Question

我注意到svmRadial中caret的拟合函数实际上使用了lssvm，但我认为正确的（预期的）方法应该是ksvm。我错过了什么吗？

library(caret)
Loading required package: lattice
Loading required package: ggplot2
getModelInfo('svmRadial')[[1]]$fit
function(x, y, wts, param, lev, last, classProbs, ...) { 
                    lssvm(x = as.matrix(x), y = y,
                          kernel = rbfdot,
                          kpar = list(sigma = param$sigma), ...)         
                  }
getModelInfo('lssvmRadial')[[1]]$fit
function(x, y, wts, param, lev, last, classProbs, ...) { 
                    lssvm(x = as.matrix(x), y = y,
                          kernel = rbfdot,
                          kpar = list(sigma = param$sigma), ...)         
                  }
sessionInfo()
R version 3.2.0 (2015-04-16)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.3 (Yosemite)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] caret_6.0-47    ggplot2_1.0.1   lattice_0.20-31

Answer 1

使用iris数据集（R 3.2.0，kernlab_0.9-20，caret_6.0-47）：

library(caret)

data(iris)
TrainData <- iris[,1:4]
TrainClasses <- iris[,5]

out1 <- train(TrainData, TrainClasses, method = "svmRadial")
# Loading required package: kernlab
class(out1$finalModel)
# [1] "ksvm"
# attr(,"package")
# [1] "kernlab"

out2 <- train(TrainData, TrainClasses, method = "lssvmRadial")
class(out2$finalModel)
# [1] "lssvm"
# attr(,"package")
# [1] "kernlab"

根据输出，似乎方法"svmRadial"正在使用ksvm包中的kernlab。

R caret svmRadial调用lssvm而不是ksvm

1 个答案: