mlr_measures_ci_cor_t {mlr3inferr} | R Documentation |
Corrected-T CI
Description
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
.
Super classes
mlr3::Measure
-> mlr3inferr::MeasureAbstractCi
-> MeasureCiCorrectedT
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
MeasureCiCorrectedT$new(measure)
Arguments
measure
(
Measure
orcharacter(1)
)
A measure of ID of a measure.
Method clone()
The objects of this class are cloneable with this method.
Usage
MeasureCiCorrectedT$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
References
Nadeau, Claude, Bengio, Yoshua (1999). “Inference for the generalization error.” Advances in neural information processing systems, 12.
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)
[Package mlr3inferr version 0.1.0 Index]