mlr_resamplings_paired_subsampling function

Paired Subsampling

Paired Subsampling to enable inference on the generalization error. One should not directlu call $aggregate() with a non-CI measure on a resample result using paired subsampling, as most of the resampling iterations are only intended

Details

The first repeats_in iterations are a standard ResamplingSubsampling

and should be used to obtain a point estimate of the generalization error. The remaining iterations should be used to estimate the standard error. Here, the data is divided repeats_out times into two equally sized disjunct subsets, to each of which subsampling which, a subsampling with repeats_in repetitions is applied. See the $unflatten(iter) method to map the iterations to this nested structure.

Parameters

• repeats_in :: integer(1)

  The inner repetitions.

• repeats_out :: integer(1)

  The outer repetitions.

• ratio :: numeric(1)

  The proportion of data to use for training.

Examples

pw_subs = rsmp("paired_subsampling")
pw_subs

References

Nadeau, Claude, Bengio, Yoshua (1999). Inference for the generalization error.

Advances in neural information processing systems, 12 .

Super class

mlr3::Resampling -> ResamplingPairedSubsampling

Active bindings

• iters: (integer(1))

   The total number of resampling iterations.
  

Methods

Public methods

• list("ResamplingPairedSubsampling$new()")

• list("ResamplingPairedSubsampling$unflatten()")

• list("ResamplingPairedSubsampling$clone()")

Method new()

Creates a new instance of this R6 class.

Usage

ResamplingPairedSubsampling$new()

Method unflatten()

Unflatten the resampling iteration into a more informative representation:

• inner: The subsampling iteration
• outer: NA for the first repeats_in iterations. Otherwise it indicates the outer iteration of the paired subsamplings.
• partition: NA for the first repeats_in iterations. Otherwise it indicates whether the subsampling is applied to the first or second partition Of the two disjoint halfs.

Usage

ResamplingPairedSubsampling$unflatten(iter)

Arguments

• iter: (integer(1))

   Resampling iteration.
  

Returns

list(outer, partition, inner)

Method clone()

The objects of this class are cloneable with this method.

Usage

ResamplingPairedSubsampling$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.