MATLAB: In the following code, Why is the classification accuracy (acc1, acc2) calculated differently

crossvalcrossvalidationcvpartitionfitcknnholdoudknn classificationMATLABpredicttraining

The following function (knn_test) takes the following parameters: X indicates dataset sampels, Y indicates dataset labels, and filterindex corresponds column filter. If I want to filter the data, filterIndex is set to 1, whichever column is to be filtered.
I want to validate two holdout method. One with crossval function, and the other with cvpartition function. But when I call this method, acc1 and acc2 variables show different values.
I added break point to the code and debugged the code. I examined CVKNNModels' partition property and it was the same with the c particion in the Model 2.
What could have gone wrong with the following code?
Why did these two accuracy variables take different values?
If I want to use this function for holdout classification, which model should I choose?
Thanks. 
function [acc1,acc2]=knn_test(X,Y,filterIndex)
columnfilterIndex = find(filterIndex==1);
%Model 1
tra1Data = X(:,[columnfilterIndex]);
tra1Label=Y;
KNNModel1=fitcknn(tra1Data, tra1Label, 'Distance', 'Euclidean', 'NumNeighbors', 3, 'DistanceWeight', 'Equal', 'Standardize', true);
rng('default');
CVKNNModel = crossval(KNNModel1,'holdout',0.3);
loss=kfoldLoss(CVKNNModel);
acc1=1-loss;
%Model 2
rng('default');
c = cvpartition(Y,'HoldOut',0.3);
tra2Data=X(c.training,[columnfilterIndex]);
tra2Label=Y(c.training,:);
test2Data=X(c.test,[columnfilterIndex]);
test2Label=Y(c.test,:);
KNNModel2 = fitcknn(tra2Data,tra2Label,'Distance', 'Euclidean','NumNeighbors',3, 'DistanceWeight', 'Equal','Standardize', true);
pre_test = predict(KNNModel2,test2Data);
correctPredictions = (pre_test == test2Label);
acc2 = sum(correctPredictions)/length(correctPredictions);
%perf=classperf(uint8(test2Label),uint8(pre_test)); 
%acc2=perf.CorrectRate;

Best Answer

The two ways of hold-out cross-validation that you described have some subtle differences. For Model 1, when calling the crossval method on the KNNModel1, the prior is based on the whole dataset. For Model 2, the prior is based on the training partition tra2Data. If you specify the same prior, you should get the same results.
KNNModel2 = fitcknn(tra2Data,tra2Label,...
    'Distance', 'Euclidean','NumNeighbors',3, ...
    'DistanceWeight', 'Equal','Standardize', true, 'Prior', KNNModel1.Prior);
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