How to monitor and mitigate bias related to tree structure? Explain how max depth relates to the concept of "black box" versus "white box" models in machine learning.
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what is max depth in decision tree
- Can we use max depth as a hyperparameter to tune our decision tree model? What are some common search algorithms used to find the optimal max depth (e.g., grid search, random search)?
- How does limiting depth affect the granularity of the decisions? Discuss the impact of max depth on the calibration of probability estimates produced by a classification tree.
- Illustrate with examples of shallow versus deep trees and their resulting boundaries.
- 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?
- What are some practical strategies for finding a good balance? Describe the relationship between max depth and the complexity of the decision boundaries learned by a classification tree.
- How does max depth relate to the complexity of the rules learned by a decision tree? Can we extract human-readable rules from a decision tree with a limited max depth?
- What are the implications for the generalizability of these rules? How does max depth affect the ability of a decision tree to capture non-linear relationships in the data?
- 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?
- Could limiting depth prevent the model from uncovering complex but genuine relationships?
- Why is simplicity often desirable in machine learning models? How does max depth affect the robustness of a decision tree to noisy data or outliers in the training set?
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