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%% Reduce the Size of Full ECOC Models % Full ECOC models (i.e., |ClassificationECOC| models) hold the % training data. For efficiency, you might not want to predict new labels % using a large classifier. %% % Load Fisher's iris data set. % Copyright 2015 The MathWorks, Inc. load fisheriris X = meas; Y = categorical(species); classOrder = unique(Y); %% % Train an ECOC model using SVM binary classifiers. 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); Mdl = fitcecoc(X,Y,'Learners',t,'ClassNames',classOrder); %% % |t| is an SVM template object. The software uses default values for empty % options in |t| during training. |Mdl| is a |ClassificationECOC| model. %% % Reduce the size of the trained ECOC model. CMdl = compact(Mdl) %% % |CMdl| is a |CompactClassificationECOC| model. It does not store the % training data nor some of the properties that |Mdl| stores. %% % Display how much memory each classifier uses. whos('Mdl','CMdl') %% % The full ECOC model (|Mdl|) is approximately double the size of the % compact ECOC model (|CMdl|). %% % You can remove |Mdl| from the MATLAB(R) Workspace, and pass % |CMdl| and new predictor values to |predict| to efficiently % label new observations.