Dear all,
I wonder because loss returns very low values different to classification error in Feature forward selection. For example:
classifierfun = @(train_data,train_labels,test_data,test_labels) ...
loss(fitcsvm(train_data,train_labels,'KernelFunction',
'gaussian','KernelScale','auto','Standardize',true),test_data,test_labels,'LossFun', 'ClassifError');
[fs,history] = sequentialfs(classifierfun,table2array(TableFeaturesNormalized),Y,'
cv',c,'nfeatures',min(size(TableFeaturesNormalized,2),max_its_fs),'options',opts)
I get
Step 1, added column 178, criterion value 0.00996737
Step 2, added column 245, criterion value 0.00997051
The same in here
opts = statset(‘display’,’iter’);
costfun = @(XT,yT,Xt,yt)loss(fitcecoc(XT,yT),Xt,yt);
[fs, history] = sequentialfs(costfun, X_train,
y_train, ‘cv’, cv, ‘options’, opts);
why is this criterion value so low if it is a classification error?
However, if I do
classifierfun = @(train_data,train_labels,test_data,test_labels) ...
sum(predict(fitcsvm(train_data,train_labels,'KernelFunction', 'gaussian','Standardize',true),
test_data) ~= test_labels);
The values make sense
Step 1, added column 178, criterion value 0.36233363
Step 2, added column 245, criterion value 0.35302325
Thank you for the help
