



How can we choose a max depth that balances performance across multiple metrics? What is the relationship between max depth and the complexity of the decision rules generated by the tree?
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what is max depth in decision tree
- Can a very shallow tree fail to capture important underlying relationships?
- Can deeper trees learn more complex and potentially discriminatory rules?
- How can limiting max depth improve the stability of the learned model? What is the relationship between max depth and the computational complexity of tree traversal during prediction?
- Can a very shallow tree miss important relationships?
- Are there any advantages to using a combination of methods? Explain how max depth can influence the interpretability of the decision paths in a tree.
- How does max depth relate to the concept of model capacity? Is it always better to have a more complex model (deeper tree) if we have enough data?
- How does the interpretability of a decision tree, influenced by max depth, relate to fairness and accountability?
- Are standard decision trees with depth limits well-suited for capturing temporal dependencies?
- How does max depth relate to the concept of Occam's razor in model selection? Is a simpler model (shallower tree) always better if it performs similarly to a more complex one?
- In ensemble methods like boosting, are the individual trees typically deep or shallow?
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