Pruning a decision tree is a method employed to simplify its structure, preventing it from becoming overly complex and thus ensuring optimal performance on new, unseen data. The primary goal of pruning is to streamline the tree by eliminating unnecessary branches while preserving its predictive capabilities. Two main pruning approaches are utilized: pre-pruning and post-pruning.
Pre-pruning, also referred to as early stopping, involves imposing constraints during the tree-building process. This may include setting limits on the tree’s maximum depth, specifying the minimum number of samples needed to split a node, or establishing a threshold for the minimum number of samples permitted in a leaf node. These constraints serve as safeguards to prevent the tree from growing excessively intricate or becoming too tailored to the training data.
On the other hand, post-pruning, also known as cost-complexity pruning, follows a process where the full tree is initially constructed, and later, branches that contribute minimally to improving predictive performance are removed. The decision tree is allowed to grow without restrictions initially, and then nodes are pruned based on a cost-complexity measure considering both the accuracy of the tree and its size. Nodes that do not significantly improve accuracy are pruned, resulting in a simplified overall model.