出了点问题；所有准确性指标值都缺失：模型验证

Question

我想运行此代码用于模型验证，但我在 train()

中遇到错误

library(caret)
diabet<-read.csv("C:/Users/Downloads/diabetes.csv")
diabet$Outcome<-as.factor(diabet$Outcome)
diabet$BMI<-as.factor(diabet$BMI)
train_control<- trainControl(method="boot", number=100)
model <-train(diabet$Outcome~diabet$BMI, trControl=train_control, method="nb")
print(model)

我遇到了这种类型的错误

Something is wrong; all the Accuracy metric values are missing:
    Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :2     NA's   :2    
Error: Stopping
In addition: There were 50 or more warnings (use warnings() to see the first 50)

谁能帮我解决这个错误？

Answer 1

根据the documentation，有两种调用train()的方法：

## S3 method for class 'default':
train(x, y, 
      method = "rf",  
      ..., 
      weights = NULL,
      metric = ifelse(is.factor(y), "Accuracy", "RMSE"),   
      maximize = ifelse(metric == "RMSE", FALSE, TRUE),
      trControl = trainControl(), 
      tuneGrid = NULL, 
      tuneLength = 3)

## S3 method for class 'formula':
train(form, data, ..., weights, subset, na.action, contrasts = NULL)

根据你的问题 post，你似乎在尝试使用第二个签名，所以你应该如何实现它：

model <-train(Outcome~BMI, data=diabet, trControl=train_control, method="nb")

这样您就有了 form 的有效公式，并且您还在函数调用中传入了所需的 data。

出了点问题；所有准确性指标值都缺失：模型验证

Something is wrong; all the Accuracy metric values are missing: model validation

r

data-science