I'm trying to get the error rates for a Naive Bayes classifier, by adding in each variable incrementally. For example I have 25 variables in my dataset. I want to get the error rates of the model as I add in one variable at a time. So you know it would output the error rate of the model with the first 2 columns, the error rate with the first 3 columns, then with the first 4 columns, and so on up to the last column. 

Here is the pseudocode of what I'm trying to achieve

```
START
IMPORT DATASET WITH ALL VARIABLES

num_variables = num_dataset_cols
i= 1

WHILE (i <= num_variables)
{
   CREATE NEW DATASET WITH x COLUMNs

   BUILD THE MODEL 
   GET THE ERROR RATE

   ADD IN NEXT COLUMN

   i = i + 1
}

```

Here is a reproducible question. Obviously you can't build a NB classifier with this data, but that's not my problem. My problem is adding in the columns one by one. So far, the only way I can do it is by overwriting each column. For a NB classifier, the first column is the class node, so there must be at least 2 columns starting off in order for it to run.

```
#REPRODUCIBLE EXAMPLE
col1 <- c("A", "B", "C", "D", "E")
col2 <- c(1,2,3,4,5)
col3 <- c(TRUE, FALSE, FALSE, TRUE, FALSE)
col4 <- c("n","y","y","n","y")
col5 <- c("10", "15", "50", "100", "20")

dataset <- data.frame(col1, col2, col3, col4,col5)

num_variables <- ncol(dataset)

i <- 1

while i <= num_variables 
{
data <- dataset[c(1, i+1)]
str(data)

#BUILD MODEL AND GET VALIDATION ERROR

#INCREMENT i TO GET NEXT COLUMN
i <- i + 1

}

```

You should be able to see from the `str(data)` that each time the column is overwritten. Does anyone know how I could go about adding each column without overwriting the previous one? Someone suggested an array to me, but I'm not too familiar with arrays in R. Would this work?