Are there any specific challenges related to depth in online settings? Explain how max depth relates to the idea of Occam's razor in model selection.
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what is max depth in decision tree
- How to choose a depth that balances these competing objectives? Explain how max depth relates to the concept of causality in machine learning, particularly when using decision trees to infer causal relationships from observational data.
- Does the optimal max depth change after performing feature selection?
- How does max depth influence the bias of predictions made by a decision tree? Can we use max depth to control the degree of local fitting in a decision tree?
- 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)?
- How can limiting depth prevent the model from fitting to local noise? Explain how max depth interacts with the presence of missing values in the dataset when training a decision tree.
- Are there any special considerations for setting max depth in the presence of missing data? Describe the effect of max depth on the runtime performance of a decision tree during prediction.
- Do deeper trees require more data to avoid overfitting?
- How to choose a depth that optimizes the most relevant metric for the problem? Discuss the potential for max depth to influence the stability of feature importance rankings derived from a decision tree when the model is retrained on slightly different data subsets.
- 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?
- How does max depth relate to overfitting?
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