Decision Tree Prunning 편집하기 (부분)

==How Pruning Works==
Pruning generally involves evaluating each node and determining whether it adds significant value to the model. Nodes that have minimal impact on prediction accuracy or generalization are removed to simplify the model.

1. '''Grow the Tree''': In post-pruning, the tree is allowed to grow to its maximum depth, capturing all potential splits. 

2. '''Evaluate Nodes''': Each node is evaluated to determine whether removing it would significantly impact the model’s performance. 

3. '''Remove Nodes''': Nodes that do not contribute to improved accuracy or increase complexity without significant benefit are removed. 

4. '''Validate and Finalize the Model''': Pruned models are evaluated on a validation set to ensure that pruning has improved generalization.