I have never been able to get class probabilities to work and I am relatively
new to using these tools, and I am looking for some insight as to what may be
wrong.
I am using caret with kernlab/ksvm. I will simplify my problem to a basic data
set which produces the same problem. I have read the caret vignettes as well
as documentation for ?train. I appreciate any direction you can give. I
realize this is a very small dataset, the actual data is much larger, I am just
using 10 rows as an example:
trainset <- data.frame(
outcome=factor(c("0","1","0","1","0","1","1","1","1","0")),
age=c(10, 23, 5, 28, 81, 48, 82, 23, 11, 9),
amount=c(10.11, 22.23, 494.2, 2.0, 29.2, 39.2, 39.2, 39.0, 11.1, 12.2)
)
> str(trainset)
'data.frame': 7 obs. of 3 variables:
$ outcome: Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1
$ age : num 23 5 28 48 82 11 9
$ amount : num 22.2 494.2 2 39.2 39.2 ...
> colSums(is.na(trainset))
outcome age amount
0 0 0
## SAMPLING AND FORMULA
dataset <- trainset
index <- 1:nrow(dataset)
testindex <- sample(index, trunc(length(index)*30/100))
trainset <- dataset[-testindex,]
testset <- dataset[testindex,-1]
## TUNE caret / kernlab
set.seed(1)
MyTrainControl=trainControl(
method = "repeatedcv",
number=10,
repeats=5,
returnResamp = "all",
classProbs = TRUE
)
## MODEL
rbfSVM <- train(outcome~., data = trainset,
method="svmRadial",
preProc = c("scale"),
tuneLength = 10,
trControl=MyTrainControl,
fit = FALSE
)
There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In train.default(x, y, weights = w, ...) :
At least one of the class levels are not valid R variables names; This may
cause errors if class probabilities are generated because the variables names
will be converted to: X0, X1
2: In caret:::predictionFunction(method = method, modelFit = mod$fit, ... :
kernlab class prediction calculations failed; returning NAs
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