Imagine you have a Decision Tree that is overfitting the training data. How would you apply pruning to address this issue?
- Ignore irrelevant features
- Increase tree depth
- Remove irrelevant branches
- Use the entire dataset for training
Pruning involves removing branches that have little predictive power, reducing the model's complexity and sensitivity to noise in the training data. By removing irrelevant branches, the overfitting issue can be mitigated, and the model may generalize better to unseen data.
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