Corrected-T CI

Corrected-T confidence intervals based on ResamplingSubsampling. A heuristic factor is applied to correct for the dependence between the iterations. The confidence intervals tend to be liberal.

Parameters

Only those from MeasureAbstractCi.

References

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

Super classes

mlr3::Measure -> mlr3inferr::MeasureAbstractCi -> MeasureCiCorrectedT

Methods

Public methods

• MeasureCiCorrectedT$new()

• MeasureCiCorrectedT$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

MeasureCiCorrectedT$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

MeasureCiCorrectedT$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

m_cort = msr("ci.cor_t", "classif.acc")
m_cort
#> <MeasureCiCorrectedT:classif.acc>: Corrected-T CI
#> * Packages: mlr3, mlr3measures, mlr3inferr
#> * Range: [0, 1]
#> * Minimize: FALSE
#> * Average: custom
#> * Parameters: alpha=0.05, within_range=TRUE
#> * Properties: primary_iters
#> * Predict type: response
rr = resample(
  tsk("sonar"),
  lrn("classif.featureless"),
  rsmp("subsampling", repeats = 10)
)
rr$aggregate(m_cort)
#>       classif.acc classif.acc.lower classif.acc.upper 
#>         0.4898551         0.3408775         0.6388327