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Classification edge by resubstitution



edge = resubEdge(tree) returns the classification edge obtained by tree on its training data.


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Estimate the quality of a classification tree for the Fisher iris data by resubstitution.

load fisheriris
tree = fitctree(meas,species);
redge = resubEdge(tree)
redge =

Input Arguments

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Classification tree, specified as a ClassificationTree object. Use the fitctree function to create a classification tree object.

Output Arguments

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Classification edge obtained by re-substituting the training data into the calculation of edge, returned as a scalar.

More About

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The edge is the weighted mean value of the classification margin. The weights are the class probabilities in tree.Prior.


The classification margin is the difference between the classification score for the true class and maximal classification score for the false classes. Margin is a column vector with the same number of rows as in the matrix X.

Score (tree)

For trees, the score of a classification of a leaf node is the posterior probability of the classification at that node. The posterior probability of the classification at a node is the number of training sequences that lead to that node with the classification, divided by the number of training sequences that lead to that node.

For an example, see Posterior Probability Definition for Classification Tree.

Extended Capabilities

Version History

Introduced in R2011a