

Are there any situations where a default max depth might be sufficient? How does max depth relate to the idea of model selection uncertainty?
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what is max depth in decision tree
- Explain how shallower trees tend to have higher bias and lower variance, while deeper trees tend to have lower bias and higher variance.
- How does the typical depth of a well-performing decision tree compare to the complexity of other models?
- Are standard decision trees with depth limits well-suited for capturing temporal dependencies?
- Can very deep decision trees encode biases present in the training data in complex ways? How does max depth relate to the idea of explainable AI (XAI)?
- Do shallower decision trees inherently provide better explanations?
- Are there any general guidelines for setting max depth differently for these two types of problems?
- Provide examples of scenarios where each might be appropriate or inappropriate.
- How does max depth interact with the size and complexity of the feature space?
- What are some signs that the chosen depth is too small or too large?
- How does the depth of the tree affect our ability to understand and trust the model's decisions?
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