Nested CV CI

Confidence Intervals based on ResamplingNestedCV, including bias-correction.

Parameters

Those from MeasureAbstractCi, as well as:

• bias :: logical(1)
  Whether to do bias correction. This is initialized to TRUE. If FALSE, the outer iterations are used for the point estimate and no bias correction is applied.

References

Bates, Stephen, Hastie, Trevor, Tibshirani, Robert (2024). “Cross-validation: what does it estimate and how well does it do it?” Journal of the American Statistical Association, 119(546), 1434–1445.

Super classes

mlr3::Measure -> mlr3inferr::MeasureAbstractCi -> MeasureCiNestedCV

Methods

Public methods

• MeasureCiNestedCV$new()

• MeasureCiNestedCV$clone()

Inherited methods

• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()
• mlr3inferr::MeasureAbstractCi$aggregate()

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Method new()

Creates a new instance of this R6 class.

Usage

MeasureCiNestedCV$new(measure)

Arguments

measure

(Measure or character(1))
A measure of ID of a measure.

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Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureCiNestedCV$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

ci_ncv = msr("ci.ncv", "classif.acc")
ci_ncv
#> <MeasureCiNestedCV:classif.acc>: Nested CV CI
#> * Packages: mlr3, mlr3measures, mlr3inferr
#> * Range: [0, 1]
#> * Minimize: FALSE
#> * Average: custom
#> * Parameters: bias=TRUE, alpha=0.05, within_range=TRUE
#> * Properties: primary_iters
#> * Predict type: response