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Reduced Error Pruning Advantages

classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. This article includes decision tree pruning example a list of references, but its sources remain unclear because it has pre pruning and post pruning in decision tree insufficient inline citations. Please help to improve this article by introducing more precise citations. (May 2008) (Learn how

Pruned Meaning In English

and when 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

Decision Tree Pruning Tutorial

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 about the sample space. However, it is hard to tell when cost complexity pruning 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 pruning. Starting at the leaves, each node is replaced with its most popular class. If the prediction accuracy is not affected then t

in: ·Journal Journal of Artificial Intelligence Research archive Volume 15 Issue 1, July 2001 Pages 163-187 AI Access Foundation , USA tableofcontents 2001 Article Bibliometrics ·Downloads (6 Weeks): n/a ·Downloads (12 Months): n/a ·Downloads (cumulative): n/a

Reduced Error Pruning Algorithm

·Citation Count: 12 Recent authors with related interests Concepts in this article pruning decision tree in r powered by Concepts inAn analysis of reduced error pruning Pruning Pruning is a horticultural practice involving the selective removal of parts tree pruning in data mining ppt of a plant, such as branches, buds, or roots. Reasons to prune plants include deadwood removal, shaping (by controlling or directing growth), improving or maintaining health, reducing risk from falling branches, preparing nursery specimens https://en.wikipedia.org/wiki/Pruning_(decision_trees) for transplanting, and both harvesting and increasing the yield or quality of flowers and fruits. morefromWikipedia Inductive reasoning Inductive reasoning, also known as induction, is a kind of reasoning that constructs or evaluates propositions that are abstractions of observations of individual instances of members of the same class. Inductive reasoning contrasts with deductive reasoning in that a general conclusion is arrived at by specific examples. morefromWikipedia Decision tree learning Decision http://dl.acm.org/citation.cfm?id=1622850 tree learning, used in statistics, data mining and machine learning, uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. More descriptive names for such tree models are classification trees or regression trees. In these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. morefromWikipedia Sample size determination Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is determined based on the expense of data collection, and the need to have sufficient statistical power. morefromWikipedia Uniform distribution (continuous) In probability theory and statistics, the continuous uniform distribution or rectangular distribution is a family of probability distributions such that for each member of the family, all intervals of the same length on the distribution's support are equally probable. The support is defined by the two parameters, a and b, which are its minimum and maximum values. The distribution is often abbreviated U(a,

LinkedIn Reddit Read full-text A Comparative Study of Reduced Error Pruning Method in Decision Tree AlgorithmsArticle (PDF Available) · May 2013 with 943 https://www.researchgate.net/publication/236843067_A_Comparative_Study_of_Reduced_Error_Pruning_Method_in_Decision_Tree_Algorithms Reads1st W Nor2nd Haizan W Mohamed3rd Mohd Najib B. Mohd Salleh8.24 · Universiti Tun Hussein Onn Malaysia4th Abdul Halim Bin Omar0.88 · Universiti Tun Hussein Onn MalaysiaAbstractDecision tree is one of the most popular and efficient technique in data mining. This technique has been established and well-explored by many researchers. However, some decision tree decision tree algorithms may produce a large structure of tree size and it is difficult to understand. Furthermore, misclassification of data often occurs in learning process. Therefore, a decision tree algorithm that can produce a simple tree structure with high accuracy in term of classification rate is a need to work with huge volume of data. reduced error pruning Pruning methods have been introduced to reduce the complexity of tree structure without decrease the accuracy of classification. One of pruning methods is the Reduced Error Pruning (REP). To better understand pruning methods, an experiment was conducted using Weka application to compare the performance in term of complexity of tree structure and accuracy of classification for J48, REPTree, PART, JRip, and Ridor algorithms using seven standard datasets from UCI machine learning repository. In data modeling, J48 and REPTree generate tree structure as an output while PART, Ridor and JRip generate rules. In additional J48, REPTree and PART using REP method for pruning while Ridor and JRip using improvement of REP method, namely IREP and RIPPER methods. The experiment result shown performance of J48 and REPTree are competitive in producing better result. Between J48 and REPTree, average differences performance of accuracy of classification is 7.1006% and 6.2857% for complexity of tree structure. For classification rules algorithms, Ridor is the best algorithms c

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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

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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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Tree Misclassification Error p years sibsp number of siblings or spouses aboard parch number of parents or children aboard span p Classification Error Rate Decision Tree p class kw library span rpart span class kw library span rpart plot span class kw data span ptitanic span class kw str span ptitanic 'data frame' obs what is root node error of variables pclass Factor w levels st nd rd how to calculate accuracy of a decision tree survived Factor w levels died survived p Root Node Error Decision Tree p sex Factor w levels female male age Class 'labelled' atomic -