x - train[,c( 2:18, 20:21, 24, 27:31)]
y - train$out
svm.pr - svm(x, y, probability = TRUE, method=C-classification,
kernel=radial, cost=bestc, gamma=bestg, cross=10)
pred - predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)],
decision.values = TRUE, probability = TRUE)
attr(pred,
svm.model - svm(y~.,data=dataset,probability=TRUE)
svm.pred-predict(svm.model, test.set, decision.values = TRUE,
probability = TRUE)
library(ROCR)
svm.roc - prediction(attributes(svm.pred)$decision.values, test.set)
svm.auc - performance(svm.roc, 'tpr', 'fpr')
plot(svm.auc)
On Thu, Apr 29,
HI, Saeed,
Thanks so much for the help, I run your code and found the following
problem, do you have any comments or suggestions?
svm.p-svm(as.factor(out) ~ ., data=train[,c( 2:18, 20:21, 24, 27:32)],
probability=TRUE, method=C-classification,
+ kernel=radial, cost=bestc, gamma=bestg, cross=10)
HI, Saeed,
It worked this time.
Thanks, I appreciated it very much!
On Thu, Apr 29, 2010 at 5:23 PM, Saeed Abu Nimeh sabun...@gmail.com wrote:
in svm.roc - prediction(attributes(svm.pred)$decision.values, valid)
valid should be the output variable in the validation set. maybe
valid[,1]
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