



Do shallower trees tend to produce more linear or simpler boundaries?
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what is max depth in decision tree
- How does limiting depth act as a constraint to prevent memorization?
- How does limiting the complexity of the tree through max depth improve generalization?
- Do deeper trees require more data to avoid overfitting?
- How sensitive is the performance of a decision tree to the choice of max depth?
- Are there any adaptations for time series data? Explain how max depth affects the granularity of the predictions made by a decision tree, particularly in regression tasks.
- How can cross-validation help determine the optimal max depth?
- How does max depth affect the likelihood of a decision tree overfitting to specific features? Can a decision tree with a small max depth still be useful?
- Are standard decision trees with a fixed max depth well-suited for time series analysis?
- How does the depth of the tree affect the agent's ability to learn and make optimal decisions over time? How does max depth interact with the handling of uncertainty in decision tree predictions, such as when probabilistic outputs are desired?
- What are some best practices for setting the max depth when using ensemble methods? How does max depth affect the runtime performance of a decision tree in real-time applications?
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