CODEWITHSUNDEEP
rdata-science
Brijesh Sundi
Brijesh Sundi

Reputation: 27

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

I want to run this code for model Validation and I am getting the error in the train()

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)

I am getting this type error

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)

can any one help me out how can I fix the error?

Upvotes: 1

Views: 2376

Answers (1)

onlyphantom
onlyphantom

Reputation: 9603

As per the documentation, there are two ways to call the train():

## 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)

From your question post, it looks like you're attempting to use the second signature, so this is how you should be implementing it:

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

This way you have a valid formula for form, and you also pass in the required data in the function call.

Upvotes: 1

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