Question

我将重点讨论以下示例：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")

使用公式界面，此代码可以完美运行。但是，如果我将其翻转为使用矩阵形式，则在预测并返回错误（NA）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")

仅尝试弄清楚为什么它不使用非公式接口对预测的数据集计算概率。引发此警告：

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

使用非公式（矩阵）接口的插入符号中的SVM类概率

0 个答案: