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A fully grown Decision Tree (no pruning, no depth limit) tends to overfit. Which explanation best captures why?
It creates leaf nodes that perfectly memorise training data, including noise
It always produces linear decision boundaries
It requires normalised features to function correctly
It uses too few features at each split
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