how to do cross-validation without built-in function in excel data

i am studying about different classifires and i like to know how to do cross-validation without built-in function.i am using iris dataset for practise?

[Saket Chirania]

There is inbuild function named, crossvalind, which helps to perform diffrent cross validation methods such as Kfold, HoldOut, etc.

However, you can import your data to the MATLAB workspace using readmatrix function from the excel data.

Consider, you want to perform 5 cross folds.

• 1st fold : 1 2 for test, 3:10 for train
• 2nd fold : 3 4 for test, 1 2 5:10 for train
• 3rd fold : 5 6 for test, 1:4 7:10 for train
• 4th fold : 7 8 for test, 1:6 9:10 for train
• 5th fold : 9 10 for test, 1:8 for train

Following code is an example for this process:

data = readmatrix('IrisData.xls');

dataRowNumber = size(data,1);
labels= rand(dataRowNumber,1) > 0.5;
randomColumn = rand(dataRowNumber,1);
X = [ randomColumn data labels];

SortedData = sort(X,1);
crossValidationFolds = 5;
numberOfRowsPerFold = dataRowNumber / crossValidationFolds;
crossValidationTrainData = [];
crossValidationTestData = [];


for startOfRow = 1:numberOfRowsPerFold:dataRowNumber
    testRows = startOfRow:startOfRow+numberOfRowsPerFold-1;

        if (startOfRow == 1)     
        trainRows = [max(testRows)+1:dataRowNumber];     
        else
        trainRows = [1:startOfRow-1 max(testRows)+1:dataRowNumber];  
        end

    crossValidationTrainData = [crossValidationTrainData ; SortedData(trainRows ,:)];  
    crossValidationTestData = [crossValidationTestData ;SortedData(testRows ,:)];

end