我是R的新手，尝试从文本中检索数据，然后将其应用到SVM中进行分类。这是代码：
train<-read.table("training.txt")
train[which(train=="?",arr.ind=TRUE)]<-NA
train=unique(train)
y=train[,length(train)]
classifier<-svm(y~.,data=train[,-length(train)],scale=F)
classifier<-svm(x=train[,-length(train)],y=factor(y),scale=F)
我尝试了两种不同的方法来调用svm，因为第一种方法(svm(y~.,data=train[,-length(train)],scale=F))似乎还可以，但是第二种方法有问题，它报告：
Error in svm.default(x = train[, length(train)], y = factor(y), scale = F) :
NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning message:
In svm.default(x = train[, length(train)], y = factor(y), scale = F) :
NAs introduced by coercion
这是的示例training.txt，最后一列是目标
39,State-gov,77516,Bachelors,13,Never-married,Adm-clerical,Not-in-family,White,Male,2174,0,40,United-States,0
50,Self-emp-not-inc,83311,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,13,United-States,0
38,Private,215646,HS-grad,9,Divorced,Handlers-cleaners,Not-in-family,White,Male,0,0,40,United-States,0
53,Private,234721,11th,7,Married-civ-spouse,Handlers-cleaners,Husband,Black,Male,0,0,40,United-States,0
28,Private,338409,Bachelors,13,Married-civ-spouse,Prof-specialty,Wife,Black,Female,0,0,40,Cuba,0
37,Private,284582,Masters,14,Married-civ-spouse,Exec-managerial,Wife,White,Female,0,0,40,United-States,0
49,Private,160187,9th,5,Married-spouse-absent,Other-service,Not-in-family,Black,Female,0,0,16,Jamaica,0
52,Self-emp-not-inc,209642,HS-grad,9,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,45,United-States,1
31,Private,45781,Masters,14,Never-married,Prof-specialty,Not-in-family,White,Female,14084,0,50,United-States,1
42,Private,159449,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,5178,0,40,United-States,1
37,Private,280464,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,Black,Male,0,0,80,United-States,1
30,State-gov,141297,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,Asian-Pac-Islander,Male,0,0,40,India,1
23,Private,122272,Bachelors,13,Never-married,Adm-clerical,Own-child,White,Female,0,0,30,United-States,0
32,Private,205019,Assoc-acdm,12,Never-married,Sales,Not-in-family,Black,Male,0,0,50,United-States,0
40,Private,121772,Assoc-voc,11,Married-civ-spouse,Craft-repair,Husband,Asian-Pac-Islander,Male,0,0,40,NA,1
有什么想法吗？提前致谢！
从文档：
对于x参数：
a data matrix, a vector, or a sparse matrix (object of class Matrix
provided by the Matrix package,or of class matrix.csr provided by the
SparseM package, or of class simple_triplet_matrix provided by the slam package).
对于y参数：
a response vector with one label for each row/component of x. Can be
either a factor (for classification tasks) or a numeric vector (for regression).
x=train[,-length(train)]在第二个函数中键入：时，实际上使用的data.frame是不支持的，它会崩溃。
该svm函数仅适用于数字矩阵
library(e1071)
train[which(train=="?",arr.ind=TRUE)]<-NA
train=unique(train)
y=factor(train[,length(train)])
train <- data.frame(lapply(train,as.numeric)) #convert to numeric. factors are integer fields anyway behind the scenes.
train <- as.matrix(train[-length(train)])
classifier<-svm(x= train ,y=y,scale=F)
输出：
> summary(classifier)
Call:
svm.default(x = train, y = y, scale = F)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1
gamma: 0.07142857
Number of Support Vectors: 14
( 9 5 )
Number of Classes: 2
Levels:
0 1
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