I am assuming that you want to have some clarification regarding the cross-validation syntax in KNN and SVM
- KNN Classifier
mdlknn = fitcknn(X,labels, 'NumNeighbors', ...
k, 'Distance',@distKNN, 'Leaveout','on',...
'HyperparameterOptimizationOptions','UseParallel');
mdlknnNoCrossVal = fitcknn(X,labels, 'NumNeighbors', ...
k, 'Distance',@distKNN, 'HyperparameterOptimizationOptions','UseParallel');
mdlknn = crossval(mdlknnNoCrossVal, 'leaveout','on')
These two syntaxes may be equivalent for the creation of a cross-validated KNN model. “mdlknn" is a "ClassificationPartitionModel" classifier. "mdlknnNoCrossVal” creates a "ClassificationKNN" classifier which is cross validated using the “crossval” function.
- ECOC classifier
In the case of ECOC classifier you may use the “templateKNN” as the learner. It provides the feature to customize the distance metric using the ‘Distance’ argument. The distance metric can be passed as a function handle like that of the KNN classifier syntax.
And the two syntaxes are valid for the process of cross validation in the case of ECOC also.
