
values for k
How do random forests ensure that the dierent decision trees in the forest exhibit dierent behavior?
Are regression random forests sensitive to outliers? Motivate your answer.
Explain how bagging ensemble learning models address the biasvariance dilemma.
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