Today’s Lecture:
- I’ve experienced When building decision trees, a technique called pre-pruning, sometimes called early halting, involves applying limitations to stop the tree’s growth before it reaches full maturity.
- This is deciding whether to stop splitting nodes throughout the building of the tree based on predetermined standards, including imposing a maximum depth limitation, establishing a minimum sample requirement for node splitting, or necessitating a minimum quantity of samples in a leaf node.
- Pre-pruning is primarily done to keep the tree from growing too complicated and overfitting the training set, which will eventually improve the model’s capacity to generalize to new, unobserved data.
- However, in order to enhance overall model performance, post-pruning, also known as simple pruning, is extending a decision tree to its full depth before carefully deleting any branches or nodes.
- After allowing the original tree to grow unrestrictedly, branches that do not substantially improve prediction accuracy are trimmed in accordance with predetermined standards.
- The trade-off between the tree’s complexity and its fit to the training set is taken into account by common techniques like cost-complexity pruning. Post-pruning is primarily used to reduce overfitting and simplify the decision tree, especially in cases where the tree structure retains noise or training data-specific patterns that may not translate well to fresh data.
- In conclusion, post-pruning removes portions of the tree whereas pre-pruning regulates tree growth throughout building.