Returns the out-of-bag error of the Learner for learners that support it
(learners with property "oob_error"
).
Returns NA
for unsupported learners.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr()
:
mlr_measures$get("oob_error") msr("oob_error")
Type: NA
Range: \((-\infty, \infty)\)
Minimize: NA
Required prediction: 'response'
mlr3::Measure
-> MeasureOOBError
new()
Creates a new instance of this R6 class.
MeasureOOBError$new()
clone()
The objects of this class are cloneable with this method.
MeasureOOBError$clone(deep = FALSE)
deep
Whether to make a deep clone.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/train-predict.html
Package mlr3measures for the scoring functions.
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures)
for a table of available Measures in the running session (depending on the loaded packages).
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
MeasureClassif
,
MeasureRegr
,
Measure
,
mlr_measures_aic
,
mlr_measures_bic
,
mlr_measures_classif.costs
,
mlr_measures_debug
,
mlr_measures_elapsed_time
,
mlr_measures_selected_features
,
mlr_measures