R因变量中的调整SVM类型错误

我正在使用

e1071

中的

svm

处理这样的数据集：

sdewey <- svm(x = as.matrix(trainS), 
              y = trainingSmall$DEWEY,
              type="C-classification")

svm_tune <- tune(svm, train.x=as.matrix(trainS), train.y=trainingSmall$DEWEY, type="C-classification",    ranges=list(cost=10^(-1:6), gamma=1^(-1:1)))

这是一个多类问题。 我不确定如何解决这个问题，或者是否有其他方法可以找到成本和伽马参数的最佳值

是指没有前4列（杜威、D1、D2和D3）的数据

---

谢谢

谢谢，效果很好。我意识到我留下了type=“linear”标志，这没有意义，因为没有必要为特定类型查找成本和gamma（我试图看看linear是否比RBF工作得最差/更好）。我编辑了这个问题来解决这个问题。

'data.frame':   1000 obs. of  1542 variables:
 $ women.prisoners                                  : int  1 0 0 0 0 0 0 0 0 0 ...
 $ reformatories.for.women                          : int  1 0 0 0 0 0 0 0 0 0 ...
 $ women                                            : int  1 0 0 0 0 0 0 0 0 0 ...
 $ criminal.justice                                 : int  1 0 0 0 0 0 0 0 0 0 ...
 $ soccer                                           : int  0 1 0 0 0 0 0 0 0 0 ...
 $ coal.mines.and.mining                            : int  0 0 1 0 0 0 0 0 0 0 ...
 $ coal                                             : int  0 0 1 0 0 0 0 0 0 0 ...
 $ engineering.geology                              : int  0 0 1 0 0 0 0 0 0 0 ...
 $ family.violence                                  : int  0 0 0 1 0 0 0 0 0 0 ...

require(e1071)

trainingSmall<-read.csv("trainingSmallExtra.csv")

sdewey <- svm(x      = as.matrix(trainingSmall[,4:nrow(trainingSmall)]), 
              y      = trainingSmall$DEWEY,
              type   = "C-classification",
              kernel = "linear" # same as no kernel
              )

trainingSmall$DEWEY <- as.factor(trainingSmall$DEWEY)

svm_tune <- tune(svm, train.x = as.matrix(trainingSmall[,4:nrow(trainingSmall)]), 
                      train.y = trainingSmall$DEWEY, # the way I'm formatting your  
                      kernel  = "linear",            # code is Google's R style
                      type    = "C-classification",    
                      ranges  = list(
                                      cost  = 10^(-1:6), 
                                      gamma =  1^(-1:1)
                                    )
                 )