mc_cv | R Documentation |
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 in |
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. |
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.
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") } )
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