Does the optimal max depth depend on the splitting criterion?
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what is max depth in decision tree
- What are some signs that the chosen depth is too small or too large?
- What factors might influence the choice of depth in transfer learning scenarios? Discuss the role of max depth in ensuring the fairness and preventing discriminatory outcomes when using decision trees in sensitive applications (e.g., credit scoring, hiring).
- Are there any depth-specific strategies for imbalanced classification? Explain the relationship between max depth and the ability of a decision tree to extrapolate in regression tasks.
- How can we make the decision paths in deeper trees more accessible? How does max depth interact with the presence of correlated features in the dataset?
- How to select a depth that promotes robust generalization? Explain how max depth relates to the computational trade-offs in implementing different splitting criteria for decision trees.
- What analogies can be used to understand the concept of max depth? How does max depth affect the ability of a decision tree to capture complex relationships in the data?
- How can limiting depth improve stability? Discuss the use of max depth in tree-based models for time series forecasting.
- How to balance exploration and preventing spurious findings? Explain how max depth interacts with the process of pruning decision trees after they have been fully grown (post-pruning).
- Can we achieve a good balance between interpretability and predictive performance by carefully choosing the max depth?
- What are the implications of a very small max depth? What are the implications of a very large max depth?
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