How is the depth of the tree controlled and updated in such algorithms?
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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?
- How is the concept of max depth evolving in the context of more advanced tree-based models? What is the fundamental purpose of limiting the max depth of a decision tree?
- Are there any theoretical guidelines for choosing a reasonable max depth based on the data characteristics? How does the number of classes in a classification problem affect the optimal max depth?
- How to interpret visualizations from trees with different depths? Discuss the potential for max depth to influence the fairness and accuracy of decision trees used in algorithmic fairness applications, where the goal is to build models that are both predictive and equitable across different demographic groups.
- Can a shallower decision tree effectively mimic the behavior of a deeper model or another type of complex model?
- When might it be acceptable to use an unlimited max depth? How does the type of problem (classification vs. regression) affect the choice of max depth?
- Does the optimal depth vary depending on the metric of interest?
- In what situations might a very shallow decision tree be preferred?
- How does max depth affect the memory usage of a decision tree model?
- Are there any techniques to improve the interpretability of deeper decision trees? How does max depth affect the sensitivity of a decision tree to small perturbations in the training data?
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