Caret 出现traning或者Testing 数据集准确度100%

library(caret)
data(mdrr)
dim(mdrrDescr)
## creat data
df=mdrrDescr %>% dplyr::select(1:10) %>% 
  mutate(y=factor(ifelse(Ms>2.12,"Yes","No"))) %>% as_tibble()
## creat x 
df1=df %>% filter(y=="Yes") %>% mutate(x=rnorm(n(),1.2,0.3))
df2=df %>% filter(y=="No") %>% mutate(x=rnorm(n(),12.2,0.3))

## split data
df=rbind(df1,df2)
samp = createDataPartition(df$Sv, p = 0.80, list = FALSE)
ames_train = df[samp,]
ames_test = df[-samp,]

## model traning
set.seed(12)
myControl= trainControl(method = "cv",
                        number = 10,
                        summaryFunction=prSummary,
                        classProbs=T,
                        savePredictions = T,
                        verboseIter = F) 
## linear regression model
model_lm =train(y~., 
                data=ames_train[,-c(1)] ,
                method = "glm",
                family = "binomial",
                #weights = model_weights,
                trControl = myControl)
## model performance 
confusionMatrix(predict(model_lm,ames_test),ames_test$y)

#importance
varImp(model_lm,scale = F)