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Classification Error Rate Decision Tree

Decision Tree Classification In Data Mining Example
Decision Tree Classification In R
Decision Tree Classification Remote Sensing
Misclassification Rate Decision Tree

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Decision Tree Classification In Data Mining Example

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Decision Tree Classification In R

top When is classification error rate preferable when pruning decision trees? up vote 2 down vote favorite I'm going through Chapter 8 of "Introduction to Statistical learning" which introduces decision trees. My question is specific to the three approaches to pruning a decision tree (i.e., classification error rate, Gini Index, and cross-entropy). With regard to building classification trees, the chapter states that "classification error is not sufficiently sensitive enough for tree-growing, and in practice, the

Decision Tree Classification Remote Sensing

Gini Index and cross-entropy are preferred". However, it also states that "Any of these three approaches might be used when pruning the tree, but the classification error rate is preferable if prediction accuracy of the final pruned tree is the goal." There are two questions with regard to this: Given that classification error rate is not sensitive enough, why should it be used, over Gini Index and cross-entropy, if prediction accuracy is the goal? What advantage does it have over Gini Index and cross-entropy? If classification error rate is preferred, in what instances would we use the Gini Index and cross-entropy when pruning a decision tree? cart share|improve this question asked Mar 8 '15 at 10:32 Eugene Yan 1255 add a comment| 1 Answer 1 active oldest votes up vote 1 down vote accepted It's generally the case that, if you're trying to maximize some loss function (classification accuracy, Brier score, log-loss, etc.) it's more effective to use modeling procedures (tree learning, tree pruning) that maximize this directly. So the default attitude would be that, if you're trying to maximize classification accuracy, you should both train and prune your tree based on classification accuracy. However, there are a couple of things that might motivate you to make exceptions to this and not train your tree based on classification accuracy: The tree

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misclassification error tree

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