应该是具有相同水平，误差和参考的因素

Question

I have this code (below) and need to use CARET and split the data set in 40% of all data in the dataset should be in trainset, the rest in testset; the payment variable should be distributed equally across the split but the code of the confusionmatrixline gives an error which says:

"Error: data and reference should be factors with the same levels."

EDIT: the payment variable is a binominal variable so 0 (no) and 1 (yes). gdp are just numbers

Sample dataset: (don't now how to make a table here yet)

payment    gdp 

0          838493

1         9303032

0          72738 

1        38300022

1         283283

How to fix this??

My code:

 `index <- createDataPartition(y = dataset$payment, p = 0.40, list = F)
 trainset <- dataset[index, ]
 testset <- dataset[-index, ]

payment_knn <- train(payment ~ gdp, method = "knn", data = trainset, 
trControl = trainControl(method = 'cv', number = 5))
predicted_outcomes <- predict(payment_knn, testset)
conMX_pay <- confusionMatrix(predicted_outcomes, testset$payment) 
conMX_pay `

Answer 1

This is purely for illustration purposes. Make sure test data is the same as train data.

df<-df %>% 
  mutate(payment=as.factor(payment),gdp=as.numeric(gdp))
metric<-"Accuracy"
control<-trainControl(method="cv",number = 10)
train_set<-createDataPartition(df$payment,p=0.8,list=F)
valid_me<-df[-train_set,]
train_me<-df[train_set,]
#Training
  set.seed(233)       

fit.knn<-train(payment~.,method="knn",data=train_me,metric=metric,trControl=control)
    validated<-predict(fit.knn,valid_me)
    confusionMatrix(validated,valid_me$payment)

This works fine given the data in your question. Warnings because the data set is too small. Purely for illustration. Data Used:

payment      gdp
1       0   838493
2       1  9303032
3       0    72738
4       1 38300022
5       1   283283

Cheers!