R-随机森林-在测试数据上应用混淆矩阵的错误

bulbasaur[1:5,]
   Appt_date count no_of_reps PerReCount
1 2016-01-01     2          1   2.000000
2 2016-01-04   174         58   3.000000
3 2016-01-05   206         59   3.491525
4 2016-01-06   203         61   3.327869
5 2016-01-07   236         64   3.687500

我写的代码是:

install.packages("caret")
library(caret)

leaf <- bulbasaur
ctrl = trainControl(method="repeatedcv", number=100, repeats=50, selectionFunction = "oneSE")
in_train = createDataPartition(leaf$PerReCount, p=.75, list=FALSE)

#random forest
trf = train(PerReCount ~ ., data=leaf, method="rf", metric="RMSE",trControl=ctrl, subset = in_train)


#boosting
tgbm = train(PerReCount ~ ., data=leaf, method="gbm", metric="RMSE",
             trControl=ctrl, subset = in_train, verbose=FALSE)

resampls = resamples(list(RF = trf, GBM = tgbm))
difValues = diff(resampls)
summary(difValues)



######Using it on test matrix
test = leaf[-in_train,]
test$pred.leaf.rf = predict(trf, test, "raw")
confusionMatrix(test$pred.leaf.rf, test$PerReCount)

Error in confusionMatrix.default(test$pred.leaf.rf, test$PerReCount) : 
  the data cannot have more levels than the reference

我尝试了一些改变,比如 leaf$PerReCount <- as.factors(leaf$PerReCount) type = "class" ,但准确率太低了,我不想把它从回归改为分类。我如何在不转换为因子的情况下解决它,或者以任何其他方式解决问题,或者在不使用混淆矩阵的情况下获得精度计数。谢谢