Are there any advantages to using a combination of methods? Explain how max depth can influence the interpretability of the decision paths in a tree.
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what is max depth in decision tree
- How does the effectiveness of max depth as a regularization technique change with the number of features? What is the relationship between max depth and the statistical power of a decision tree to detect real patterns in the data?
- How can visualizing the tree help in understanding the impact of max depth?
- How to provide explanations for predictions from deeper trees? Discuss the potential for max depth to influence the generalization ability of a decision tree to new, unseen domains or populations (out-of-distribution generalization).
- How does increasing depth allow for more complex and irregular boundaries? Describe how max depth influences the ability of a decision tree to capture interactions between different features.
- Is there a concept similar to max depth in other tree-based models like gradient boosting machines? How does the implementation of decision trees in different libraries (e.g., scikit-learn, R's rpart) handle max depth?
- Do deeper trees require more data to avoid overfitting?
- Should we generally use deeper trees for larger datasets?
- Does the optimal max depth differ depending on the metric being optimized (e.g., accuracy, precision, recall, F1-score, AUC)?
- How does increasing max depth allow a decision tree to model more complex interactions? What is the impact of max depth on the stability of feature importance estimates derived from a decision tree?
- Would the optimal max depth for a transferred tree be similar to the original tree?
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