mlr_measures_ci_wald_cv function

Cross-Validation CI

Confidence intervals for cross-validation. The method is asymptotically exact for the so called Test Error as defined by Bayle et al. (2020). For the (expected) risk, the confidence intervals tend to be too liberal. This inference method can only be applied to decomposable losses.

Parameters

Those from MeasureAbstractCi, as well as:

• variance :: "all-pairs" or "within-fold"

  How to estimate the variance. The results tend to be very similar.

Examples

m_waldcv = msr("ci.wald_cv", "classif.ce")
m_waldcv
rr = resample(tsk("sonar"), lrn("classif.featureless"), rsmp("cv"))
rr$aggregate(m_waldcv)

References

Bayle, Pierre, Bayle, Alexandre, Janson, Lucas, Mackey, Lester (2020). Cross-validation confidence intervals for test error.

Advances in Neural Information Processing Systems, 33 , 16339--16350.

Super classes

mlr3::Measure -> mlr3inferr::MeasureAbstractCi -> MeasureCiWaldCV

Methods

Public methods

• list("MeasureCiWaldCV$new()")

• list("MeasureCiWaldCV$clone()")

Method new()

Creates a new instance of this R6 class.

Usage

MeasureCiWaldCV$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

MeasureCiWaldCV$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.