Monte Carlo Cross-Validation
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.
mc_cv(data, prop = 3/4, times = 25, strata = NULL, breaks = 4, pool = 0.1, ...)
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 that is used to conduct stratified sampling to create the resamples. This could be a single character value or a variable name that corresponds to a variable that exists in the data frame. |
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. |
... |
Not currently used. |
The strata argument causes the random sampling to be conducted
within the stratification variable. This can help ensure that the number of
data points in the analysis data is equivalent to the proportions in the
original data set. (Strata below 10% of the total are pooled together
by default.)
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.
mc_cv(mtcars, times = 2)
mc_cv(mtcars, prop = .5, times = 2)
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")
})
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")
})
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")
})Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.