Home > decision tree > reduced error pruning algorithm

# Reduced Error Pruning Algorithm

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 2008) (Learn how and when to remove this template message) Contents 1 Introduction 2 Techniques 2.1 Reduced error pruning 2.2 Cost complexity pruning 3 See cost complexity pruning also 4 References 5 Further reading 6 External links Introduction One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples. A small tree might not capture important structural information

## Pruning Decision Tree In R

about the sample space. However, it is hard to tell when a tree algorithm should stop because it is impossible to tell if the addition of a single extra node will dramatically decrease error. This problem is known as the horizon effect. A common strategy is to grow the tree until each node contains a small number of instances then use pruning to remove nodes that do not provide additional information.[1] Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a cross-validation set. There are many techniques for tree pruning that differ in the measurement that is used to optimize performance. Techniques Pruning can occur in a top down or bottom up fashion. A top down pruning will traverse nodes and trim subtrees starting at the root, while a bottom up pruning will start at the leaf nodes. Below are several popular pruning algorithms. Reduced error pruning One of the simplest forms of pruning is reduced error prun

be down. Please try the request again. Your cache administrator is webmaster. Generated Wed, 26 Oct 2016 22:11:56 GMT by s_wx1087 (squid/3.5.20)

be down. Please try the request again. Your cache administrator is webmaster. Generated Wed, 26 Oct 2016 22:11:56 GMT by s_wx1087 (squid/3.5.20)

be down. Please try the request again. Your cache administrator is webmaster. Generated Wed, 26 Oct 2016 22:11:56 GMT by s_wx1087 (squid/3.5.20)

### Related content

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

reduced error pruning wiki

reduced error pruning decision trees
Reduced Error Pruning 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 Tutorial decision tree pruning tutorial 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 p Reduced

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

resubstitution error decision tree
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

root node error