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Decision Tree Pruning

Pruning is a technique in decision tree that reduces the size of trees by removing sections of the tree that are non-critical and redundant to classify instances.

Pruned trees tend to be smaller and less complex. They are usually faster and better in classifying independent test data correctly.

There are two approaches to carry out tree pruning. They are prepruning and postpruning.

The prepruning apporoach is used at the early constrcution stage of decison tree. Hence further split of the tree is stopped at the given node, which become leaf node of the tree. This approach would overcome the overfitting issue.

The postpruning approach is the one in which subtrees are removed from the fully grown tree.

The cost complexity algorithm of CART and pessimistic pruning of C4.5 are example of the postpruning approach.

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