Interpreting predictions of svm

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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.

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?

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