Tausif Tech Hub

 

Pruning is a technique used in decision tree algorithms to reduce the size of the tree and prevent overfitting. It helps improve the model’s accuracy on unseen data by removing unnecessary branches that do not provide much predictive power. There are mainly two types of pruning:

1. Pre-pruning (Early Stopping):

   • In this technique, the tree is stopped from growing further during the training phase.

   • A condition is set (like minimum number of samples in a node or maximum depth), and if it is met, the node is not split further.

   • It reduces complexity and saves computation time.

   • However, it may lead to underfitting if the tree is stopped too early.

2. Post-pruning (Backward Pruning):

   • In this method, the tree is first allowed to grow completely and then unnecessary branches are removed.

   • Subtrees are replaced with leaf nodes if they do not significantly improve accuracy.

   • Techniques like cost complexity pruning (used in CART) are applied.

   • This method is generally more accurate than pre-pruning.

Conclusion:
Pruning helps improve the generalization ability of decision trees by reducing overfitting and simplifying the model.