在Caret中使用glmnet回归与度量作为ROC时出错

我正在使用Caret的trainControl训练一个glmnet正则化逻辑回归模型，并使用metric =“ROC”训练函数如下，并得到以下错误：

> ctrl_s10_2class <- trainControl(method = "repeatedcv", number = 10, repeats = 10 , savePredictions = TRUE, classProbs = TRUE)
> model_train_glmnet_s10_2class <- train(Class ~ ZCR + Energy + SpectralC + SpectralS + SpectralE + SpectralF + SpectralR + MFCC1 + MFCC2 + MFCC3 + MFCC4 + MFCC5 + MFCC6 + MFCC7 + MFCC8 + MFCC9 + MFCC10 + MFCC11 + MFCC12 + MFCC13, data = training_s10_2class, method="glmnet", trControl = ctrl_s10_2class, metric = "ROC")

Error in evalSummaryFunction(y, wts = weights, ctrl = trControl, lev =  classLevels,  : 

train()'s use of ROC codes requires class probabilities. See the classProbs option of trainControl()

In addition: Warning messages:
1: In train.default(x, y, weights = w, ...) :
You are trying to do regression and your outcome only has two possible values Are you trying to do classification? If so, use a 2 level factor as your outcome column.
2: In train.default(x, y, weights = w, ...) :
cannnot compute class probabilities for regression

但是我已经在trainControl函数中打开了classProbs = TRUE . 此外，为了解决警告消息，我想我必须重新整理我的2类数据，我发现这个错误：

> sensor6data_s10_2class <- within(sensor6data_s10_2class, Class <- as.factor(Class))
> sensor6data_s10_2class$Class2 <- relevel(sensor6data_s10_2class$Class,ref="1")
> model_train_glmnet_s10_2class <- train(Class2 ~ ZCR + Energy + SpectralC + SpectralS + SpectralE + SpectralF + SpectralR + MFCC1 + MFCC2 + MFCC3 + MFCC4 + MFCC5 + MFCC6 + MFCC7 + MFCC8 + MFCC9 + MFCC10 + MFCC11 + MFCC12 + MFCC13, data = training_s10_2class, method="glmnet", trControl = ctrl_s10_2class, metric = "ROC")

Error in train.default(x, y, weights = w, ...) : 
At least one of the class levels is not a valid R variable name; This will cause errors when class probabilities are generated because the variables names will be converted to  X1, X0 . Please use factor levels that can be used as valid R variable names  (see ?make.names for help).

非常感谢任何帮助解决这个问题，无论有没有重新定位！谢谢 .