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    %% Estimate _k_-Fold Cross-Validation Edge
%%
% Load the NLP data set.
load nlpdata
%%
% |X| is a sparse matrix of predictor data, and |Y| is a categorical vector
% of class labels.
%%
% For simplicity, use the label 'others' for all observations in |Y| that
% are not |'simulink'|, |'dsp'|, or |'comm'|.
Y(~(ismember(Y,{'simulink','dsp','comm'}))) = 'others';
%%
% Cross-validate a multiclass, linear classification model. 
rng(1); % For reproducibility 
CVMdl = fitcecoc(X,Y,'Learner','linear','CrossVal','on');
%%
% |CVMdl| is a |ClassificationPartitionedLinearECOC| 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 of the out-of-fold edges.
e = kfoldEdge(CVMdl)
%%
% Alternatively, you can obtain the per-fold edges by specifying the
% name-value pair |'Mode','individual'| in |kfoldEdge|.