You should give us the data is what you should do :)
Aside from that: you can only make probability predictions if you activated it when making the model.
On 07.08.2012, at 17:23, Camomille wrote:
> Hi, I have some difficulties in interpreting the prediction of a svm model
> using the package e1071.
>
> y1 is the variable I want to predict. It is of type factor and has got two
> levels: "< 50%" and "> 50%".
> z is the dataset.
>
>> model <- svm(y1 ~ ., data = z,type="C-classification", cross=10)
>> model
>
> Call:
> svm(formula = y1 ~ ., data = z, type = "C-classification", cross = 10)
>
>
> Parameters:
> SVM-Type: C-classification
> SVM-Kernel: radial
> cost: 1
> gamma: 0.07142857
>
> Number of Support Vectors: 68
>
>> pred <- predict(model,newdata=z,probability=TRUE,decision.values = TRUE)
>> table(pred)
> pred
> < 50% > 50%
> 414 0
>
> The results of "pred" is not what I intended to get as, I expected this
> type of result:
>
> < 50% > 50%
>
> < 50% 89 25
>> 50% 38 262
>
> What should I do?
>
>
>
>
> --
> View this message in context:
http://r.789695.n4.nabble.com/Interpreting-predictions-of-svm-tp4639405.html> Sent from the R help mailing list archive at Nabble.com.
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>
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http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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