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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[edit] 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[edit] 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[edit] One of the simplest forms of pruning is reduced error prun

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

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

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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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