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Class: ClassificationEnsemble

Predict ensemble response by resubstitution


label = resubPredict(ens)
[label,score] = resubPredict(ens)
[label,score] = resubPredict(ens,Name,Value)


label = resubPredict(ens) returns the labels ens predicts for the data ens.X. label is the predictions of ens on the data that fitensemble used to create ens.

[label,score] = resubPredict(ens) also returns scores for all classes.

[label,score] = resubPredict(ens,Name,Value) finds resubstitution predictions with additional options specified by one or more Name,Value pair arguments.

Input Arguments


A classification ensemble created with fitensemble.

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 single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.


Indices of weak learners in the ensemble ranging from 1 to NumTrained. oobLoss uses only these learners for calculating loss.

Default: 1:NumTrained

Output Arguments


The response ens predicts for the training data. label is the same data type as the training response data ens.Y, and has the same number of entries as the number of rows in ens.X.


An N-by-K matrix, where N is the number of rows in ens.X, and K is the number of classes in ens. High score value indicates that an observation likely comes from this class.


Score (ensemble)

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.


Find the total number of misclassifications of the Fisher iris data for a classification ensemble:

load fisheriris
ens = fitensemble(meas,species,'AdaBoostM2',100,'Tree');
Ypredict = resubPredict(ens); % the predictions
Ysame = strcmp(Ypredict,species); % true when ==
sum(~Ysame) % how many are different?

ans =
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