Predict ensemble response by resubstitution
label = resubPredict(ens)
[label,score] = resubPredict(ens)
[label,score] = resubPredict(ens,Name,Value)
A classification ensemble created with fitensemble.
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.
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.
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 = 5