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)
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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！
答案 0 :(得分：0)
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。）