Monte Carlo Cross-Validation — mc

One resample of Monte Carlo cross-validation takes a random sample (without replacement) of the original data set to be used for analysis. All other data points are added to the assessment set.

Usage

mc_cv(data, prop = 3/4, times = 25, strata = NULL, breaks = 4, pool = 0.1, ...)

Arguments

data: A data frame.
prop: The proportion of data to be retained for modeling/analysis.
times: The number of times to repeat the sampling.
strata: A variable in data (single character or name) used to conduct stratified sampling. When not NULL, each resample is created within the stratification variable. Numeric strata are binned into quartiles.
breaks: A single number giving the number of bins desired to stratify a numeric stratification variable.
pool: A proportion of data used to determine if a particular group is too small and should be pooled into another group. We do not recommend decreasing this argument below its default of 0.1 because of the dangers of stratifying groups that are too small.
...: These dots are for future extensions and must be empty.

Value

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

Details

With a strata argument, the random sampling is conducted within the stratification variable. This can help ensure that the resamples have equivalent proportions as the original data set. For a categorical variable, sampling is conducted separately within each class. For a numeric stratification variable, strata is binned into quartiles, which are then used to stratify. Strata below 10% of the total are pooled together; see make_strata() for more details.

Examples

mc_cv(mtcars, times = 2)
#> # Monte Carlo cross-validation (0.75/0.25) with 2 resamples  
#> # A tibble: 2 × 2
#>   splits         id       
#>   <list>         <chr>    
#> 1 <split [24/8]> Resample1
#> 2 <split [24/8]> Resample2
mc_cv(mtcars, prop = .5, times = 2)
#> # Monte Carlo cross-validation (0.5/0.5) with 2 resamples  
#> # A tibble: 2 × 2
#>   splits          id       
#>   <list>          <chr>    
#> 1 <split [16/16]> Resample1
#> 2 <split [16/16]> Resample2

library(purrr)
data(wa_churn, package = "modeldata")

set.seed(13)
resample1 <- mc_cv(wa_churn, times = 3, prop = .5)
map_dbl(
  resample1$splits,
  function(x) {
    dat <- as.data.frame(x)$churn
    mean(dat == "Yes")
  }
)
#> [1] 0.2709458 0.2621414 0.2632775

set.seed(13)
resample2 <- mc_cv(wa_churn, strata = churn, times = 3, prop = .5)
map_dbl(
  resample2$splits,
  function(x) {
    dat <- as.data.frame(x)$churn
    mean(dat == "Yes")
  }
)
#> [1] 0.2652655 0.2652655 0.2652655

set.seed(13)
resample3 <- mc_cv(wa_churn, strata = tenure, breaks = 6, times = 3, prop = .5)
map_dbl(
  resample3$splits,
  function(x) {
    dat <- as.data.frame(x)$churn
    mean(dat == "Yes")
  }
)
#> [1] 0.2636364 0.2599432 0.2576705