support vector machine train caret error kernlab class probability calculations failed; returning NAs

Question

i have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to finalize values of various tuning parameters and got the final Support vector machine . For the test data i can predict the 'class'. But when i try to predict probabilities for test data, i get below error (for example my model tells me that 1st data point in test data has y='good', but i want to know what is the probability of getting 'good' ...generally in case of support vector machine, model will calculate probability of prediction..if Y variable has 2 outcomes then model will predict probability of each outcome. The outcome which has the maximum probability is considered as the final solution)

**Warning message:  
In probFunction(method, modelFit, ppUnk) :  
  kernlab class probability calculations failed; returning NAs**

sample code as below

library(caret)
trainset <- data.frame( 
     class=factor(c("Good",    "Bad",   "Good", "Good", "Bad",  "Good", "Good", "Good", "Good", "Bad",  "Bad",  "Bad")),
     age=c(67,  22, 49, 45, 53, 35, 53, 35, 61, 28, 25, 24))

testset <- data.frame( 
     class=factor(c("Good",    "Bad",   "Good"  )),
    age=c(64,   23, 50))



library(kernlab)
set.seed(231)

### finding optimal value of a tuning parameter
sigDist <- sigest(class ~ ., data = trainset, frac = 1)
### creating a grid of two tuning parameters, .sigma comes from the earlier line. we are trying to find best value of .C
svmTuneGrid <- data.frame(.sigma = sigDist[1], .C = 2^(-2:7))

set.seed(1056)
svmFit <- train(class ~ .,
                data = trainset,
                method = "svmRadial",
                preProc = c("center", "scale"),
                tuneGrid = svmTuneGrid,
                trControl = trainControl(method = "repeatedcv", repeats = 5))

### svmFit finds the optimal values of tuning parameters and builds the model using the best parameters

### to predict class of test data
predictedClasses <- predict(svmFit, testset )
str(predictedClasses)


### predict probablities but i get an error
predictedProbs <- predict(svmFit, newdata = testset , type = "prob")
head(predictedProbs)

new question below this line: as per below output there are 9 support vectors. how to recognize out of 12 training data points which are those 9?

svmFit$finalModel

Support Vector Machine object of class "ksvm"

SV type: C-svc (classification) parameter : cost C = 1

Gaussian Radial Basis kernel function. Hyperparameter : sigma = 0.72640759446315

Number of Support Vectors : 9

Objective Function Value : -5.6994 Training error : 0.083333

Answer 1

In the train control statement, you have to specify if you want the class probabilities classProbs = TRUE returned.

svmFit <- train(class ~ .,
    data = trainset,
    method = "svmRadial",
    preProc = c("center", "scale"),
    tuneGrid = svmTuneGrid,
    trControl = trainControl(method = "repeatedcv", repeats = 5, 
classProbs =  TRUE))

predictedClasses <- predict(svmFit, testset )
predictedProbs <- predict(svmFit, newdata = testset , type = "prob")

giving the probabilities of being in the Bad or Good class in the test dataset as:

print(predictedProbs)
    Bad      Good
1 0.2302979 0.7697021
2 0.7135050 0.2864950
3 0.2230889 0.7769111

EDIT

To answer your new question, you can access the position of the support vectors in your original data set with alphaindex(svmFit$finalModel) with coefficients coef(svmFit$finalModel).