Decision Tree Prunning 편집하기 (부분)

==Importance of Pruning==
Pruning plays a critical role in decision tree models by addressing overfitting and enhancing interpretability:
*'''Prevents Overfitting''': By removing unnecessary branches, pruning helps reduce the risk of overfitting, allowing the model to generalize better to new data.
*'''Improves Model Simplicity''': Pruned trees are smaller and less complex, making them easier to interpret and more efficient in computation.
*'''Enhances Model Stability''': Pruning can create more stable models by reducing sensitivity to noise or small variations in the training data.