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# Reduced Error Pruning Decision Trees Examples

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

## Pre Pruning And Post Pruning In Decision Tree

citations. Please help to improve this article by introducing more precise citations. (May 2008) (Learn how and when reduced error pruning algorithm to remove this template message) Contents 1 Introduction 2 Techniques 2.1 Reduced error pruning 2.2 Cost complexity pruning 3 See also 4 References 5 Further reading 6 External links Introduction cost complexity pruning example 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 about the sample space. However, it is hard to tell when a tree algorithm should stop

## Pessimistic Pruning

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 pruning. Starting at the leaves, each node is replaced with its most popular class. If the prediction accuracy is not affected then the change is kept. While somewhat naive, reduced error pruni

Help pages Full-text links: Download: PDF PostScript (license) Current browse context: cs.AI new| recent| 1106Change to browse by: cs References pruning decision tree in r & CitationsNASA ADS DBLP - CS Bibliography listing | bibtex decision tree pruning python Tapio Elomaa Matti Kääriäinen Bookmark (what is this?) Computer Science > Artificial Intelligence Title: An Analysis

## Pre Pruning In Data Mining

of Reduced Error Pruning Authors: T. Elomaa, M. Kaariainen (Submitted on 3 Jun 2011) Abstract: Top-down induction of decision trees has been observed to suffer from https://en.wikipedia.org/wiki/Pruning_(decision_trees) the inadequate functioning of the pruning phase. In particular, it is known that the size of the resulting tree grows linearly with the sample size, even though the accuracy of the tree does not improve. Reduced Error Pruning is an algorithm that has been used as a representative technique in attempts to explain http://arxiv.org/abs/1106.0668 the problems of decision tree learning. In this paper we present analyses of Reduced Error Pruning in three different settings. First we study the basic algorithmic properties of the method, properties that hold independent of the input decision tree and pruning examples. Then we examine a situation that intuitively should lead to the subtree under consideration to be replaced by a leaf node, one in which the class label and attribute values of the pruning examples are independent of each other. This analysis is conducted under two different assumptions. The general analysis shows that the pruning probability of a node fitting pure noise is bounded by a function that decreases exponentially as the size of the tree grows. In a specific analysis we assume that the examples are distributed uniformly to the tree. This assumption lets us approximate the number of subtrees that are pruned because they do not receive any pruning examples. This paper clar

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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 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

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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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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

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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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