A form of post-pruning

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Multiple Choice

A form of post-pruning

Explanation:
In decision tree learning, pruning methods aim to reduce overfitting by simplifying the tree after it has been grown. The idea behind post-pruning is to make pruning decisions only after the full tree exists, using data that wasn’t used to build the tree to judge generalization. Reduced error pruning is a concrete post-pruning method: after the tree is built, you examine non-leaf nodes and test whether replacing the entire subtree under a node with a single leaf improves or at least does not worsen accuracy on a separate pruning data set (or via cross-validation). If pruning that subtree does not increase error, you keep the prune. This bottom-up pruning continues until no further pruning reduces error. That makes it a clear example of post-pruning. Pre-pruning, by contrast, stops growth early during tree construction, which is a different approach. The general term pruning can refer to either, but reduced error pruning specifically fits the post-pruning category.

In decision tree learning, pruning methods aim to reduce overfitting by simplifying the tree after it has been grown. The idea behind post-pruning is to make pruning decisions only after the full tree exists, using data that wasn’t used to build the tree to judge generalization.

Reduced error pruning is a concrete post-pruning method: after the tree is built, you examine non-leaf nodes and test whether replacing the entire subtree under a node with a single leaf improves or at least does not worsen accuracy on a separate pruning data set (or via cross-validation). If pruning that subtree does not increase error, you keep the prune. This bottom-up pruning continues until no further pruning reduces error. That makes it a clear example of post-pruning.

Pre-pruning, by contrast, stops growth early during tree construction, which is a different approach. The general term pruning can refer to either, but reduced error pruning specifically fits the post-pruning category.

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