www.gusucode.com > stats 源码程序 matlab案例代码 > stats/PredictkFoldCrossValidationLabelsOfECOCModelsExample.m
%% Predict _k_-Fold Cross-Validation Labels of ECOC Models %% % Load Fisher's iris data set. % Copyright 2015 The MathWorks, Inc. load fisheriris X = meas; Y = categorical(species); classOrder = unique(Y); 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 |ClassificationPartitionedModel| model. By default, the % software implements 10-fold cross validation. You can alter the number of % folds using the |'KFold'| name-value pair argument. %% % Predict the out-of-fold labels. Print a random subset of true % and predicted labels. labels = kfoldPredict(CVMdl); idx = randsample(numel(labels),10); table(Y(idx),labels(idx),... 'VariableNames',{'TrueLabels','PredictedLabels'}) %% % |CVMdl| correctly labeled the out-of-fold observations with indices |idx|.