mlr_measures_ci_cor_t function

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. This inference method can also be applied to non-decomposable losses.

Parameters

Only those from MeasureAbstractCi.

Examples

m_cort = msr("ci.cor_t", "classif.acc")
m_cort
rr = resample(
  tsk("sonar"),
  lrn("classif.featureless"),
  rsmp("subsampling", repeats = 10)
)
rr$aggregate(m_cort)

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

• list("MeasureCiCorrectedT$new()")

• list("MeasureCiCorrectedT$clone()")

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.
  

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.