What are the different pruning methods in machine learning?

What are the different pruning methods in machine learning?

There are several pruning methods, including pre-pruning, post-pruning, and cost-complexity pruning. Would you like to know more about each?

Yes, can you explain pre-pruning and post-pruning?

Pre-pruning stops the tree from growing when a certain condition is met, while post-pruning removes branches after the tree is fully grown. Both aim to reduce overfitting.

What are the advantages of using cost-complexity pruning?

Cost-complexity pruning balances the tree's complexity and its accuracy, helping to find an optimal size that generalizes well to unseen data.

Are there specific scenarios where one method is preferred over the others?

Yes, pre-pruning is often used when computational resources are limited, while post-pruning is preferred when you want to fully explore the data before simplifying the model.