mlr_resamplings_paired_subsampling {mlr3inferr}R Documentation

Paired Subsampling

Description

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

Super class

mlr3::Resampling -> ResamplingPairedSubsampling

Active bindings

iters

(integer(1))
The total number of resampling iterations.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
ResamplingPairedSubsampling$new()

Method unflatten()

Unflatten the resampling iteration into a more informative representation:

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.

References

Nadeau, Claude, Bengio, Yoshua (1999). “Inference for the generalization error.” Advances in neural information processing systems, 12.

Examples

pw_subs = rsmp("paired_subsampling")
pw_subs

[Package mlr3inferr version 0.1.0 Index]