How to do a ROC analysis using Matlab build-in SVM (Not LibSVM)
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Hi all,
Just wondering anyone knows how to do a ROC analysis using Matlab build-in SVM? This question has been asked by millions of times on the web, but no answer.
svmStruct = svmtrain(featureSelcted, groundTruthGroup, 'Kernel_Function', 'rbf', 'Method', 'QP');
svmClassified = svmclassify(svmStruct,featureSelcted);
% Predict resubstitution response of SVM classifier
SVMScore ???
% Fit probabilities for scores
[FPR, TPR, Thr, AUC, OPTROCPT] = perfcurve(groundTruth(:,1), SVMScore(:,1), 1);
Essentially, we need a function to get the '*scores*' of the SVM classifier (SVMScore). Thanks!
A.
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Ilya
2013년 3월 21일
Well, there is an answer here http://www.mathworks.com/matlabcentral/answers/64475-does-anybody-have-expertise-with-matlab-svm-classifier, with a reference to another thread. I am collecting all the pieces in one place.
Assume your class labels are -1 and +1, assume that you have trained with 'autoscale' set to true by default, let svm be the struct for the trained SVM model, and let Xnew be the new data for which you need to compute the soft scores.
shift = svm.ScaleData.shift;
scale = svm.ScaleData.scaleFactor;
Xnew = bsxfun(@plus,Xnew,shift);
Xnew = bsxfun(@times,Xnew,scale);
sv = svm.SupportVectors;
alphaHat = svm.Alpha;
bias = svm.Bias;
kfun = svm.KernelFunction;
kfunargs = svm.KernelFunctionArgs;
f = kfun(sv,Xnew,kfunargs{:})'*alphaHat(:) + bias;
f = -f; % flip the sign to get the score for the +1 class
Then call perfcurve(true_labels,f,1).
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Tahir
2015년 5월 8일
Hi, i just use the code ...but i have confusion about the line
f = -f; % flip the sign to get the score for the +1 class
why this flipping?? i use labels 0 and 1...still i should flip the f.??
Farag Zbeda
2015년 6월 3일
Hi Ilya
What do yo mean by true_labels when calling perfcurve(true_labels,f,1)? I guess they are the test instances labels. If so, how to calculate them?
Thanks
Faraj
추가 답변 (1개)
Mehrdad Heydarzadeh
2016년 8월 10일
Hi Aaronne, It seems that your problem has been answered. But there is a shorter way of doing that using Matlab functions. Just have a look at the second example in following link: http://www.mathworks.com/help/stats/perfcurve.html
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