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Resubstitution classification edge for discriminant analysis classifier



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


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Estimate the quality of a discriminant analysis classifier for Fisher's iris data by resubstitution.

Load Fisher's iris data set.

load fisheriris

Train a discriminant analysis classifier.

Mdl = fitcdiscr(meas,species);

Compute the resubstitution edge.

redge = resubEdge(Mdl)
redge = 0.9454

Input Arguments

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Trained discriminant analysis classifier, specified as a ClassificationDiscriminant object. To create a discriminant analysis classifier, use fitcdiscr.

Output Arguments

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Classification edge obtained by resubstituting 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 class prior probabilities. If you supply additional weights, those weights are normalized to sum to the prior probabilities in the respective classes, and are then used to compute the weighted average.


The classification margin is the difference between the classification score for the true class and maximal classification score for the false classes.

The classification margin is a column vector with the same number of rows as in the matrix X. A high value of margin indicates a more reliable prediction than a low value.


For discriminant analysis, the score of a classification is the posterior probability of the classification. For the definition of posterior probability in discriminant analysis, see Posterior Probability.

Version History

Introduced in R2011b

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