I'm testing the kernlab
package in a regression problem. It seems it's a common issue to get 'Error in .local(object, ...) : test vector does not match model !
when passing the ksvm
object to the predict
function. However I just found answers to classification problems or custom kernels that are not applicable to my problem (I'm using a built-in one for regression). I'm running out of ideas here, my sample code is:
data <- matrix(rnorm(200*10),200,10)
tr <- data[1:150,]
ts <- data[151:200,]
mod <- ksvm(x = tr[,-1],
y = tr[,1],
kernel = "rbfdot", type = 'nu-svr',
kpar = "automatic", C = 60, cross = 3)
pred <- predict(mod,
ts
)
You forgot to remove the y
variable in the test set, and so it fails because the number of predictors don't match. This will work:
predict(mod,ts[,-1])
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