CODEWITHSUNDEEP
rmachine-learningsvmroc
Akrout Med Amine
Akrout Med Amine

Reputation: 1

Plotting Roc Curve in SVM

I am trying to create a ROC curve for an SVM and here is the code I have used :

#learning from training
#tuned <- tune.svm(y~., data=train, gamma = 10^(-6:-1), cost = 10^(1:2))
summary(tuned)


svmmodel<-svm(y~., data=train, method="C-classification",
              kernel="radial", gamma = 0.01, cost = 100,cross=5, probability=TRUE) 

svmmodel

#predicting the test data
svmmodel.predict<-predict(svmmodel,subset(test,select=-y),decision.values=TRUE)
svmmodel.probs<-attr(svmmodel.predict,"decision.values")
svmmodel.class<-predict(svmmodel,test,type="class")
svmmodel.labels<-test$y
#analyzing result
svmmodel.confusion<-confusion.matrix(svmmodel.labels,svmmodel.class)
svmmodel.accuracy<-prop.correct(svmmodel.confusion)

#roc analysis for test data
svmmodel.prediction<-prediction(svmmodel.probs,svmmodel.labels)
svmmodel.performance<-performance(svmmodel.prediction,"tpr","fpr")
svmmodel.auc<-performance(svmmodel.prediction,"auc")@y.values[[1]]

but the problem that the curve ROC is somthing like this :

enter image description here

Upvotes: 0

Views: 13494

Answers (4)

Mirza Sohel
Mirza Sohel

Reputation: 11

This can be done very easily with 'ROCR' package. I use something like this to get the ROC curve.

p1<- predict(svm,test, type="decision")
pr<-prediction(p1, test$status)
prf<- performance(pr, measure="tpr",x.measure="fpr")
plot(prf)
lines(x = c(0,1), y = c(0,1),col="blue")

Upvotes: 1

user8637194
user8637194

Reputation: 1

Instead of using decision.values, try this:

fit = f(x,y,probability = TRUE)
pred = prediction(attr(predict(fit,x_test, probability = TRUE), "probabilities")[,2], test[,colnames(test) == y_name])

Upvotes: 0

A.S.An
A.S.An

Reputation: 1

Did you solve the problem? I had same problem with you, the reverse ROC and AUC were obtained from the testset.

In my case, It can be solved to sort the training dataset.

For example, 

train <- train[order(train$y, decreasing = TRUE),]

Upvotes: 0

Vikrant Karale
Vikrant Karale

Reputation: 147

I have answered similar question at MATLAB - generate confusion matrix from classifier
By using the code given at the link above, If you get inverse ROC curve like you have shown in your figure then replace the following lines (in the code given at the link):
1. Replace the line in the code given on the link. 

b_pred = (tot_op>=th_vals(i,1));

by 

b_pred = (tot_op<=th_vals(i,1));

2. Replace the line 

AUC = sum(0.5*(sens(2:end)+sens(1:end-1)).*(cspec(2:end) - cspec(1:end-1)));

by 

AUC = sum(0.5*(sens(2:end)+sens(1:end-1)).*(cspec(1:end-1) - cspec(2:end)));

in the code given on the link.

Upvotes: 1

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