具有插入错误的SVM分类(基本)

Question

我可能在这里犯了一个非常简单(和愚蠢)的错误,但我无法弄明白.我正在玩Kaggle(数字识别器)的一些数据,并尝试使用带有Caret包的SVM进行分类.如果我只是将标签值作为数字类型train插入函数中,Caret中的函数似乎默认为回归并且性能很差.所以我接下来尝试将其转换为函数的因子factor()并尝试运行SVM分类.下面是一些代码,我生成一些虚拟数据,然后将其插入Caret:

library(caret)
library(doMC)
registerDoMC(cores = 4)

ytrain <- factor(sample(0:9, 1000, replace=TRUE))
xtrain <- matrix(runif(252 * 1000,0 , 255), 1000, 252)

preProcValues <- preProcess(xtrain, method = c("center", "scale"))
transformerdxtrain <- predict(preProcValues, xtrain)

fitControl <- trainControl(method = "repeatedcv", number = 10, repeats = 10)
svmFit <- train(transformerdxtrain[1:10,], ytrain[1:10], method = "svmradial")

我收到此错误:

Error in kernelMult(kernelf(object), newdata, xmatrix(object)[[p]], coef(object)[[p]]) : 
  dims [product 20] do not match the length of object [0]
In addition: Warning messages:
1: In train.default(transformerdxtrain[1:10, ], ytrain[1:10], method = "svmradial") :
  At least one of the class levels are not valid R variables names; This may cause errors if class probabilities are generated because the variables names will be converted to: X0, X1, X2, X3, X4, X5, X6, X7, X8, X9
2: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method,  :
  There were missing values in resampled performance measures.

有人能告诉我我做错了什么吗？谢谢!

Answer 1

您有 10 个不同的类，但仅在 中包含 10 个案例train()。这意味着当您重新采样时，您的分类器的各个实例中通常不会包含所有 10 个类。train()很难组合这些不同类别 SVM 的结果。

您可以通过增加案例数量、减少类数量、甚至使用不同的分类器的某种组合来解决此问题。