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 ensemble 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.
Object of class ClassificationPartitionedEnsemble. Create ens with fitensemble along with one of the cross-validation options: 'crossval', 'kfold', 'holdout', 'leaveout', or 'cvpartition'. Alternatively, create ens from a classification ensemble with crossval.
Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single 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 ens.KFold. Use only these folds for predictions.
String representing the meaning of the output edge:
The average classification margin. E is a scalar or vector, depending on the setting of the mode name-value pair.
The edge is the weighted mean value of the classification margin. The weights are the class probabilities in obj.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 obj.X.
For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.
Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:
AdaBoostM1 scores range from –∞ to ∞.
Bag scores range from 0 to 1.
Compute the k-fold edge for an ensemble trained on the Fisher iris data:
load fisheriris ens = fitensemble(meas,species,'AdaBoostM2',100,'Tree'); cvens = crossval(ens); E = kfoldEdge(cvens) E = 3.2078