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Strange error with ROCR
5 messages · Christian Schulz, Tobias Sing, Noah Silverman
Hi,
you need the score value , have a look at ?svm.predict and in the ROCR
example.
traindata <- as.data.frame(matrix(runif(1000),ncol=10))
trainlabels <-
as.factor(sample(c("win","lose"),nrow(data),replace=T,prob=c(0.5,0.5)))
model <- svm(traindata,trainlabels, type="C-classification",
kernel="radial", cost=10,
class.weights=c("win"=3,"lose"=1), scale=FALSE, probability = TRUE)
prediction <- predict(model, traindata, decision.values = TRUE,
probability = TRUE)
probs <- attr(prediction, "probabilities")[,1]
pred <- prediction(probs,trainlabels)
HTH Christian
Hello,
I've come across a strange error...
Here is what happens:
model <- svm(traindata,trainlabels, type="C-classification",
kernel="radial", cost=10, class.weights=c("win"=3,"lose"=1),
scale=FALSE, probability = TRUE)
predictions <- predict(model, traindata)
pred <- prediction(predictions, trainlabels)
This returns an error:
Error in prediction(predictions, trainlabels) :
Format of predictions is invalid.
Yet my predictions is just a matrix of predicted labels. Nothing
fancy. (In fact, my step follow the exact example on the ROCR homepage.)
A search through google for "Format of predictions is invalid" returns
zero results.
Can anyone suggest how I might fix this problem?
Thank You,
[[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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Is the "probability of the true label" the best prediction to feed to the ROCR package, or is it better to use the "decision.value"
Since AFAIK they are related by a monotonous transformation, both approaches should lead to the same ROC curve, shouldn't they? (not tested)
On Tue, Aug 4, 2009 at 8:14 PM, Noah Silverman<noah at smartmediacorp.com> wrote:
Good point. ?I'm not sure how I missed that. This does lead to an additional question: Is the "probability of the true label" the best prediction to feed to the ROCR package, or is it better to use the "decision.value" Anybody have any experience on this one? Thanks! -N On 8/4/09 3:28 AM, Christian Schulz wrote:
Hi,
you need ?the score value , have a look at ?svm.predict and in the
ROCR example.
traindata <- as.data.frame(matrix(runif(1000),ncol=10))
trainlabels <-
as.factor(sample(c("win","lose"),nrow(data),replace=T,prob=c(0.5,0.5)))
model <- svm(traindata,trainlabels, type="C-classification",
kernel="radial", cost=10,
class.weights=c("win"=3,"lose"=1), scale=FALSE, probability = TRUE)
prediction <- predict(model, traindata, decision.values = TRUE,
probability = TRUE)
probs <- ?attr(prediction, "probabilities")[,1]
pred <- prediction(probs,trainlabels)
HTH Christian
Hello,
I've come across a strange error...
Here is what happens:
model <- svm(traindata,trainlabels, type="C-classification",
kernel="radial", cost=10, ?class.weights=c("win"=3,"lose"=1),
scale=FALSE, probability = TRUE)
predictions <- predict(model, traindata)
pred <- prediction(predictions, trainlabels)
This returns an error:
Error in prediction(predictions, trainlabels) :
? ?Format of predictions is invalid.
Yet my predictions is just a matrix of predicted labels. ?Nothing
fancy. ?(In fact, my step follow the exact example on the ROCR
homepage.)
A search through google for "Format of predictions is invalid"
returns zero results.
Can anyone suggest how I might fix this problem?
Thank You,
? ? [[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
? ? ? ?[[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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