使用非公式(矩阵)接口的插入符号SVM类概率

我以这个例子为中心: support vector machine train caret error kernlab class probability calculations failed; returning NAs

采样代码

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, 
classProbs =  TRUE))

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

使用公式接口,这段代码运行得非常好。然而,如果我把它翻过来使用矩阵形式,则在预测和返回错误(NAs)时不会计算类概率。见下文。

set.seed(1056)
svmFit <- train(x = trainset["age"], y = trainset$class,
                method = "svmRadial",
                preProc = c("center", "scale"),
                tuneGrid = svmTuneGrid,
                trControl = trainControl(method = "repeatedcv", repeats = 5, classProbs = TRUE))
predictedProbs <- predict(svmFit, newdata = testset , type = "prob")

只是想弄明白为什么它不会使用非公式接口计算预测数据集的概率。抛出此警告:

Warning message:
In method$prob(modelFit = modelFit, newdata = newdata, submodels = param) :
  kernlab class probability calculations failed; returning NAs