库中的SVM e1071（预测函数总是返回一个类）在R中

Question

编辑：我意识到我可以使用dput()函数输出训练集的一部分

> dput(lc_train)
structure(list(int_rate = structure(c(0.327601699000463, -1.90117708704328, 
-1.06167801512506, -1.24645906084619, -0.849521999667472, 0.327601699000463, 
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1.06900712936302, 0.439382825424356, -0.73545962576554, 1.22185071039161, 
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1.35644431159589, 0.954944755461087, 1.35416306411785, -1.47458380865006, 
-0.73545962576554, 0.327601699000463, 0.439382825424356, 0.327601699000463, 
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0.886507331119928, 0.21582057257657, -0.0145854227053318, -0.459428680922866, 
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-0.73545962576554, -0.511897372917754, -1.24645906084619), "`scaled:center`" = 14.3639361702128, "`scaled:scale`" = 4.38356648994426), 
    revol_util = structure(c(-1.1664699090807, -0.264229209519369, 
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), row.names = c(NA, -451L), .internal.selfref = <pointer: 0x101822d78>, index = integer(0))

我正在从svm包中运行e1071。

> svmfit = svm(default ~., data = lc_train, kernel = "linear", cost = 10, scale = FALSE)
> summary(svmfit)

Call:
svm(formula = default ~ ., data = lc_train, kernel = "linear", cost = 10, scale = FALSE)


Parameters:
   SVM-Type:  C-classification 
 SVM-Kernel:  linear 
       cost:  10 
      gamma:  0.3333333 

Number of Support Vectors:  1941

 ( 996 945 )


Number of Classes:  2 

Levels: 
 -1 1



> 
> ypred = predict(svmfit, lc_train)
> unique(ypred)
[1] -1
Levels: -1 1

从培训数据的svmfit摘要中可以看出，SVM确实分为两类。看起来在一个类中有996个支持向量，在另一个类中有945个支持向量。

但是，当我在同一训练数据集predict上使用lc_train函数时，它会告诉我所有预测都是一个类（-1）。

我真的很困惑，因为summary(svmfit)清楚地显示了两个类，我在每种情况下使用相同的训练数据集。

我正在使用数据表，特别是lc_train是一个数据表。我不确定这是否会搞砸。

谢谢！

Answer 1

以下是重现您的问题的代码：

library("data.table")
library("e1071")

set.seed(10)

lc_train <- data.table(default = as.factor(sample(c(-1, 1), size = 200, replace = TRUE)), x1=rnorm(100), x2=rnorm(100))
svmfit  <-  svm(default ~., data = lc_train, kernel = "linear", cost = 10, scale = FALSE)
summary(svmfit)
ypred <-  predict(svmfit, lc_train)
table(ypred)

摘要显示您的数据中有两个类，这是真的。但是svm只预测一个班级。可能由于数据而发生，您可能想要更改模型参数，如@Vongo建议的那样（kernel = "radial"）。

（在上面的代码中将随机种子更改为100 set.seed(100)后，您会在预测中看到两个类。）