Leave-one-out (LOO) cross-validation uses one data point in the original set as the assessment data and all other data points as the analysis set. A LOO resampling set has as many resamples as rows in the original data set.

loo_cv(data, ...)

Arguments

data

A data frame.

...

Not currently used.

Value

An tibble with classes loo_cv, 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

loo_cv(mtcars)
#> # Leave-one-out cross-validation #> # A tibble: 32 x 2 #> splits id #> <list> <chr> #> 1 <split [31/1]> Resample1 #> 2 <split [31/1]> Resample2 #> 3 <split [31/1]> Resample3 #> 4 <split [31/1]> Resample4 #> 5 <split [31/1]> Resample5 #> 6 <split [31/1]> Resample6 #> 7 <split [31/1]> Resample7 #> 8 <split [31/1]> Resample8 #> 9 <split [31/1]> Resample9 #> 10 <split [31/1]> Resample10 #> # … with 22 more rows