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Answer: Stacking, because it combines predictions from multiple models and can capture complex relationships.
Stacking would be the most appropriate ensemble method in this scenario because it combines predictions from multiple models, which can capture complex relationships and improve overall performance. Stacking allows for the integration of diverse models like a decision tree, logistic regression, and a neural network, potentially leading to better predictive accuracy compared to using a single model.
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Consider a scenario where you have to predict the likelihood of a customer churning. You are given three different models: a decision tree, a logistic regression, and a neural network. Which ensemble method would you use to combine these models, and why?
A
Bagging, because it reduces variance and improves stability.
B
Boosting, because it focuses on improving the performance of weak learners.
C
Stacking, because it combines predictions from multiple models and can capture complex relationships.
D
None of the above, because ensemble methods are not suitable for this task.
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