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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.