plotroc draws the recevier operating characteristic(ROC) curve for an svm-model
auc = libsvm_rocplot(training_label, training_instance) auc = libsvm_rocplot(training_label, training_instance , model) auc = libsvm_rocplot(training_label, training_instance , libsvm_options) auc = libsvm_rocplot(training_label, training_instance , libsvm_options, uselinear)
Use cross-validation on training data to get decision values and plot ROC curve.
Use the given model to predict testing data and obtain decision values for ROC
[label,instance]=libsvmread(fullfile(libsvm_getpath(),"demos","heart_scale")); // 5-fold cross-classification, training of svm is done inside of libsvm_rocplot libsvm_rocplot(label, instance,'-v 5'); // training using libsvm_svmtrain model = libsvm_svmtrain(label,instance); libsvm_rocplot(label,instance,model); //-------------------------- //libsvm_rocplot for linear models [label,instance]=libsvmread(fullfile(libsvm_getpath(),"demos","heart_scale")); // 5-fold cross-classification, training of svm is done inside of libsvm_rocplot libsvm_rocplot(label, instance,'-v 5',%t); // training using train model = libsvm_lintrain(label,instance); libsvm_rocplot(label,instance,model); | ![]() | ![]() |