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Reduced Error Pruning In Decision Trees

9: Exercise 4 10: Advanced Topics 11: Evaluating Decision Trees 12: Exercise 5 13: Overfitting 14: Pruning 15: Exercise 6 16: Further Topics 17: pre pruning and post pruning in decision tree Conclusion Software Data Sets Books Papers Sites Feeds About Contact Decision Trees decision tree pruning tutorial Tutorial 14: Pruning Pruning to avoid overfitting The approach to constructing decision trees usually involves using greedy heuristics (such as reduced error pruning algorithm Entropy reduction) that overfit the training data and lead to poor accuracy in future predictions. In response to the problem of overfitting nearly all modern decision tree algorithms adopt a pruning strategy

Cost Complexity Pruning Example

of some sort. Many algorithms use a technique known as postpruning or backward pruning. This essentially involves growing the tree from a dataset until all possible leaf nodes have been reached (i.e. purity) and then removing particular substrees. Studies have shown that post-pruning will result in smaller and more accurate trees by up to 25%. Different pruning techniques have been developed which have been compared pessimistic pruning in several papers and like with the different splitting criteria it has been found that there is not much variation in terms of performance (e.g. see Mingers89 and Esposito et. al. 97). There are quite a few methods that have been developed. We'll look at one of the basic ones here. Pruning strategies An example: Reduced Error Pruning (Quinlan 86) At each node in a tree it is possible to see the number of instances that are misclassified on a testing set by propagating errors upwards from leaf nodes. This can be compared to the error-rate if the node was replaced by the most common class resulting from that node. If the difference is a reduction in error, then the subtree at the node can be considered for pruning. This calculation is performed for all nodes in the tree and whichever one has the highest reduced-error rate is pruned. The procedure is then recursed over the freshly pruned tree until there is no possible reduction in error rate at any node. An example … | 1/2 | Income | | High 1/2 | District | | Suburban 0/0 | null 0:0

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