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    %% Cross Validating a Discriminant Analysis Classifier
% This example shows how to perform five-fold cross validation of a
% quadratic discriminant analysis classifier.
%%
% Load the sample data.

% Copyright 2015 The MathWorks, Inc.

load fisheriris
%%
% Create a quadratic discriminant analysis classifier for the data.
quadisc = fitcdiscr(meas,species,'DiscrimType','quadratic');
%% 
% Find the resubstitution error of the classifier.
qerror = resubLoss(quadisc)
%% 
% The classifier does an excellent job. Nevertheless, resubstitution error
% can be an optimistic estimate of the error when classifying new data. So
% proceed to cross validation.
%% 
% Create a cross-validation model.
cvmodel = crossval(quadisc,'kfold',5);
%% 
% Find the cross-validation loss for the model, meaning the error of the
% out-of-fold observations.
cverror = kfoldLoss(cvmodel)
%%
% The cross-validated loss is as low as the original resubstitution
% loss. Therefore, you can have confidence that the classifier is
% reasonably accurate.