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Classification edge for observations not used for training


E = kfoldEdge(obj)
E = kfoldEdge(obj,Name,Value)


E = kfoldEdge(obj) returns classification edge (average classification margin) obtained by cross-validated classification model obj. For every fold, this method computes classification edge for in-fold observations using an ensemble trained on out-of-fold observations.

E = kfoldEdge(obj,Name,Value) calculates edge with additional options specified by one or more Name,Value pair arguments. You can specify several name-value pair arguments in any order as Name1,Value1,…,NameN,ValueN.

Input Arguments


Object of class ClassificationPartitionedModel.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.


Indices of folds ranging from 1 to obj.KFold. Use only these folds for predictions.

Default: 1:obj.KFold


Character vector or string scalar representing the meaning of the output edge:

  • 'average'edge is a scalar value, the average over all folds.

  • 'individual'edge is a vector of length obj.KFold with one element per fold.

Default: 'average'

Output Arguments


The average classification margin. E is a scalar or vector, depending on the setting of the mode name-value pair.


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Compute the k-fold edge for a model trained on Fisher's iris data.

Load Fisher's iris data set.

load fisheriris

Train a classification tree classifier.

tree = fitctree(meas,species);

Cross validate the classifier using 10-fold cross validation.

cvtree = crossval(tree);

Compute the k-fold edge.

edge = kfoldEdge(cvtree)
edge = 0.8578

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