fitrsvm fails if epsilon is generated using a for loop
I wanted to run a grid search to find suitable parameters for my SVM model but I have discovered that fitrsvm gives inconsistent errors if the value of the epsilon parameter is generated using a ‘for loop’. For example the RMSE for my model with epsilon = 0.8 will be different if I use the for loop: for epsilon = 0.8:.1:1.2 compared with if I use the for loop for epsilon = 0.1:.1:1.2 The RMSEs are 2.6868 and 2.7020 respectively I thought this might be some floating point error, so I tried to ensure that the epsilon value passed to fitrsvm was exactly 0.8. I did this by creating variable d_epsilon (line 17) and passing its value to fitrsvm (ie by changing line 26 to ‘Epsilon’ = d_epsilon but this did not work. By contrast using c_epsilon which is completely independent of the for loop (line 16) does work. In my real project, I use nested loops to search for values for Epsilon, Boxconstraint, and KernelScale. The inconsistencies in my results are about 10%. (I am using a grid search as the parameters returned using OptimizeHyperparameters perform worse that some of the parameters cited in journal articles for my dataset (UCI’s auto-mpg). clear all %%read in auto-mpg.csv. This is a cleaned version of UCI dataset auto-mpg data = readtable('auto-mpg.csv','ReadVariableNames',false); VarNames = {'mpg','cylinders' 'displacement' 'horsepower' 'weight' 'acceleration' ... 'modelYear' 'origin' 'carName'}; data.Properties.VariableNames = VarNames; data = [data(:,2:9) data(:,1)]; data.carName=[]; %%carry out 10 fold cross-validation with different epsilon values testResults_SVM=[]; testActual_SVM=[]; rng('default') c = cvpartition(data.mpg,'KFold',10); for epsilon = 0.1:0.1:1.2 %c_epsilon= 0.80000; %d_epsilon = str2double(string(round(epsilon,2))) for fold = 1:10 cv_trainingData = data(c.training(fold), :); cv_testData = data(c.test(fold), :); AutoSVM = fitrsvm(cv_trainingData,'mpg',... 'KernelFunction', 'gaussian', ... 'PolynomialOrder', [], ... 'KernelScale', 5.5, ... 'BoxConstraint', 100, ... 'Epsilon', epsilon, ... 'Standardize', true); convergenceChk(fold)=AutoSVM.ConvergenceInfo.Converged; testResults_SVM=[testResults_SVM;predict(AutoSVM,cv_testData)]; testActual_SVM=[testActual_SVM;cv_testData.mpg]; end %%generate summary statistics and plots residual_SVM = testResults_SVM-testActual_SVM; AutoMSE_SVM=((sum((residual_SVM).^2))/size(testResults_SVM,1)); AutoRMSE_SVM = sqrt(AutoMSE_SVM); if round(epsilon,4) == 0.8 AutoRMSE_SVM end end
Prashant Kumar answered .
2025-11-20
clear all
%%read in auto-mpg.csv. This is a cleaned version of UCI dataset auto-mpg
data = readtable('auto-mpg.csv','ReadVariableNames',false);
VarNames = {'mpg','cylinders' 'displacement' 'horsepower' 'weight' 'acceleration' ...
'modelYear' 'origin' 'carName'};
data.Properties.VariableNames = VarNames;
data = [data(:,2:9) data(:,1)];
data.carName=[];
rng('default')
c = cvpartition(data.mpg,'KFold',10);
LossesLoop1 = [];
LossesLoop2 = zeros(1,7);
LossesIndividual = [];
for epsilon = 0.1:0.1:1.2
AutoSVM = fitrsvm(data,'mpg',...
'CVPartition', c,...
'KernelFunction', 'gaussian', ...
'PolynomialOrder', [], ...
'KernelScale', 5.5, ...
'BoxConstraint', 100, ...
'Epsilon', epsilon, ...
'Standardize', true);
LossesLoop1(end+1) = kfoldLoss(AutoSVM);
end
for epsilon = 0.8:0.1:1.2
AutoSVM = fitrsvm(data,'mpg',...
'CVPartition', c,...
'KernelFunction', 'gaussian', ...
'PolynomialOrder', [], ...
'KernelScale', 5.5, ...
'BoxConstraint', 100, ...
'Epsilon', epsilon, ...
'Standardize', true);
LossesLoop2(end+1) = kfoldLoss(AutoSVM);
end
for epsilon = [.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2]
AutoSVM = fitrsvm(data,'mpg',...
'CVPartition', c,...
'KernelFunction', 'gaussian', ...
'PolynomialOrder', [], ...
'KernelScale', 5.5, ...
'BoxConstraint', 100, ...
'Epsilon', epsilon, ...
'Standardize', true);
LossesIndividual(end+1) = kfoldLoss(AutoSVM);
end
LossesLoop1
LossesLoop2
LossesIndividual
isequal(LossesLoop1, LossesIndividual)
isequal(LossesLoop2(8:end), LossesIndividual(8:end))
isequal(.1:.1:1.2, [.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2])
[.1:.1:1.2] - [.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2]