



How does max depth relate to the minimum number of samples required to split a node?
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what is max depth in decision tree
- Are shallower trees more likely to generalize well to different settings?
- How can limiting depth improve stability? Discuss the use of max depth in tree-based models for time series forecasting.
- 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 max depth impact the computational cost of predicting with a decision tree? Is there a rule of thumb for choosing an initial value for max depth?
- Does the optimal max depth depend on the splitting criterion?
- Can deep trees effectively handle correlated features?
- How can cross-validation help determine the optimal max depth?
- Should we generally use deeper trees for larger datasets?
- Can an adaptively adjusted max depth help the model to remain relevant as the underlying data distribution changes? Explain how max depth relates to the communication of a decision tree model's logic to non-technical stakeholders.
- What are the risks of simply trying very many different max depth values?
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