mlr3 (version 0.1.4)

ResamplingHoldout: Holdout Resampling

Description

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).

Format

R6::R6Class inheriting from Resampling.

Construction

ResamplingHoldout$new()
mlr_resamplings$get("holdout")
rsmp("holdout")

Fields

See Resampling.

Methods

See Resampling.

Parameters

• ratio :: numeric(1) Ratio of observations to put into the training set.

See Also

Dictionary of Resamplings: mlr_resamplings

as.data.table(mlr_resamplings) for a complete table of all (also dynamically created) Resampling implementations.

Examples

# NOT RUN {
# Create a task with 10 observations
task = tsk("iris")
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 list
# }