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Related Questions
- What is the purpose of regularization in machine learning and how does it impact decision tree regression?
- How do L1 and L2 regularization techniques differ in terms of their effect on model complexity and feature selection?
- Can you explain how L1 regularization (Lasso) reduces overfitting in decision tree regression, and what are its limitations?
- How does L2 regularization (Ridge) modify the decision tree regression algorithm to prevent overfitting, and what are its advantages?
- In what situations would L1 regularization be preferred over L2 regularization in decision tree regression, and vice versa?
- How do L1 and L2 regularization affect the interpretability of decision tree regression models, and are there any strategies to overcome this issue?
- Can you provide examples of real-world applications where L1 and L2 regularization have been used to improve the performance of decision tree regression models?
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