When building a model on a data set and re-predicting the same data, the performance estimate from those predictions is often called the "apparent" performance of the model. This estimate can be wildly optimistic. "Apparent sampling" here means that the analysis and assessment samples are the same. These resamples are sometimes used in the analysis of bootstrap samples and should otherwise be avoided like old sushi.

## Usage

apparent(data, ...)

## Arguments

data

A data frame.

...

Not currently used.

## Value

A tibble with a single row and classes apparent, rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and one column called id that has a character string with the resample identifier.

## Examples

apparent(mtcars)
#> # Apparent sampling
#> # A tibble: 1 × 2
#>   splits          id
#>   <list>          <chr>
#> 1 <split [32/32]> Apparent