Hello All, I am a newbie in Validating models, I am currently trying to make use of the MATLAB K-fold validation to assess the performance of my polynomial model that predicts house prices. I have 243 samples, i divided them into 10 groups, i then used 'for loop' to test 9 groups against 1 group (repeated X 10) My problem is storing the error rate (performance) for the 10 times i am making the prediction. How do i do that please? I tried making use of 'classperf' but i am getting the following error, Please see my code below;
Data set sample:
DateX: 10,20,30,40 … PriceY: 200,250,300,400 …
——–Code (MATLAB)——-
K = 10;
cvFolds = crossvalind('Kfold',DateX,K); %10-folds
cp = classperf(DateX); %To store (performance)
for i = 1:K
testIDx = (cvFolds == i);
trainIDx = ~testIDx;
model1 = polyfit(DateX(trainIDx),PriceY(trainIDx),2);
prediction1=model1(1)*DateX(testIDx).^2+model1(2)*DateX(testIDx)+model1(3);
cp = classperf(cp,prediction1,testIDx);
end
My Error Message;
Error using classperf (line 230) When the class labels of the CP object are numeric, the output of the classifier must be all non-negative integers or NaN's.
Is there any suggestions please on how i can store the performance of my model AND i would apperciate examples of other methods In MATLAB that i can apply to make predictions and validate performance (Neural networks, Classification e.t.c.)?? THANK YOU!
Best Answer