How to implement a hold-out validation in R

问题:

Let's say I'm using the Sonar data and I'd like to make a hold-out validation in R. I partitioned the data using the createFolds from caret package as folds <- createFolds(mydata$Class, k=5).

I would like then to use exactly the fold mydata[i] as test data and train a classifier using mydata[-i] as train data.

My first thought was to use the train function, but I couldn't find any support for hold-out validation. Am I missing something here?

Also, I'd like to be able to use exactly the pre-defined folds as parameter, instead of letting the function partition the data. Does anyone have any thoughts?

Thanks in advance

回答1:

I think that maybe you want to use 1/5th of the data as a test set and train using the other 4/5ths?

If that is the case, you should used createDataPartition first and let train do the rest. For example:

> library(caret)
> library(mlbench)
> data(Sonar)
> 
> set.seed(1)
> in_train <- createDataPartition(Sonar$Class, p = 4/5, list = FALSE)
> 
> training <- Sonar[ in_train,]
> testing  <- Sonar[-in_train,]
> 
> nrow(Sonar)
[1] 208
> nrow(training)
[1] 167
> nrow(testing)
[1] 41
> 
> lda_fit <- train(Class ~ ., data = training, method = "lda")
> lda_fit
Linear Discriminant Analysis 

167 samples
 60 predictors
  2 classes: 'M', 'R' 

No pre-processing
Resampling: Bootstrapped (25 reps) 

Summary of sample sizes: 167, 167, 167, 167, 167, 167, ... 

Resampling results

  Accuracy  Kappa  Accuracy SD  Kappa SD
  0.71      0.416  0.0532       0.108  

Max