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# Reduced Error Pruning 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 Tutorial decision tree pruning tutorial 14: Pruning Pruning to avoid overfitting The approach to constructing decision trees usually involves using greedy heuristics (such as cost complexity pruning example 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 of

## Reduced Error Pruning Algorithm

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 in pruning decision tree in r 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 0:1 | Rural

## Tree Pruning In Data Mining Examples

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reduced error pruning algorithm
Reduced Error Pruning Algorithm p classify instances Pruning reduces the complexity of the final classifier and hence improves predictive accuracy by the reduction of reduced error pruning example overfitting This article includes a list of references but its sources decision tree pruning tutorial remain unclear because it has insufficient inline citations Please help to improve this article by introducing more pre pruning and post pruning in decision tree precise citations May Learn how and when to remove this template message Contents Introduction Techniques Reduced error pruning Cost complexity pruning See cost complexity pruning also References Further reading External links Introduction

reduced error pruning decision trees examples

reduced error pruning in decision trees
Reduced Error Pruning In Decision Trees p Exercise Advanced Topics Evaluating Decision Trees Exercise Overfitting Pruning Exercise Further Topics 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 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 p Cost

reduced error pruning tutorial

reduced error pruning examples
Reduced Error Pruning Examples p Help pages Full-text links Download PDF PostScript license Current browse context cs AI prev next new recent Change to browse by cs References CitationsNASA ADS DBLP - CS p Decision Tree Pruning Tutorial p Bibliography listing bibtex Tapio Elomaa Matti K xE xE ri xE inen Bookmark what is this pessimistic pruning Computer Science Artificial Intelligence Title An Analysis of Reduced Error Pruning Authors T Elomaa M Kaariainen Submitted reduced error pruning algorithm on Jun Abstract Top-down induction of decision trees has been observed to suffer from the inadequate functioning of the pruning phase In

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reduced error pruning and rule post pruning
Reduced Error Pruning And Rule Post Pruning p result in improved estimated accuracy Sort the pruned rules by their estimated accuracy and consider them in this sequence when classifying unseen instances Patricia Riddle Fri May NZST p p here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn p Pruning Decision Tree In R p more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users decision

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Resubstitution Error Decision Tree p here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn classification error rate decision tree more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags what is root node error Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of million programmers just like you how to calculate accuracy of a decision tree helping each

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