Holdout Resampling
Splits data into a training set and a test set.
Parameter ratio determines the ratio of observation going into the training set (default: 2/3).
This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():
mlr_resamplings$get("holdout")
rsmp("holdout")ratio (numeric(1))
Ratio of observations to put into the training set.
mlr3::Resampling -> ResamplingHoldout
iters(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.
new()
Creates a new instance of this R6 class.
ResamplingHoldout$new()
clone()
The objects of this class are cloneable with this method.
ResamplingHoldout$clone(deep = FALSE)
deepWhether to make a deep clone.
Bischl B, Mersmann O, Trautmann H, Weihs C (2012). “Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation.” Evolutionary Computation, 20(2), 249–275. doi: 10.1162/evco_a_00069.
as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.
# Create a task with 10 observations
task = tsk("penguins")
task$filter(1:10)
# Instantiate Resampling
rho = rsmp("holdout", ratio = 0.5)
rho$instantiate(task)
# Individual sets:
rho$train_set(1)
rho$test_set(1)
intersect(rho$train_set(1), rho$test_set(1))
# Internal storage:
rho$instance # simple listPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.