R : SVM ROC/AUC and Splitting Data Into Training and Testing Data
#Function begins # split the data set in test and training set split.data <- function(data, p = 0.7, s = 666){ set.seed(s) index <- sample(1:dim(data)[1]) train <- data[index[1:floor(dim(data)[1] * p)], ] test <- data[index[((ceiling(dim(data)[1] * p)) + 1):dim(data)[1]], ] return(list(train = train, test = test)) } #Function ends
The function takes a matrix and splits it into two parts , train & test.
#dati dati = split.data(magic04, p = 0.7) train<-dati$train test<-dati$test
#dati dati = split.data(magic04, p = 0.7) train<-dati$train test<-dati$test #str(train) #str(test) #SVM TRAINING library(e1071) model <- svm(train[,1:10],train[,11], probability = T) # prediction on the test set pred <- predict(model, test[,1:(dim(test)[[2]]-1)], probability = T) # Check the predictions table(pred,test[,dim(test)[2]]) pred.prob <- attr(pred, "probabilities") pred.to.roc <- pred.prob[, 1] # performance assessment library(ROCR) pred.rocr <- prediction(pred.to.roc, as.factor(test[,(dim(test)[[2]])])) perf.rocr <- performance(pred.rocr, measure = "auc", x.measure = "cutoff") cat("AUC =",deparse(as.numeric([email protected])),"\n") perf.tpr.rocr <- performance(pred.rocr, "tpr","fpr") plot(perf.tpr.rocr, colorize=T)
AUC = 0.914772127332079
