Forecast 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).

Dictionary

This Resampling can be instantiated via the dictionary mlr_resamplings or with the associated sugar function rsmp():

mlr_resamplings$get("forecast_cv")
rsmp("forecast_cv")

Parameters

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

See also

• as_benchmark_result() to convert to a BenchmarkResult.

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/performance.html#sec-resampling

• Package mlr3viz for some generic visualizations.

Other resample: mlr_resamplings_forecast_cv

Super class

mlr3::Resampling -> ResamplingForecastHoldout

Public fields

iters

(integer(1))
Returns the number of resampling iterations, depending on the values stored in the param_set.

Methods

Public methods

• ResamplingForecastHoldout$new()

• ResamplingForecastHoldout$clone()

Inherited methods

• mlr3::Resampling$format()
• mlr3::Resampling$help()
• mlr3::Resampling$instantiate()
• mlr3::Resampling$print()
• mlr3::Resampling$test_set()
• mlr3::Resampling$train_set()

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Method new()

Creates a new instance of this R6 class.

Usage

ResamplingForecastHoldout$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingForecastHoldout$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Create a task with 10 observations
task = mlr3::tsk("airpassengers")
task$filter(1:10)

# Instantiate Resampling
rfho = mlr3::rsmp("forecast_holdout", ratio = 0.5)
rfho$instantiate(task)

# Individual sets:
rfho$train_set(1)
#> [1] 1 2 3 4 5
rfho$test_set(1)
#> [1]  6  7  8  9 10
intersect(rfho$train_set(1), rfho$test_set(1))
#> integer(0)

# Internal storage:
rfho$instance # simple list
#> $train
#> [1] 1 2 3 4 5
#> 
#> $test
#> [1]  6  7  8  9 10
#>