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%% Determine _k_-Fold Cross-Validation Loss of ECOC Models %% % Load Fisher's iris data set. % Copyright 2015 The MathWorks, Inc. load fisheriris X = meas; Y = categorical(species); classOrder = unique(Y); % Class order rng(1); % For reproducibility %% % Train an ECOC model using SVM binary classifiers, and specify to cross % validate. It is good practice to standardize the predictors and define % the class order. Specify to standardize the predictors using an SVM % template. t = templateSVM('Standardize',1); CVMdl = fitcecoc(X,Y,'CrossVal','on','Learners',t,'ClassNames',classOrder); %% % |CVMdl| is a |ClassificationPartitionedECOC| model. By default, the % software implements 10-fold cross validation. You can alter the number of % folds using the |'KFold'| name-value pair argument. %% % Estimate the average out-of-fold classification error. L = kfoldLoss(CVMdl) %% % The average classification error for the folds is 4%. %% % Alternatively, you can obtain the per-fold losses by specifying the % name-value pair |'Mode','individual'| in |kfoldLoss|.