Using Cross Validation for regression

hello, i have a dataset which has inputs 5x100 real numbers called "input" and i have 1x100 real numbers target called"output", and i need to use 10 fold crossvalidation but i m new in matlab and my dataset is not classification , it is for regression, and i cant use command " crossvalind=('Kfold',groups,k) because i dont have any groups,what should i do ? i have 100 samples and i have to use 90 of them for train,other 10 for test and i have to do this for 10 time for all datas and all datas must be used.

Answer 1

[Ahmet Cecen]

I would do something along the lines of:

 Randomize = randperm(length(input));
 inputRandom = input(Randomize);
 outputRandom = output(Randomize);

 for i=1:10
     Xtest = inputRandom(((i-1)*10+1):i*10);
     ytest = outputRandom(((i-1)*10+1):i*10);
     Xtrain = inputRandom; Xtrain(((i-1)*10+1):i*10) = [];
     ytrain = outputRandom; ytrain(((i-1)*10+1):i*10) = [];

     [b,bint,r,rint,stats] = regress(ytrain,Xtrain);
     YCrossValidated = Xtest*b;
     crossValidateResiduals = YCrossValidated - Ytest;
     % Calculate any fit statistics HERE and STORE anything you need.
 end

[Can Lo]

thank you so much for your help sir, i really appreciate it ! i didnt use this exactly but it gave me the idea :) but how can i use 10 fold crossvalidation for my situation ? or cross validation is only used for classification problems ?