Reduced Error Pruning

• Consider each node for pruning
• Pruning = removing the subtree at that node, make it a leaf and assign the most common class at that node
• A node is removed if the resulting tree performs no worse then the original on the validation set - removes coincidences and errors
• Nodes are removed iteratively choosing the node whose removal most increases the decision tree accuracy on the graph
• Pruning continues until further pruning is harmful
• uses training, validation & test sets - effective approach if a large amount of data is available