Someone have a look and drop a response please.
SVM parameter optimization using GA
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Commented: Josh on 7 Dec 2022
I am facing issues of high prediction error. Please help.
X = data1(1:83,1:end-1);
Y = data1(1:83,end);
X1 = data1(84:end,1:end-1);
Y1 = data1(84:end,end);
c = cvpartition(Y,'KFold',5,'Stratify',false);
fobj = @(x)kfoldLoss(fitrsvm(X,Y,'CVPartition',c,'KernelFunction','gaussian','BoxConstraint', x(1),'KernelScale',x(2),'Epsilon',x(3)));
intcon = 1; % intcon is the indicator of integer variables
lb = [1e-3,1e-3,1e-3]; % set lower bounds
ub = [1e3,1e1,1e1]; % set upper bounds
[sol,fval] = ga(fobj,3,,,,,lb,ub,,intcon);
FinalModel = fitrsvm(X,Y,'KernelFunction', 'gaussian','BoxConstraint', sol(1),'KernelScale',sol(2),'Epsilon',sol(3));
yfit = predict(FinalModel, X1);
RMSE = rmse(yfit,Y1)
Sudarshan on 7 Dec 2022
I tried running the script and reproducing the high RMSE values.
- The RMSE value decreases on taking a higher number of training samples.
- Instead of using just 83 samples for X, I used 150 samples, and the RMSE significantly decreased from 0.15 to 0.008.
- The reason for the high RMSE value could be that there are less training samples.
You could try increasing the size of the training dataset and see if that solves the issue.
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