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Reduced Error Pruning Examples

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Decision Tree Pruning Tutorial

Bibliography listing | bibtex Tapio Elomaa Matti Kääriäinen 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 3 Jun 2011) Abstract: Top-down induction of decision trees has been observed to suffer from the inadequate functioning of the pruning phase. In particular, it is known that the

Pre Pruning And Post Pruning In Decision Tree

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 the problems of decision tree learning. In this paper we present analyses of Reduced Error Pruning in three different settings.

Cost Complexity Pruning Example

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 clarifies the different variants of the Reduced Error Pruning algorithm, brings new insight to its algorithmic properties, analyses the algorithm with less imposed assumptions than before, and includes the previously overlooked empty subtrees to the analysis. Subjects: Artificial Intelligence (cs.AI) Journalreference: Journ

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